diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ccq/README.txt b/ccq/README.txt new file mode 100644 index 0000000..bf761b0 --- /dev/null +++ b/ccq/README.txt @@ -0,0 +1,6 @@ +# A Chat Called Quest - ccq + +metodi di Natural Language Processing per la comprensione, produzione e traduzione di lingua italiana e lingua italiana dei segni (LIS). +ancora è un work in progress, ma ci sono i primi esperimentini coi modelli transformers sotto forma di un odiabile jupiter-notebook ccq/ccq_explore.ipynb (nella cartella c'è l export del suddetto notebook processato in html) + +rigiratore di sintassi tra lingue comuni e non diff --git a/ccq/ccq_explore.ipynb b/ccq/ccq_explore.ipynb new file mode 100644 index 0000000..30a2a02 --- /dev/null +++ b/ccq/ccq_explore.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "80e0cc42", + "metadata": {}, + "source": [ + "# 0 : Cosa c'è in questo notebook\n", + "Scaricamanto ed utilizzo modelli di Natural Language Processing da huggingface.com per fare un po di esperimenti ed esempi con dati testuali per il progetto __geografia__.\n", + "\n", + "In particolare si esplorano un po di metodi e strumenti per la comprensione e annotazione dei testi, sfruttando modelli già allenati su task che ci posssono interessare come ad esempio :\n", + "- __Mask-Filling__ (con huggingface transformer): predice un token mancante in una frase\n", + "- __Zero-Shot classification__ (con huggingface transformer): classifica una frase rispetto a delle classi variabili (utile per topic analysis, sentiment retrival e menate generiche)\n", + "- __Pos-Tagging__ (con la libreria spacy) : analisi grammaticale e sintattica del testo\n", + "\n", + "#### 0.1 Transformer Models da hugging face\n", + "I modelli pre-allenati sono stati sottoposti ad estensivi training con tantissimi dati, in genere macinati per giorni. i task non-supervisionati tipici utilizzati per l'allenamento sono in genere:\n", + "- next sentence prediction ( prendi un paragrafo, lo splitti sulla base del carattere '.' ed usi la prima frase come dato e la seconda come label da predirre)\n", + "- fill-mask: predirre la parola mancante di una frase ( nel training si prendono frasi complete e si maschera un termine a random per frase)\n", + "\n", + "Accanto a questi task usati per allenare la struttura della rete neurale (aka \"il grosso\" dei nodi profondi), i modelli di huggingface possono essere già \"fine-tunati\" rispetto ad ulteriori downstream task (tipo sentiment analysis o zero shot) e quindi utilizzabili direttamente. per alcune coppie di modelli-task invece è necessario un ulteriore sessione di fine-tuning con un dataset rilevante per il task d'interesse.\n", + "Qua c'è una lista delle tipologie di task piu comuni:\n", + "https://huggingface.co/tasks\n", + " \n", + "Infine un altra differenza sostanziale che ci interessa è se il modello sia:\n", + "- monolingua\n", + "- multilingua\n", + "\n", + "i modelli considerati per questo girovagare sono:\n", + "- il modello __dbmdz/bert-base-italian-cased__ è piu leggero e solo in italiano, ma di task ready-to-go disponibili c'è solo fill-mask e pos-tag\n", + "- il modello __facebook/bart-large-mnli__ è molto pesante, ma è multilingua ed ha implementato lo zero-shot-classification\n", + "- il modello __bert-base-cased__ solo inglese ma con praticamente tutti i downstream task già pronti all'utilizzo\n", + "\n", + "#### 0.2 Spacy Library\n", + "https://spacy.io/\n", + "libreria python piu semplice ed efficiente rispetto ai modelli transformer di hugging face, ma meno versatile per quantità di task possibili. ottima per pulire testi e tokenization (preprocessing), molto buono il pos-tagging\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "756d5d05", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/agropunx/anaconda3/envs/geografia/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "2022-07-06 20:22:23.485088: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2022-07-06 20:22:23.485116: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "from transformers import AutoModel, AutoTokenizer \n", + "from transformers import pipeline\n", + "multilingual_model_name = \"facebook/bart-large-mnli\" # 1.5 GB\n", + "italian_model_name = \"dbmdz/bert-base-italian-cased\" # 422 MB\n", + "alltask_model_name = \"bert-base-cased\" \n", + "\n", + "## Download model and configuration from huggingface.co and cache. \n", + "#muli_tokenizer = AutoTokenizer.from_pretrained(multilingual_model_name)\n", + "#muli_model = AutoModel.from_pretrained(multilingual_model_name)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1f1657a9", + "metadata": {}, + "source": [ + "## 1 : Fill-Mask task example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2ace43c4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at dbmdz/bert-base-italian-cased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n", + "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'score': 0.9144049286842346,\n", + " 'token': 482,\n", + " 'token_str': 'stato',\n", + " 'sequence': 'Umberto Eco è stato un grande scrittore'},\n", + " {'score': 0.025699790567159653,\n", + " 'token': 5801,\n", + " 'token_str': 'diventato',\n", + " 'sequence': 'Umberto Eco è diventato un grande scrittore'},\n", + " {'score': 0.022715440019965172,\n", + " 'token': 409,\n", + " 'token_str': 'anche',\n", + " 'sequence': 'Umberto Eco è anche un grande scrittore'},\n", + " {'score': 0.006274252198636532,\n", + " 'token': 1402,\n", + " 'token_str': 'oggi',\n", + " 'sequence': 'Umberto Eco è oggi un grande scrittore'},\n", + " {'score': 0.004773850552737713,\n", + " 'token': 14743,\n", + " 'token_str': 'divenuto',\n", + " 'sequence': 'Umberto Eco è divenuto un grande scrittore'}]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fill = pipeline('fill-mask',model=italian_model_name)\n", + "masked_sentence = 'Umberto Eco è [MASK] un grande scrittore'\n", + "fill(masked_sentence)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "df12c632", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'score': 0.1162119209766388,\n", + " 'token': 7607,\n", + " 'token_str': 'troviamo',\n", + " 'sequence': 'a che ora ci troviamo?'