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dl_tuto_1_lesson1-pets.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "dl_tuto_1_lesson1-pets.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ia35/9914df0727f711f672130b463698c606/dl_tuto_1_lesson1-pets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "76Ni6FL1QkSr",
"colab_type": "text"
},
"source": [
"# Tuto 1 - quelle est la race de votre animal domestique ?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a0ZjBO46P1QI",
"colab_type": "text"
},
"source": [
"Ce code, présenté lors du [Meetup IA par le Code](https://www.meetup.com/fr-FR/Groupe-Meetup-Rennes-Deep-learning-pour-Informaticiens/), a été corrigé et complété par : \n",
"\n",
"**Jackie Boscher**\n",
"\n",
"Les commentaires en français sont ceux de JB\n",
"\n",
"\n",
"![BackProp.fr](http://bec552ebfe.url-de-test.ws/ml/buttonBackProp.png) \n",
"\n",
"[BackProp.fr](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3pDh8sEXP8Ks",
"colab_type": "text"
},
"source": [
"![backprop.fr](http://bec552ebfe.url-de-test.ws/ml/BackPropLogo.png)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yIbZxZwpSdds",
"colab_type": "text"
},
"source": [
"Cette leçon reprend le cours [fastai](https://course.fast.ai/videos/?lesson=1) de @jeremyphoward : What's your pet\n",
"\n",
"Ce cours a déjà été commenté par nous [ici](http://intelligence-artificielle.agency/notes-sur-la-lecon-1-de-fastai-deep-learning/). Il s'agit d'aller plus loin dans l'exégèse ! sans reprendre ce qui a déjà été [fait](http://intelligence-artificielle.agency/notes-sur-la-lecon-1-de-fastai-deep-learning/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FdS71-csQkSu",
"colab_type": "text"
},
"source": [
"Cette leçon vous apprendra à reconnaitre automatiquement, à partir d'une photo, la race de votre chat ou chien"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fWaSBx0VFy2H",
"colab_type": "text"
},
"source": [
"L’espèce canine regroupe ainsi le plus grand nombre de races. \n",
"\n",
"347 races [officiellement](https://www.toutoupourlechien.com/race-de-chien.html) reconnues à ce jour par la Fédération Cynologique Internationale tandis-que le LOOF reconnaît une soixantaine de races de chats"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AIHM6iR0Rkm5",
"colab_type": "text"
},
"source": [
"Comme vous le savez (voir leçons précédentes, les notebooks commencent toujours avec les lignes suivantes (dans les cours de fastai) :"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "goqW6LPwnm0o",
"colab_type": "text"
},
"source": [
"[%reload_ext](https://ipython.readthedocs.io/en/stable/interactive/magics.html) : Reload an IPython extension by its module name.\n",
"\n",
"[autoreload](https://ipython.readthedocs.io/en/stable/config/extensions/autoreload.html) reloads modules automatically before entering the execution of code typed at the IPython prompt.\n",
"C’est rarement utile. Mais on ne sait jamais. \n",
"\n",
"Quant à Reload %autoreload 2, « it Reloads all modules (except those excluded by %aimport) every time before executing the Python code typed »"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "75DdUf2nX8ol",
"colab_type": "text"
},
"source": [
"%[aimport](https://ipython.readthedocs.io/en/stable/config/extensions/autoreload.html)\n",
"\n",
"List modules which are to be automatically imported or not to be imported.\n",
"\n",
"Par exemple : \n",
"\n",
"%aimport -foo \n",
"\n",
"Mark module ‘foo’ to not be autoreloaded.\n",
"\n",
"alors que : \n",
"\n",
"%aimport foo\n",
"\n",
"Import module ‘foo’ and mark it to be autoreloaded for %autoreload 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4zNQnHuri2ku",
"colab_type": "text"
},
"source": [
"Voir aussi [ici](https://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5r6_qoJEQkSx",
"colab_type": "code",
"colab": {}
},
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qcBYFdWtQkS7",
"colab_type": "text"
},
"source": [
"fastai supporte 4 domaines d'application du Deep Learning : \n",
"\n",
"1. vision\n",
"2. text\n",
"3. tabular\n",
"4. collab (collaborative filtering) - système de recommandation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o7UCE78UYTHe",
"colab_type": "text"
},
"source": [
"Lorsqu'on importe fastai on importe en même temps les modules PyTorch"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RUKmDJ5En8au",
"colab_type": "text"
},
"source": [
"Les librairies fastai sont importées (au dessus de PyTorch)\n",
"\n",
"[fastai.vision](https://docs.fast.ai/vision.html) est la librairie qui s’occupe de la vision (traitement d’images) et [fastai.metrics](https://docs.fast.ai/metrics.html) est une bibliothèque de métriques (par exemple RMSE, recall, …)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jwN3VPkXQkS9",
"colab_type": "code",