},\n", + " {'score': 0.06763438880443573,\n", + " 'token': 17567,\n", + " 'token_str': 'aspettiamo',\n", + " 'sequence': 'a che ora ci aspettiamo?'},\n", + " {'score': 0.06530702114105225,\n", + " 'token': 1303,\n", + " 'token_str': 'sarà',\n", + " 'sequence': 'a che ora ci sarà?'},\n", + " {'score': 0.05703262239694595,\n", + " 'token': 4238,\n", + " 'token_str': 'vediamo',\n", + " 'sequence': 'a che ora ci vediamo?'},\n", + " {'score': 0.05302278324961662,\n", + " 'token': 17387,\n", + " 'token_str': 'vedete',\n", + " 'sequence': 'a che ora ci vedete?'}]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masked_sentence = 'a che ora ci [MASK]?'\n", + "fill(masked_sentence)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6d87ab9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'score': 0.36587169766426086,\n", + " 'token': 510,\n", + " 'token_str': 'cosa',\n", + " 'sequence': 'a che cosa ci troviamo?'},\n", + " {'score': 0.046960778534412384,\n", + " 'token': 739,\n", + " 'token_str': 'modo',\n", + " 'sequence': 'a che modo ci troviamo?'},\n", + " {'score': 0.04375715181231499,\n", + " 'token': 212,\n", + " 'token_str': 'non',\n", + " 'sequence': 'a che non ci troviamo?'},\n", + " {'score': 0.023330960422754288,\n", + " 'token': 1302,\n", + " 'token_str': 'livello',\n", + " 'sequence': 'a che livello ci troviamo?'},\n", + " {'score': 0.02314317226409912,\n", + " 'token': 1711,\n", + " 'token_str': 'condizioni',\n", + " 'sequence': 'a che condizioni ci troviamo?'}]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masked_sentence = 'a che [MASK] ci troviamo?'\n", + "fill(masked_sentence)" + ] + }, + { + "cell_type": "markdown", + "id": "ffa2e70f", + "metadata": {}, + "source": [ + "## 2 : Zero-Shot classification task example\n", + "\n", + "uso modello multilingua ( unico disponibile su huggingface in grado di performare l'inferenza zero-shot in italiano)\n", + " \n", + "link generico in italiano sullo __zero-shot__ learning e inference: https://zephyrnet.com/it/Zero-shot-learning-puoi-classificare-un-oggetto-senza-vederlo-prima/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ba49a3f4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at dbmdz/bert-base-italian-cased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias']\n", + "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at dbmdz/bert-base-italian-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", + "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. Define a descriptive label2id mapping in the model config to ensure correct outputs.\n" + ] + } + ], + "source": [ + "zs = pipeline(\"zero-shot-classification\",model=italian_model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f11053eb", + "metadata": {}, + "outputs": [], + "source": [ + "zs('che merda di giornata', candidate_labels=['positivo','negativo'],hypothesis_template='questa frase ha un tono {}',) #multiclass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c151ae8", + "metadata": {}, + "outputs": [], + "source": [ + "zs(sequences='dove vai stasera?',hypothesis_template='questa frase è una {}',candidate_labels=['affermazione','domanda'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87c76244", + "metadata": {}, + "outputs": [], + "source": [ + "zs(sequences='where are you going?',hypothesis_template='this phrase is a {}',candidate_labels=['question','affirmation'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a29d6a5", + "metadata": {}, + "outputs": [], + "source": [ + "zs(sequences='Voglio uscire con voi stasera, ma devo pulire casa prima',candidate_labels=['dubbio','invito','carro','positivo','negativo'], multiclass=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fcf8ff42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sequence': 'Sono felice di uscire con voi stasera',\n", + " 'labels': ['relax',\n", + " 'pace',\n", + " 'indifferenza',\n", + " 'interesse',\n", + " 'fastidio',\n", + " 'dubbio',\n", + " 'noia',\n", + " 'rabbia',\n", + " 'sconforto',\n", + " 'calma',\n", + " 'tristezza',\n", + " 'eccitazione',\n", + " 'gioia',\n", + " 'felicità'],\n", + " 'scores': [0.07741541415452957,\n", + " 0.07665539532899857,\n", + " 0.0763017013669014,\n", + " 0.07597608864307404,\n", + " 0.07575443387031555,\n", + " 0.07570748031139374,\n", + " 0.0756787359714508,\n", + " 0.07537700980901718,\n", + " 0.07522716373205185,\n", + " 0.07521089166402817,\n", + " 0.07490663230419159,\n", + " 0.07221430540084839,\n", + " 0.07031130790710449,\n", + " 0.02326352894306183]}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#https://www.google.it/search?q=thayer+mood+plane+italia&biw=1528&bih=788&tbm=isch&source=iu&ictx=1&vet=1&fir=idr_CWra3dTA3M%252C3vr2PPAMir9TdM%252C_%253BEhLHcfw0fkhWHM%252CEh6xJHBGzk4cMM%252C_%253BaP-KPSMboAJg2M%252CnleMstLur1huUM%252C_%253BxHGASYvcnwBR5M%252CrZKJnx3na11TtM%252C_%253Bl2JjpwGxrk9aOM%252CEh6xJHBGzk4cMM%252C_%253BGC1du1ZkYglw-M%252Co4brRqZQk5p8CM%252C_%253BO8E8kjpRcFlMWM%252CnleMstLur1huUM%252C_%253BoYYMLq4zR-JFeM%252Co4brRqZQk5p8CM%252C_%253B1CIJmqw2RNg0VM%252C5o_XDTGFr1iuXM%252C_%253BaDO14J7XISRjIM%252CEh6xJHBGzk4cMM%252C_&usg=AI4_-kSIgRNOu3F0-x0YYMbNCEIyLXfEzg&sa=X&ved=2ahUKEwiyqNDN_cD3AhVc7rsIHWi3CFYQ9QF6BAghEAE#imgrc=EhLHcfw0fkhWHM\n", + "moods=['fastidio','rabbia','sconforto','tristezza','noia','pace','relax','calma','felicità','gioia','indifferenza','interesse','dubbio','eccitazione']\n", + "zs(sequences='Sono