"colab": {}
},
"source": [
"from fastai.vision import *\n",
"from fastai.metrics import error_rate"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "PMTXY29JQkTE",
"colab_type": "text"
},
"source": [
"Cette remarque ne s'applique pas dans le cas de Google Colab.\n",
"\n",
"*If you're using a computer with an unusually small GPU, you may get an out of memory error when running this notebook. If this happens, click Kernel->Restart, uncomment the 2nd line below to use a smaller *batch size* (you'll learn all about what this means during the course), and try again. *"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NiMT9w4okTEL",
"colab_type": "text"
},
"source": [
"bs pour batch size\n",
"\n",
"bs est le nombre d'images qu'on entraîne en même temp"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EuuZcaNVQkTF",
"colab_type": "code",
"colab": {}
},
"source": [
"bs = 64\n",
"# bs = 16 # uncomment this line if you run out of memory even after clicking Kernel->Restart"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "l4dyHGocQkTM",
"colab_type": "text"
},
"source": [
"## Données"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UgKQiNeXQkTO",
"colab_type": "text"
},
"source": [
"Le jeu de données est [Oxford-IIIT Pet Dataset](http://www.robots.ox.ac.uk/~vgg/data/pets/) atrribué à [O. M. Parkhi et al., 2012](http://www.robots.ox.ac.uk/~vgg/publications/2012/parkhi12a/parkhi12a.pdf)\n",
"\n",
"Ce jeu comprend des images de chats (12 races) et de chiens (25 races). \n",
"L'exercice consisite à catégoriser correctement une image.\n",
"\n",
"Il y a donc en tout 37 catégories et chaque classe a environ 200 images. Les images sont très variées.\n",
"\n",
"En 2012, le meilleurs score était de 59.21%, avec un logiciel dédié aux chats/chiens.\n",
"\n",
"Ces données sont aussi sur [Kaggle](https://www.kaggle.com/c/oxford-iiit-pet-dataset/overview). \n",
"\n",
"La métrique recommandée est Multi Class Loss\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tcg0xKaqeBUd",
"colab_type": "text"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-hOv5uPmhZT-",
"colab_type": "text"
},
"source": [
"Lorsqu'on importe fastai, on peut uitliser URLs : Global constants for dataset and model URLs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3JZM6Fhmd_5k",
"colab_type": "code",
"colab": {}
},
"source": [
"??URLs"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "q7du67l2hnky",
"colab_type": "text"
},
"source": [
"On utilise la fonction untar_data à qui on passe en paramètre une URL. Cette fonction de fastai télécharge les données et les décompresse"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WS99l4vzQkTQ",
"colab_type": "code",
"outputId": "7dd74ae5-76cb-45f3-a0a2-db942dda9959",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 227
}
},
"source": [
"help(untar_data)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Help on function untar_data in module fastai.datasets:\n",
"\n",
"untar_data(url:str, fname:Union[pathlib.Path, str]=None, dest:Union[pathlib.Path, str]=None, data=True, force_download=False) -> pathlib.Path\n",
" Download `url` to `fname` if `dest` doesn't exist, and un-tgz to folder `dest`.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wbnsBpoFqDiv",
"colab_type": "text"
},
"source": [
"Notez que `untar_data` retourne un objet de type [pathlib](https://docs.python.org/3/library/pathlib.html) "
]
},
{
"cell_type": "code",
"metadata": {
"id": "LUFxfafkqkeT",
"colab_type": "code",
"outputId": "ae6f7c1e-f005-4c81-af61-96728e9118d4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"URLs.PETS"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lekmrN9FQkTg",
"colab_type": "code",
"outputId": "2972cb2e-9b18-4c62-f4b7-06849d068bd1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"path = untar_data(URLs.PETS); path"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PosixPath('/root/.fastai/data/oxford-iiit-pet')"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2f77096-qmjd",
"colab_type": "code",
"outputId": "e5c92d7c-0b0e-490f-c31f-f736180b5136",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"type(path)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"pathlib.PosixPath"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v2XcUecIQkTq",
"colab_type": "code",
"outputId": "d3be27fc-0b52-47f6-ed8e-f999d466b827",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"path.ls()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[PosixPath('/root/.fastai/data/oxford-iiit-pet/images'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vj357e41k_jP",
"colab_type": "code",
"colab": {}
},
"source": [
"path.ls?"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BFjEGbOMqrll",
"colab_type": "code",
"outputId": "4b2e92e8-e1a4-4fbc-c4b0-a9768eae3464",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 131