felice di uscire con voi stasera',candidate_labels=moods)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8ec3e16f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sequence': 'ti ammazzo',\n", + " 'labels': ['noia',\n", + " 'eccitazione',\n", + " 'pace',\n", + " 'dubbio',\n", + " 'gioia',\n", + " 'rabbia',\n", + " 'indifferenza',\n", + " 'tristezza',\n", + " 'sconforto',\n", + " 'felicità',\n", + " 'fastidio',\n", + " 'calma',\n", + " 'relax',\n", + " 'interesse'],\n", + " 'scores': [0.07284864038228989,\n", + " 0.07268563657999039,\n", + " 0.07168520241975784,\n", + " 0.07163083553314209,\n", + " 0.07157977670431137,\n", + " 0.07146152853965759,\n", + " 0.07145626842975616,\n", + " 0.07143832743167877,\n", + " 0.07120247930288315,\n", + " 0.07110902667045593,\n", + " 0.07106047123670578,\n", + " 0.07105831056833267,\n", + " 0.07071790099143982,\n", + " 0.07006552070379257]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sentenza = 'ti ammazzo'\n", + "zs(sequences=sentenza,candidate_labels=moods)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6a806191", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sequence': 'non capisco perchè te la prendi con me, ti ho già regalato tutte le mi capre, non posso darti anche il bue',\n", + " 'labels': ['eccitazione',\n", + " 'indifferenza',\n", + " 'gioia',\n", + " 'relax',\n", + " 'felicità',\n", + " 'dubbio',\n", + " 'tristezza',\n", + " 'fastidio',\n", + " 'pace',\n", + " 'rabbia',\n", + " 'noia',\n", + " 'interesse',\n", + " 'sconforto',\n", + " 'calma'],\n", + " 'scores': [0.07354768365621567,\n", + " 0.07300304621458054,\n", + " 0.07294241338968277,\n", + " 0.07219138741493225,\n", + " 0.07172053307294846,\n", + " 0.07144749164581299,\n", + " 0.07143128663301468,\n", + " 0.07104679197072983,\n", + " 0.07093875855207443,\n", + " 0.07085791230201721,\n", + " 0.0706729143857956,\n", + " 0.07063259184360504,\n", + " 0.07041624188423157,\n", + " 0.06915094703435898]}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sentenza = 'non capisco perchè te la prendi con me, ti ho già regalato tutte le mi capre, non posso darti anche il bue'\n", + "zs(sequences=sentenza,candidate_labels=moods, multiclass=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0d2d0f93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sequence': 'le tue orecchie sono bruttissime, non uscirò mai con te',\n", + " 'labels': ['logica', 'attualità', 'cotillons', 'estetica'],\n", + " 'scores': [0.2509896755218506,\n", + " 0.25027403235435486,\n", + " 0.24949680268764496,\n", + " 0.2492394745349884]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zs(sequences='le tue orecchie sono bruttissime, non uscirò mai con te',candidate_labels=['estetica','logica','attualità','cotillons'], multiclass=True)" + ] + }, + { + "cell_type": "markdown", + "id": "64c9b9af", + "metadata": {}, + "source": [ + "## 3 : POS-TAG task example\n", + "\n", + "Positional tagging, task già noto alla maggior parte delle persone aventi la terza elementare come __analisi grammaticale__\n", + "Nell'analisi grammaticale italiana ogni parola può appartenere ad una delle nove categorie lessicali dell'italiano, cinque variabili: articolo, aggettivo, sostantivo o nome, pronome, verbo, e quattro invariabili: avverbio, preposizione, congiunzione, interiezione o esclamazione. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "118e6275", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9b74db59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "testo originale ---> San Francisco prevede di bandire i robot di consegna porta a porta\n", + "\n", + "*** Pos-Tagging\n", + "WORD POS-TAG SYNTACTIC DEP\n", + "-------------------------------------\n", + "San PROPN nsubj\n", + "Francisco PROPN flat:name\n", + "prevede VERB ROOT\n", + "di ADP mark\n", + "bandire VERB xcomp\n", + "i DET det\n", + "robot NOUN obj\n", + "di ADP case\n", + "consegna NOUN nmod\n", + "porta VERB advcl\n", + "a ADP case\n", + "porta NOUN obl\n", + "\n", + "*** Analisi Entità specifiche nel testo:\n", + "ENTITY NAME LABEL\n", + "-------------------------------------\n", + "San Francisco LOC\n" + ] + } + ], + "source": [ + "import spacy\n", + "from spacy.lang.it.examples import sentences \n", + "\n", + "try:\n", + " nlp = spacy.load(\"it_core_news_lg\")\n", + "except:\n", + " !python -m spacy download it_core_news_lg\n", + " nlp = spacy.load(\"it_core_news_lg\")\n", + "doc = nlp(sentences[2])\n", + "print('testo originale ---> ',doc.text)\n", + "print('\\n*** Pos-Tagging')\n", + "print('WORD', ' '*(10-len('word')), 'POS-TAG',' '*(10-len('POS-TAG')), 'SYNTACTIC DEP')\n", + "print('-------------------------------------')\n", + "for token in doc:\n", + " print(token.text,' '*(10-len(token.text)), token.pos_,' '*(10-len(token.pos_)), token.dep_ )\n", + "\n", + "print('\\n*** Analisi Entità specifiche nel testo:') \n", + "print('ENTITY NAME',' '*(20-len('ENTITY NAME')), 'LABEL')\n", + "print('-------------------------------------')\n", + "for ent in doc.ents:\n", + " print(ent.text ,' '*(20-len(ent.text)), ent.label_)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "92712a0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "testo originale ---> Londra è una grande città del Regno Unito.