}
},
"source": [
"!ls -al /root/.fastai/data/oxford-iiit-pet"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"total 300\n",
"drwxrwxr-x 4 1000 1000 4096 Oct 8 2018 .\n",
"drwxr-xr-x 3 root root 4096 Sep 27 09:59 ..\n",
"drwxr-xr-x 4 1000 1000 4096 Jun 30 2012 annotations\n",
"drwxr-xr-x 2 1000 1000 294912 Jun 18 2012 images\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mbug5xXgt1St",
"colab_type": "text"
},
"source": [
"Python 3 permet de faire de la concaténation de path de la manière suivante : "
]
},
{
"cell_type": "code",
"metadata": {
"id": "117L8PeGQkTw",
"colab_type": "code",
"colab": {}
},
"source": [
"path_anno = path/'annotations'\n",
"path_img = path/'images'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tVPZwrvxkXak",
"colab_type": "code",
"outputId": "e579e9a1-7d41-4bbd-f6be-c5898aceda23",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"path_anno.ls()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/trainval.txt'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/xmls'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/._trimaps'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/trimaps'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/test.txt'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/list.txt'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/annotations/README')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9y3-PS6smOGr",
"colab_type": "text"
},
"source": [
"[os.listdir](https://docs.python.org/3/library/os.html) : Return a list containing the names of the entries in the directory given by path. The list is in arbitrary order, and does not include the special entries '.' and '..' even if they are present in the directory."
]
},
{
"cell_type": "code",
"metadata": {
"id": "6sYDYl6gmXkO",
"colab_type": "code",
"outputId": "7f69e314-3dcb-4203-ddb0-8914fdbcf1d3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"path_img"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PosixPath('/root/.fastai/data/oxford-iiit-pet/images')"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VoFm61Fsqu1Z",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"dirs = os.listdir( path_img )"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fuq8JeF8s9Kk",
"colab_type": "text"
},
"source": [
"Pour voir la liste des 10 premiers fichiers\n",
"\n",
"(on peut aussi utiliser la fonction fastai get_image_files)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uQnTE1h_rCeY",
"colab_type": "code",
"outputId": "2a03c303-2e94-4b5d-f1d7-bfa69b91170e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
}
},
"source": [
"dirs[0:10]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['wheaten_terrier_143.jpg',\n",
" 'yorkshire_terrier_99.jpg',\n",
" 'great_pyrenees_113.jpg',\n",
" 'shiba_inu_83.jpg',\n",
" 'staffordshire_bull_terrier_167.jpg',\n",
" 'basset_hound_171.jpg',\n",
" 'english_cocker_spaniel_178.jpg',\n",
" 'Bengal_61.jpg',\n",
" 'miniature_pinscher_191.jpg',\n",
" 'shiba_inu_194.jpg']"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y2vLyJqGQkT1",
"colab_type": "text"
},
"source": [
"Les étiquettes (labels) des images sont déduites des noms des fichiers.\n",
"\n",
"Par exemple, boxer_46.jpg est un boxer.\n",
"\n",
"Les noms des fichiers sont ici sous la forme d'expressions régulières : race_nombre.ext\n",
"\n",
"fastai a un outil pour ça : `ImageDataBunch.from_name_re` retourne les labels extraits des noms de fichiers à l'aide d'une [expression régulière](https://docs.python.org/3.6/library/re.htm)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dPz2Bka3wYEB",
"colab_type": "code",
"colab": {}
},
"source": [
"??get_image_files"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2CvUnuBTQkT2",
"colab_type": "code",
"outputId": "cfca4f5a-e545-482a-bf4c-4328beeb8bf3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
}
},
"source": [
"fnames = get_image_files(path_img)\n",
"fnames[:5]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[PosixPath('/root/.fastai/data/oxford-iiit-pet/images/staffordshire_bull_terrier_114.jpg'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/images/japanese_chin_150.jpg'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/images/Birman_43.jpg'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/images/shiba_inu_109.jpg'),\n",
" PosixPath('/root/.fastai/data/oxford-iiit-pet/images/British_Shorthair_258.jpg')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "E8HpCB8tQkT8",
"colab_type": "code",
"colab": {}
},
"source": [
"np.random.seed(2)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "H5J1rBTbxFZ7",
"colab_type": "code",
"colab": {}
},
"source": [
"pat = r'/([^/]+)_\\d+.jpg$'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5K1LRDHpxDqS",
"colab_type": "text"
},
"source": [
"Une petite explication de l'expression régulière s'impose, elle veut dire une expression qui commence par un slash / suivi de tout sauf un slash, suivi de _ puis d'un nombre et de .jpg qui termine l'expression\n",
"\n",
"en plaçant un r' avant le délimiteur qui ouvre notre chaîne, tous les caractères anti-slash qu'elle contient sont échappés\n",
"\n",
"Les expressions rationnelles utilisent le caractère [backslash](https://docs.python.org/fr/3/library/re.html) ('\\') pour indiquer des formes spéciales ou permettre d'utiliser des caractères spéciaux sans en invoquer le sens. Cela entre en conflit avec l'utilisation en Python du même caractère pour la même raison dans les chaînes littérales ; par exemple, pour rechercher un backslash littéral il faudrait écrire '\\\\\\\\' comme motif, parce que l'expression rationnelle devrait être \\\\ et chaque backslash doit être représenté par \\\\ au sein des chaînes littérales Python.\n",
"\n",
"La solution est d'utiliser la notation des chaînes brutes en Python pour les expressions rationnelles ; Les backslashs ne provoquent aucun traitement spécifique dans les chaînes littérales préfixées par 'r'. Ainsi, r\"\\n\" est une chaîne de deux caractères contenant '\\' et 'n', tandis que \"\\n\" est une chaîne contenant un unique caractère : un saut de ligne. Généralement, les motifs seront exprimés en Python à l'aide de chaînes brutes.\n",
"\n",
"on commence par un /\n",
"\n",
"() crée un groupe, qui commence par un /\n",
"\n",
"et pour la fin : _\\d+.jpg$\n",
"\n",
"le caractère _ suivi de \\d : chiffre, + : un ou plus donc \\d+ : un ou plusieurs chiffres\n",
" .jpg : suivi de .jpg\n",
" $ : qui termine la ligne\n",
"\n",
"Une autre façon d'écrire l'expression, peut-être plus simple : ([a-z_]*)_\\d+ à condition que seuls ces caractères soient autorisés\n",
"\n",
"Pour ceux qui veulent s'amuser avec les expressions régulières, c'est [ici](https://pythex.org)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "R2eZztxRQkUA",
"colab_type": "code",
"colab": {}
},
"source": [
"data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(), size=224, bs=bs\n",
" ).normalize(imagenet_stats)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4xSKy2pQ5jox",
"colab_type": "text"
},
"source": [
"Data [augmentation](https://docs.fast.ai/vision.transform.html#get_transforms) details\n",
"\n",
"If you want to quickly get a set of random transforms that have worked well in a wide range of tasks, you should use the get_transforms function. The most important parameters to adjust are do_flip and flip_vert, depending on the type of images you have.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V-G_0PpG7MVa",
"colab_type": "text"
},
"source": [
"If you're using a [pretrained](https://docs.fast.ai/vision.data.html) model, you'll need to use the normalization that was used to train the model. The imagenet norm and denorm functions are stored as constants inside the library named imagenet_norm and imagenet_denorm. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "-QK4f0zn6qMT",
"colab_type": "code",
"outputId": "84f9fad8-6092-4ca9-da2a-792a5362467e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"imagenet_stats"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TixBilan_XsS",
"colab_type": "code",
"outputId": "f230ac6e-1a70-42ef-ffb1-28b534209d92",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"bs"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"64"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "brMWxWR1yG39",
"colab_type": "text"
},
"source": [
"ImageDataBunch(train_dl:DataLoader, valid_dl:DataLoader, fix_dl:DataLoader=None, test_dl:Optional[DataLoader]=None, device:device=None, dl_tfms:Optional[Collection[Callable]]=None, path:PathOrStr='.', collate_fn:Callable='data_collate', no_check:bool=False) :: DataBunch"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8X2kJ8tw8mKN",
"colab_type": "text"
},
"source": [
"df pour dataloader\n",
"[Data loader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader). Combines a dataset and a sampler, and provides an iterable over the given dataset.\n",
"\n",
"ds pour dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WwNqlUR8vB7J",
"colab_type": "code",
"outputId": "8a7f94ae-8599-441c-a402-b075113f90cc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"data.train_ds"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LabelList (5912 items)\n",
"x: ImageList\n",
"Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
"y: CategoryList\n",
"wheaten_terrier,yorkshire_terrier,great_pyrenees,basset_hound,english_cocker_spaniel\n",
"Path: /root/.fastai/data/oxford-iiit-pet/images"
]
},
"metadata": {
"tags": []
},
"execution_count": 74
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0B_OwK7a7_BF",
"colab_type": "text"
},
"source": [