\n", + "\n", + "*** Pos-Tagging\n", + "WORD POS-TAG SYNTACTIC DEP\n", + "-------------------------------------\n", + "Londra PROPN nsubj\n", + "è AUX cop\n", + "una DET det\n", + "grande ADJ amod\n", + "città NOUN ROOT\n", + "del ADP case\n", + "Regno PROPN nmod\n", + "Unito PROPN flat:name\n", + ". PUNCT punct\n", + "\n", + "*** Analisi Entità specifiche nel testo:\n", + "ENTITY NAME LABEL\n", + "-------------------------------------\n", + "Londra LOC\n", + "Regno Unito LOC\n" + ] + } + ], + "source": [ + "doc = nlp(sentences[3])\n", + "print('testo originale ---> ',doc.text)\n", + "print('\\n*** Pos-Tagging')\n", + "print('WORD', ' '*(10-len('word')), 'POS-TAG',' '*(10-len('POS-TAG')), 'SYNTACTIC DEP')\n", + "print('-------------------------------------')\n", + "for token in doc:\n", + " print(token.text,' '*(10-len(token.text)), token.pos_,' '*(10-len(token.pos_)), token.dep_ )\n", + "\n", + "print('\\n*** Analisi Entità specifiche nel testo:') \n", + "print('ENTITY NAME',' '*(20-len('ENTITY NAME')), 'LABEL')\n", + "print('-------------------------------------')\n", + "for ent in doc.ents:\n", + " print(ent.text ,' '*(20-len(ent.text)), ent.label_)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7ab8299b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "testo originale ---> non capisco perchè te la prendi con Giacomo, ti ha già regalato tutte le sue capre, non può darti anche il bue\n", + "\n", + "*** Pos-Tagging\n", + "WORD POS-TAG SYNTACTIC DEP\n", + "-------------------------------------\n", + "non ADV advmod\n", + "capisco VERB ROOT\n", + "perchè SCONJ mark\n", + "te PRON expl\n", + "la PRON obj\n", + "prendi VERB ccomp\n", + "con ADP case\n", + "Giacomo PROPN obl\n", + ", PUNCT punct\n", + "ti PRON iobj\n", + "ha AUX aux\n", + "già ADV advmod\n", + "regalato VERB conj\n", + "tutte DET det:predet\n", + "le DET det\n", + "sue DET det:poss\n", + "capre NOUN obj\n", + ", PUNCT punct\n", + "non ADV advmod\n", + "può AUX aux\n", + "darti VERB conj\n", + "anche ADV advmod\n", + "il DET det\n", + "bue NOUN obj\n", + "\n", + "*** Analisi Entità specifiche nel testo:\n", + "ENTITY NAME LABEL\n", + "-------------------------------------\n", + "Giacomo PER\n" + ] + } + ], + "source": [ + "doc = nlp(sentenza.replace('me','Giacomo').replace('mi','sue').replace('ho','ha').replace('posso','può'))\n", + "print('testo originale ---> ',doc.text)\n", + "print('\\n*** Pos-Tagging')\n", + "print('WORD', ' '*(10-len('word')), 'POS-TAG',' '*(10-len('POS-TAG')), 'SYNTACTIC DEP')\n", + "print('-------------------------------------')\n", + "for token in doc:\n", + " print(token.text,' '*(10-len(token.text)), token.pos_,' '*(10-len(token.pos_)), token.dep_ )\n", + "\n", + "print('\\n*** Analisi Entità specifiche nel testo:') \n", + "print('ENTITY NAME',' '*(20-len('ENTITY NAME')), 'LABEL')\n", + "print('-------------------------------------')\n", + "for ent in doc.ents:\n", + " print(ent.text ,' '*(20-len(ent.text)), ent.label_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f5fafff", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geografia", + "language": "python", + "name": "geografia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ccq/ccq_explore.pdf b/ccq/ccq_explore.pdf new file mode 100644 index 0000000..a10761d Binary files /dev/null and b/ccq/ccq_explore.pdf differ diff --git a/colemano/README.md b/colemano/README.md new file mode 100644 index 0000000..d070925 --- /dev/null +++ b/colemano/README.md @@ -0,0 +1,24 @@ +# colemano +_un modulo scritto coi piedi_ + +Video/Image Recognition applicato all'interpretazione della lingua dei segni + +Sono utilizzate delle reti neurali (link-wiki-NN) per il riconoscimento (preparazione ed allenamento in train/ASL). +Le reti utilizzate sfruttano il transfer-learning (link-wiki-transfer-learning) +per adesso è considerata solo la ASL (American Sign Language) perchè i dataset ed un bel po di script erano già pronti. + +L'idea è di traslare il tutto anche con la LIS. + +le due principali funzionalità sono: +- data un immagine o video predirre con i modelli allenati il segno e la parola associata. +- data una parola fornire un esempio grafico/fotografico/video del segno associato + +## usage + +per lanciare la demo con flask lancia il seguente script: + +`python flask/app.py` + +cerca di avere uno sfondo uniforme durante la ripresa. + +apri il browser e vai su http://127.0.0.1:5000/ diff --git a/colemano/__init__.py b/colemano/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/colemano/demo_flask/__init__.py b/colemano/demo_flask/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/colemano/demo_flask/app.py b/colemano/demo_flask/app.py new file mode 100755 index 0000000..32afb9a --- /dev/null +++ b/colemano/demo_flask/app.py @@ -0,0 +1,50 @@ +#!/home//agropunx/anaconda3/envs/geografia/bin/python + +#Import necessary librarie +from flask import Flask, render_template, request, Response, redirect,session +from flask_bootstrap import Bootstrap +import cv2, sys, os + +di=os.path.dirname(os.path.abspath(__file__)) + +#sys.path.append('/'.join(di.split('/')[:-1])) +cur_dir = os.getcwd().split('/')[-1] +if cur_dir=='geografia': + sys.path.append('/'.join(di.split('/')[:-2])) + from colemano.src import colemano as clm + from colemano.src.config import * +else: + sys.path.append('/'.join(di.split('/')[:-1])) + from src import colemano as clm + from src.config import * + +#Initialize the Flask app +app = Flask(__name__) +coso = clm.ColeMano() + +@app.route('/',methods=['GET','POST']) +def index(): + if ' ON ' in request.args: + coso.ison = True + + elif 'OFF&RESET' in request.args: + coso.ison = False + coso.firstFrame = None + return render_template('index.html') + + +@app.route('/colemano_routine',methods=['GET','POST']) +def colemano_routine(): + return Response(coso.flask_routine(), mimetype='multipart/x-mixed-replace; boundary=frame') + + +if __name__ == '__main__': + + try: + app.run(debug=True) + except: + cv2.destroyAllWindows() + finally: + cv2.destroyAllWindows() + + diff --git a/colemano/demo_flask/templates/index.html b/colemano/demo_flask/templates/index.html new file mode 100644 index 0000000..4e397ae --- /dev/null +++ b/colemano/demo_flask/templates/index.html @@ -0,0 +1,46 @@ + + + + + + + + + + + Live Streaming Demonstration + + +
+
+
+

Colemano Live Demo : riconoscimento American Sign Language

+ +
+
+
+
+
lascia lo sfondo nel riquadro blu piu neutro ed omogeneo possibile
+
+
+
+
+
cerca di non apparire con i tuoi arti e corpicino la dentro quando fai partire il riconoscimento con i tasti qua sotto
+
+
+
+
+

Prediction On/Off

+
+ + +
+
+
+
+ + + + + diff --git a/colemano/src/__init__.py b/colemano/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/colemano/src/colemano.py b/colemano/src/colemano.py new file mode 100644 index 0000000..f8461eb --- /dev/null +++ b/colemano/src/colemano.py @@ -0,0 +1,233 @@ +#!/usr/bin/python3 + +import traceback +import datetime, imutils, cv2, argparse,os,sys +from tensorflow.keras.models import load_model +from tensorflow.keras.preprocessing.image import ImageDataGenerator +import numpy as np + +cur_dir = os.getcwd().split('/')[-1] +if cur_dir=='colemano': + from src.config import * +elif cur_dir =='src': + from config import * +elif cur_dir=='geografia': + from colemano.src.config import * +elif cur_dir=='demo_flask': + sys.path.append('./..') + from src.config import * + + + +class ColeMano(): + def __init__(self, gestures_path=GESTURES_PATH,model_path=MODEL_PATH, predict_path=PREDICT_PATH): + self.model = load_model(model_path) + self.gestures_path = gestures_path + self.prepare_predict_path(predict_path) + self.init_gestures() + self.live=False + self.ison=False + + def prepare_predict_path(self,predict_path): + self.predict_path = predict_path + '/0' + os.makedirs(self.predict_path,exist_ok=True) + for f in os.listdir(self.predict_path): + os.remove(f"{self.predict_path}/{f}") + + def init_gestures(self): + + self.datagen_root = ImageDataGenerator( + samplewise_center=True, + samplewise_std_normalization=True, + brightness_range=[0.8, 1.0], + zoom_range=[1.0, 1.2] + ) + self.gestures = self.datagen_root.flow_from_directory( + self.gestures_path , + target_size=TARGET_SIZE, + color_mode="rgb", + shuffle=False, + class_mode='categorical', + batch_size=1).class_indices + self.gestures = {v:k for k,v in self.gestures.items()} + + def video_init(self, recorded_video=None): + if recorded_video: + self.video = cv2.VideoCapture(recorded_video) + self.live =False + else: + self.video = cv2.VideoCapture(-1) + self.live = True + + print(f'video opened : {self.video.isOpened()}') + self.video.set(cv2.CAP_PROP_FRAME_WIDTH, 480) + self.video.set(cv2.CAP_PROP_FRAME_HEIGHT, 420) + + def show_example(self,what): + if isinstance(what,int): + what=self.gestures[what] + + def get_gray(self, frame): + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + return cv2.GaussianBlur(gray, (21, 21), 0) + + def get_frame(self, bg_frame=None,x_origin=-1, y_origin=-1, width=-1, height=-1): + if not bg_frame: + success, bg_frame = self.video.read() + else: + success=True + if not success: + bg_frame = np.zeros((480,420,3)) + if x_origin+y_origin+width+height>=4: + cv2.rectangle(bg_frame, (x_origin, y_origin), (x_origin + width, y_origin + height), (255, 0, 0), 2) + frame = bg_frame[x_origin:x_origin+width,y_origin:y_origin+height,:] + else: + frame=bg_frame + return bg_frame, frame + + def thresholdContours(self,gray): + # compute the absolute difference between the current frame and first frame + frameDelta = cv2.absdiff(self.firstFrame, gray) + thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1] + # dilate the thresholded image to fill in holes, then find contours on thresholded image + thresh = cv2.dilate(thresh, None, iterations=2) + cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + return imutils.grab_contours(cnts) + + def contoursLooper(self,frame, cnts,min_area=7000,show_contour_box=True): + # loop over the contours + current_area_sum=0 + for ic,c in enumerate(cnts): + # if the contour is too small, ignore it + last_area = cv2.contourArea(c) + if last_area < min_area: + continue + current_area_sum += last_area + # compute the bounding box for the contour, draw it on the frame and update the text + if show_contour_box: + (x, y, w, h) = cv2.boundingRect(c) + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) + return current_area_sum + + def predict_routine(self,gesture,ix=0,num_predict_samples=1): + if ix==num_predict_samples-1: + probs = self.model.predict(self.datagen_root.flow(self.frames,batch_size=1)) + probs = np.mean(probs,axis=0) # or median + pred = np.argmax(probs) + gesture = self.gestures[pred] + elif ix==num_predict_samples: + ix=0 + return gesture,ix + + def routine_check(self,frame,ix,last_area_sum,current_area_sum, same_gesture_ratio=.3): + if last_area_sum==0: + last_area_sum=current_area_sum + key = cv2.waitKey(1) & 0xFF + if key == ord(QUIT_BUTTON): + ix=-1 + elif (key == ord(PHOTO_BUTTON)) or ((current_area_sum!