"ImageDataBunch.from_name_re : Create from list of `fnames` in `path` with re expression `pat`\n",
"\n",
"get_transform : Utility func to easily create a list of flip, rotate, `zoom`, warp, lighting transforms (on fait du cropping, resizing, ...)\n",
"\n",
"Les images sont retaillées pour être carrées en 224\n",
"\n",
"bs est le batch size\n",
"\n",
"Les images sont aussi normalisées pour avoir la moyenne et l'écart-type de imagenet"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YkPkEhnu7obG",
"colab_type": "code",
"colab": {}
},
"source": [
"??ImageDataBunch.from_name_re"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0i-zZsuYBTWD",
"colab_type": "code",
"colab": {}
},
"source": [
"??data.show_batch"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DATclipBQkUC",
"colab_type": "code",
"outputId": "3828606a-f0c7-4789-f347-1a5c15ac5a2c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 441
}
},
"source": [
"data.show_batch(rows=3, figsize=(7,6))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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rykVHjIE8T8gSYVHtcez4NaBj3nvvhyiyhGmeMa9LqkXF9sYa49GIxy/sUXc9L7ztBRzd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D8QgVRDgvyIxDoVAyoLSaytYB0VXp6WcplbFYD4srKwz6fVZNTzIzOUEYNlGBYrLTIk7G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B47vIQAsaa3GuAfz/q9rmJortrkkrRRgnfHxrFyMcSLYF1u+8hBA4C40zhFohtv7O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\n13EmhtEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 504x432 with 9 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SreZXnAdQkUH",
"colab_type": "code",
"outputId": "965f2614-c889-497b-f69b-1afb96975428",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 91
}
},
"source": [
"print(data.classes)\n",
"len(data.classes),data.c"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"['Abyssinian', 'Bengal', 'Birman', 'Bombay', 'British_Shorthair', 'Egyptian_Mau', 'Maine_Coon', 'Persian', 'Ragdoll', 'Russian_Blue', 'Siamese', 'Sphynx', 'american_bulldog', 'american_pit_bull_terrier', 'basset_hound', 'beagle', 'boxer', 'chihuahua', 'english_cocker_spaniel', 'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 'japanese_chin', 'keeshond', 'leonberger', 'miniature_pinscher', 'newfoundland', 'pomeranian', 'pug', 'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu', 'staffordshire_bull_terrier', 'wheaten_terrier', 'yorkshire_terrier']\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(37, 37)"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mjq7QPEgEIyk",
"colab_type": "text"
},
"source": [
"On retrouve bien nos 37 classes : 12 races de chat et 25 de chiens"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vgQ622mRJ4TX",
"colab_type": "text"
},
"source": [
"Pour calculer l'effectif de chaque classe dans les données d'apprentissage : \n",
"\n",
"(méthode proposée [ici](https://forums.fast.ai/t/get-value-counts-from-a-imagedatabunch/38784))"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RFw0KJCpIOoZ",
"colab_type": "code",
"outputId": "ef890e1c-c904-48bb-a8fd-3adbea1efdc2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 663
}
},
"source": [
"import pandas as pd\n",
"vc = pd.value_counts(data.train_ds.y.items, sort=False)\n",
"vc.index = data.classes; vc"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Abyssinian 167\n",
"Bengal 164\n",
"Birman 165\n",
"Bombay 153\n",
"British_Shorthair 157\n",
"Egyptian_Mau 159\n",
"Maine_Coon 162\n",
"Persian 158\n",
"Ragdoll 164\n",
"Russian_Blue 157\n",
"Siamese 158\n",
"Sphynx 154\n",
"american_bulldog 158\n",
"american_pit_bull_terrier 166\n",
"basset_hound 157\n",
"beagle 162\n",
"boxer 152\n",
"chihuahua 153\n",
"english_cocker_spaniel 152\n",
"english_setter 160\n",
"german_shorthaired 165\n",
"great_pyrenees 161\n",
"havanese 157\n",
"japanese_chin 160\n",
"keeshond 159\n",
"leonberger 163\n",
"miniature_pinscher 171\n",
"newfoundland 153\n",
"pomeranian 165\n",
"pug 151\n",
"saint_bernard 165\n",
"samoyed 166\n",
"scottish_terrier 159\n",
"shiba_inu 161\n",
"staffordshire_bull_terrier 157\n",
"wheaten_terrier 159\n",
"yorkshire_terrier 162\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pKxxLxbAKg4w",
"colab_type": "text"
},
"source": [
"et pour les données de validation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QW195WRMKAXH",
"colab_type": "code",
"outputId": "773eb241-456d-4d9b-d762-640d859d1a57",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 663
}
},
"source": [
"vd = pd.value_counts(data.valid_ds.y.items, sort=False)\n",
"vd.index = data.classes; vd"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Abyssinian 33\n",
"Bengal 42\n",
"Birman 40\n",
"Bombay 35\n",
"British_Shorthair 36\n",
"Egyptian_Mau 46\n",
"Maine_Coon 35\n",
"Persian 49\n",
"Ragdoll 35\n",
"Russian_Blue 42\n",
"Siamese 39\n",