=0) and (abs((last_area_sum - current_area_sum) / last_area_sum) < same_gesture_ratio)): + self.frames[ix,...] = frame + ix+=1 + return ix + + def clean_cv2(self): + self.video.release() + cv2.destroyAllWindows() + + + def flask_routine(self, min_area=7000,same_gesture_ratio=0.3, num_predict_samples=3, show_contour_box=True): + + if not self.live: + self.video_init() + self.firstFrame = None + last_area_sum=0 + ix=0 + gesture='no sign' + empty_frames = np.zeros((num_predict_samples,TARGET_SIZE[0],TARGET_SIZE[1],3)) + + self.frames = empty_frames.copy() + while True: + try: + bg_frame, frame = self.get_frame(None,X_ORIGIN,Y_ORIGIN,WIDTH,HEIGHT) + gray = self.get_gray(frame) + success=True + except Exception as e: + print(traceback.format_exc()) + print("\n\n scostumat', non mi aspettavo proprio un errore come di cui sopra da te \n\n") + bg_frame = np.zeros((480,420,3)) + bg_frame, frame = self.get_frame(bg_frame,X_ORIGIN,Y_ORIGIN,WIDTH,HEIGHT) + success=False + + if self.ison and success: + gray = self.get_gray(frame) + # if the first frame is None, initialize it + if self.firstFrame is None: + self.firstFrame = gray + continue + cnts = self.thresholdContours(gray) + current_area_sum = self.contoursLooper(frame,cnts, min_area, show_contour_box) + + ix=self.routine_check(frame,ix,last_area_sum,current_area_sum,same_gesture_ratio) + if ix<0: + break + gesture,ix = self.predict_routine(gesture, ix=ix, num_predict_samples=num_predict_samples) + + last_area_sum=current_area_sum + + cv2.putText(frame, "---> {} <---".format(gesture), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) + """ + else: + cv2.putText(bg_frame, "lascia lo sfondo nel riquadro piu neutro ed omogeneo possibile",(10,10),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),2) + cv2.putText(bg_frame, "cerca di non apparire con il tuo corpicino la dentro",(10,20),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),2) + cv2.putText(bg_frame, "infine premi start per iniziare il riconoscimento",(10,30),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),2) + """ + ret, buffer = cv2.imencode('.jpg', bg_frame) + + frame = buffer.tobytes() + yield (b'--frame\r\n' + b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') # concat frame one by one and show result + #yield bg_frame + print("\n\n chiudo tutte cose lisio \n\n") + self.clean_cv2() + + + def routine(self, min_area=7000,same_gesture_ratio=0.3, num_predict_samples=3, show_contour_box=True): + if not self.live: + self.video_init() + self.firstFrame = None + last_area_sum=0 + ix=0 + gesture='nada' + self.frames = np.zeros((num_predict_samples,TARGET_SIZE[0],TARGET_SIZE[1],3)) + try: + while True: + bg_frame, frame = self.get_frame(None,X_ORIGIN,Y_ORIGIN,WIDTH,HEIGHT) + gray = self.get_gray(frame) + # if the first frame is None, initialize it + if self.firstFrame is None: + self.firstFrame = gray + continue + cnts = self.thresholdContours(gray) + current_area_sum = self.contoursLooper(frame,cnts, min_area, show_contour_box) + + ix=self.routine_check(frame,ix,last_area_sum,current_area_sum,same_gesture_ratio) + if ix<0: + break + gesture,ix = self.predict_routine(gesture, ix=ix, num_predict_samples=num_predict_samples) + + last_area_sum=current_area_sum + + cv2.putText(frame, "---> {} <---".format(gesture), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) + + cv2.imshow("Colemano Reco",bg_frame) + print("\n\n chiudo tutte cose lisio \n\n") + self.clean_cv2() + except Exception as e: + print(traceback.format_exc()) + print("\n\n scostumat', non mi aspettavo proprio un errore come di cui sopra da te \n\n") + finally: + self.clean_cv2() + + + + diff --git a/colemano/src/config.py b/colemano/src/config.py new file mode 100644 index 0000000..012fcfe --- /dev/null +++ b/colemano/src/config.py @@ -0,0 +1,24 @@ +import os +cur_dir = os.getcwd().split('/')[-1] +WORK_DIR = 'colemano' +if cur_dir==WORK_DIR: + WORK_DIR='.' +elif (cur_dir=='demo_flask') | (cur_dir=='src'): + WORK_DIR='..' +elif cur_dir=='geografia': + WORK_DIR='colemano' + + +QUIT_BUTTON = 'q' +PHOTO_BUTTON = 'p' +MODEL_PATH = f'{WORK_DIR}/train/ASL/static/models/asl_static_D.h5' +GESTURES_PATH = f'{WORK_DIR}/src/ASL_gestures' +PREDICT_PATH = f'{WORK_DIR}/tmp_camera' +TARGET_SIZE=(200,200) + +X_ORIGIN = 100 +Y_ORIGIN = 100 +WIDTH = 200 +HEIGHT = 200 + + diff --git a/colemano/train/ASL/dynamic/word_level_detection.ipynb b/colemano/train/ASL/dynamic/word_level_detection.ipynb new file mode 100644 index 0000000..5e69571 --- /dev/null +++ b/colemano/train/ASL/dynamic/word_level_detection.ipynb @@ -0,0 +1,91 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# acchiappa il dataset WLASL\n", + "\n", + "__data source__:\n", + "\n", + "https://github.com/dxli94/WLASL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__scarica e prepara il dataset__ (comandi bash)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "colemano$ git clone https://github.com/dxli94/WLASL.git ASL_train/WLASL\n", + "\n", + "# installa youtube-dl\n", + "colemano$ sudo curl -L https://yt-dl.org/downloads/latest/youtube-dl -o /usr/local/bin/youtube-dl\n", + "colemano$ sudo chmod a+rx /usr/local/bin/youtube-dl\n", + "\n", + "# Download video raw, ci mette un botto tipo 1 giorno\n", + "colemano$ cd ASL_train/WLASL/start_kit\n", + "ASL_train/WLASL/start_kit$ python3 video_downloader.py\n", + "\n", + "# Estrai i samples dai video\n", + "ASL_train/WLASL/start_kit$ python3 preprocess.py\n", + "\n", + "# un po di url sono stati spostati dalla pubblicazione, quindi lancia pure per richiederli agli autori,\n", + "# altrimenti sticaz, io l'ho fatto appena rimetto a posto metto tutti i dataset in condivisione.\n", + "# (di 20000 video circa un 1/3 son sparuti)\n", + "ASL_train/WLASL/start_kit$ python find_missing.py\n", + "# volendo si possono richiedere i videii mancanti in missing.txt agli autori" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__transfer learning e training__ (sempre bash)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "ASL_train/WLASL/start_kit$ mv videos ../data/WLASL2000/\n", + "\n", + "# per fare transfer-learning automagico scarica weights.zip e archieved.zip, unzippali e mettili in WLASL/code/I3D.\n", + "# kinetics weights---> https://drive.google.com/uc?id=1JgTRHGBRCHyHRT_rAF0fOjnfiFefXkEd&export=download\n", + "# archieve---> https://drive.google.com/uc?id=1jALimVOB69ifYkeT0Pe297S1z4U3jC48&export=download\n", + "# poi,\n", + "\n", + "ASL_train/WLASL/start_kit$ cd ../code/I3D/\n", + "\n", + "# lancia il training, serve la gpu per adesso (12-01-2022)..\n", + "ASL_train/WLASL/code/I3D/$ train_i3d.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/colemano/train/ASL/static/models/asl_static_A/keras_metadata.pb b/colemano/train/ASL/static/models/asl_static_A/keras_metadata.pb new file mode 100644 index 0000000..b975a1f --- /dev/null +++ b/colemano/train/ASL/static/models/asl_static_A/keras_metadata.pb @@ -0,0 +1,14 @@ + +?root"_tf_keras_network*?{"name": "asl_static_A", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "asl_static_A", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}, "name": "input_3", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "conv2d_8", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_8", "inbound_nodes": [[["input_3", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_8", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_8", "inbound_nodes": [[["conv2d_8", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_10", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}, "name": "dropout_10", "inbound_nodes": [[["max_pooling2d_8", 0, 0, {}]]]}, {"class_name": "Flatten", "config": {"name": "flatten_2", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten_2", "inbound_nodes": [[["dropout_10", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 512, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_4", "inbound_nodes": [[["flatten_2", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_11", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}, "name": "dropout_11", "inbound_nodes": [[["dense_4", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 25, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_5", "inbound_nodes": [[["dropout_11", 0, 0, {}]]]}], "input_layers": [["input_3", 0, 0]], "output_layers": [["dense_5", 0, 0]]}, "shared_object_id": 14, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 28, 28, 1]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 28, 28, 1]}, "float32", "input_3"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 28, 28, 1]}, "float32", "input_3"]}, "keras_version": "2.7.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "asl_static_A", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}, "name": "input_3", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Conv2D", "config": {"name": "conv2d_8", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_8", "inbound_nodes": [[["input_3", 0, 0, {}]]], "shared_object_id": 3}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_8", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_8", "inbound_nodes": [[["conv2d_8", 0, 0, {}]]], "shared_object_id": 4}, {"class_name": "Dropout", "config": {"name": "dropout_10", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}, "name": "dropout_10", "inbound_nodes": [[["max_pooling2d_8", 0, 0, {}]]], "shared_object_id": 5}, {"class_name": "Flatten", "config": {"name": "flatten_2", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten_2", "inbound_nodes": [[["dropout_10", 0, 0, {}]]], "shared_object_id": 6}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 512, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_4", "inbound_nodes": [[["flatten_2", 0, 0, {}]]], "shared_object_id": 9}, {"class_name": "Dropout", "config": {"name": "dropout_11", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}, "name": "dropout_11", "inbound_nodes": [[["dense_4", 0, 0, {}]]], "shared_object_id": 10}, {"class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 25, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 11}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 12}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_5", "inbound_nodes": [[["dropout_11", 0, 0, {}]]], "shared_object_id": 13}], "input_layers": [["input_3", 0, 0]], "output_layers": [["dense_5", 0, 0]]}}, "training_config": {"loss": "sparse_categorical_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 16}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 0.0010000000474974513, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2 + root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_3", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}}2 + +root.layer_with_weights-0"_tf_keras_layer* +{"name": "conv2d_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_8", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_3", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 1}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 28, 28, 1]}}2 + root.layer-2"_tf_keras_layer*{"name": "max_pooling2d_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_8", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_8", 0, 0, {}]]], "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 