"Sphynx 35\n",
"american_bulldog 47\n",
"american_pit_bull_terrier 34\n",
"basset_hound 43\n",
"beagle 34\n",
"boxer 43\n",
"chihuahua 43\n",
"english_cocker_spaniel 40\n",
"english_setter 41\n",
"german_shorthaired 41\n",
"great_pyrenees 38\n",
"havanese 41\n",
"japanese_chin 39\n",
"keeshond 38\n",
"leonberger 48\n",
"miniature_pinscher 37\n",
"newfoundland 43\n",
"pomeranian 42\n",
"pug 47\n",
"saint_bernard 29\n",
"samoyed 41\n",
"scottish_terrier 35\n",
"shiba_inu 48\n",
"staffordshire_bull_terrier 38\n",
"wheaten_terrier 38\n",
"yorkshire_terrier 43\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UTgvODx6QkUL",
"colab_type": "text"
},
"source": [
"## Apprentissage: resnet34"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JhTWCXs5QkUM",
"colab_type": "text"
},
"source": [
"On utilise un [réseau de neurones à convolution](http://cs231n.github.io/convolutional-networks/) basé sur une architecture Resnet\n",
"\n",
"L'apprentissage se fait avec 4 epochs (4 cycles sur la totalité des données)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FQwXtCPrB8Gp",
"colab_type": "text"
},
"source": [
"L'architecture réseau retenue est Resnet34\n",
"\n",
"On lira [ici](https://arxiv.org/pdf/1512.03385.pdf) et [là](https://towardsdatascience.com/understanding-and-visualizing-resnets-442284831be8) pour approfondir Resnet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8Qy72WEoCdG5",
"colab_type": "text"
},
"source": [
"![Texte alternatif…](http://bec552ebfe.url-de-test.ws/ml/resnet34png)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MV5HSl6wSJRG",
"colab_type": "code",
"colab": {}
},
"source": [
"??error_rate"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "H7tdwmdzTlX0",
"colab_type": "text"
},
"source": [
"error_rate = 1 - `accuracy`"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BYClrm9lQkUN",
"colab_type": "code",
"outputId": "de8911b1-1596-4252-e8b3-d87eb5c0c9d4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"learn = cnn_learner(data, models.resnet34, metrics=error_rate)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth\n",
"100%|██████████| 87306240/87306240 [00:00<00:00, 116445670.24it/s]\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1soMdTZaTwpu",
"colab_type": "text"
},
"source": [
"Les poids du modèle ResNet34 sont chargés\n",
"\n",
"[PyTorch](https://pytorch.org/docs/stable/torchvision/models.html) propose plusieurs architectures ResNet (18, 34, 50, 101, 152) en plus d'autres architectures (AlexNet, VGG, SqueezeNet, DenseNet, Inception, GoogLeNet)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XWftScE5QkUP",
"colab_type": "code",
"outputId": "6aaa2476-feb5-4eae-9d1e-e796ca564f7b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"learn.model"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Sequential(\n",
" (0): Sequential(\n",
" (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU(inplace)\n",
" (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (4): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (5): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): BasicBlock(\n",
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (6): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): BasicBlock(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (4): BasicBlock(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (5): BasicBlock(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (7): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (1): Sequential(\n",
" (0): AdaptiveConcatPool2d(\n",
" (ap): AdaptiveAvgPool2d(output_size=1)\n",
" (mp): AdaptiveMaxPool2d(output_size=1)\n",
" )\n",
" (1): Flatten()\n",
" (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (3): Dropout(p=0.25)\n",
" (4): Linear(in_features=1024, out_features=512, bias=True)\n",
" (5): ReLU(inplace)\n",
" (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (7): Dropout(p=0.5)\n",
" (8): Linear(in_features=512, out_features=37, bias=True)\n",
" )\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hGS03rhwDVkq",
"colab_type": "code",
"outputId": "24c10780-e0f2-46cd-8526-bcaf702356e0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"learn.summary()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Sequential\n",
"======================================================================\n",
"Layer (type) Output Shape Param # Trainable \n",
"======================================================================\n",
"Conv2d [64, 112, 112] 9,408 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 112, 112] 128 True \n",
"______________________________________________________________________\n",
"ReLU [64, 112, 112] 0 False \n",
"______________________________________________________________________\n",
"MaxPool2d [64, 56, 56] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [64, 56, 56] 36,864 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 56, 56] 128 True \n",
"______________________________________________________________________\n",
"ReLU [64, 56, 56] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [64, 56, 56] 36,864 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 56, 56] 128 True \n",
"______________________________________________________________________\n",
"Conv2d [64, 56, 56] 36,864 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 56, 56] 128 True \n",
"______________________________________________________________________\n",
"ReLU [64, 56, 56] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [64, 56, 56] 36,864 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 56, 56] 128 True \n",
"______________________________________________________________________\n",
"Conv2d [64, 56, 56] 36,864 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 56, 56] 128 True \n",
"______________________________________________________________________\n",
"ReLU [64, 56, 56] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [64, 56, 56] 36,864 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [64, 56, 56] 128 True \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 73,728 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"ReLU [128, 28, 28] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 8,192 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"ReLU [128, 28, 28] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"ReLU [128, 28, 28] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"ReLU [128, 28, 28] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [128, 28, 28] 147,456 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [128, 28, 28] 256 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 294,912 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"ReLU [256, 14, 14] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 32,768 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"ReLU [256, 14, 14] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"ReLU [256, 14, 14] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"ReLU [256, 14, 14] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"ReLU [256, 14, 14] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"ReLU [256, 14, 14] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [256, 14, 14] 589,824 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [256, 14, 14] 512 True \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 1,179,648 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"ReLU [512, 7, 7] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 2,359,296 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 131,072 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 2,359,296 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"ReLU [512, 7, 7] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 2,359,296 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 2,359,296 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"ReLU [512, 7, 7] 0 False \n",
"______________________________________________________________________\n",
"Conv2d [512, 7, 7] 2,359,296 True \n",
"______________________________________________________________________\n",
"BatchNorm2d [512, 7, 7] 1,024 True \n",
"______________________________________________________________________\n",
"AdaptiveAvgPool2d [512, 1, 1] 0 False \n",
"______________________________________________________________________\n",
"AdaptiveMaxPool2d [512, 1, 1] 0 False \n",
"______________________________________________________________________\n",
"Flatten [1024] 0 False \n",
"______________________________________________________________________\n",
"BatchNorm1d [1024] 2,048 True \n",
"______________________________________________________________________\n",
"Dropout [1024] 0 False \n",
"______________________________________________________________________\n",
"Linear [512] 524,800 True \n",
"______________________________________________________________________\n",
"ReLU [512] 0 False \n",
"______________________________________________________________________\n",
"BatchNorm1d [512] 1,024 True \n",
"______________________________________________________________________\n",
"Dropout [512] 0 False \n",
"______________________________________________________________________\n",
"Linear [37] 18,981 True \n",
"______________________________________________________________________\n",
"\n",
"Total params: 21,831,525\n",
"Total trainable params: 21,831,525\n",
"Total non-trainable params: 0\n",
"Optimized with 'torch.optim.adam.Adam', betas=(0.9, 0.99)\n",
"Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ \n",
"Loss function : FlattenedLoss\n",
"======================================================================\n",