18}}2 + root.layer-3"_tf_keras_layer*{"name": "dropout_10", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_10", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}, "inbound_nodes": [[["max_pooling2d_8", 0, 0, {}]]], "shared_object_id": 5}2 + root.layer-4"_tf_keras_layer*{"name": "flatten_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_2", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "inbound_nodes": [[["dropout_10", 0, 0, {}]]], "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 19}}2 +root.layer_with_weights-1"_tf_keras_layer*{"name": "dense_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 512, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["flatten_2", 0, 0, {}]]], "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 21632}}, "shared_object_id": 20}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 21632]}}2 + root.layer-6"_tf_keras_layer*{"name": "dropout_11", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_11", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense_4", 0, 0, {}]]], "shared_object_id": 10}2 +root.layer_with_weights-2"_tf_keras_layer*{"name": "dense_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 25, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 11}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 12}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_11", 0, 0, {}]]], "shared_object_id": 13, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 512}}, "shared_object_id": 21}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 512]}}2 +^root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 22}2 +_root.keras_api.metrics.1"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 16}2 \ No newline at end of file diff --git a/colemano/train/ASL/static/models/asl_static_A/saved_model.pb b/colemano/train/ASL/static/models/asl_static_A/saved_model.pb new file mode 100644 index 0000000..6680653 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_A/saved_model.pb differ diff --git a/colemano/train/ASL/static/models/asl_static_A/variables/variables.data-00000-of-00001 b/colemano/train/ASL/static/models/asl_static_A/variables/variables.data-00000-of-00001 new file mode 100644 index 0000000..b192501 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_A/variables/variables.data-00000-of-00001 differ diff --git a/colemano/train/ASL/static/models/asl_static_A/variables/variables.index b/colemano/train/ASL/static/models/asl_static_A/variables/variables.index new file mode 100644 index 0000000..4dea7b4 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_A/variables/variables.index differ diff --git a/colemano/train/ASL/static/models/asl_static_B/keras_metadata.pb b/colemano/train/ASL/static/models/asl_static_B/keras_metadata.pb new file mode 100644 index 0000000..d84bf56 --- /dev/null +++ b/colemano/train/ASL/static/models/asl_static_B/keras_metadata.pb @@ -0,0 +1,22 @@ + +mroot"_tf_keras_network*l{"name": "model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}, "name": "input_4", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "conv2d_9", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_9", "inbound_nodes": [[["input_4", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_9", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_9", "inbound_nodes": [[["conv2d_9", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_10", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_10", "inbound_nodes": [[["max_pooling2d_9", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_10", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_10", "inbound_nodes": [[["conv2d_10", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_11", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_11", "inbound_nodes": [[["max_pooling2d_10", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_11", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_11", "inbound_nodes": [[["conv2d_11", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization", "inbound_nodes": [[["max_pooling2d_11", 0, 0, {}]]]}, {"class_name": "Flatten", "config": {"name": "flatten_3", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten_3", "inbound_nodes": [[["batch_normalization", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_12", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}, "name": "dropout_12", "inbound_nodes": [[["flatten_3", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 1024, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_6", "inbound_nodes": [[["dropout_12", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 29, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_7", "inbound_nodes": [[["dense_6", 0, 0, {}]]]}], "input_layers": [["input_4", 0, 0]], "output_layers": [["dense_7", 0, 0]]}, "shared_object_id": 26, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 32, 3]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 32, 32, 3]}, "float32", "input_4"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 32, 32, 3]}, "float32", "input_4"]}, "keras_version": "2.7.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}, "name": "input_4", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Conv2D", "config": {"name": "conv2d_9", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_9", "inbound_nodes": [[["input_4", 0, 0, {}]]], "shared_object_id": 3}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_9", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_9", "inbound_nodes": [[["conv2d_9", 0, 0, {}]]], "shared_object_id": 4}, {"class_name": "Conv2D", "config": {"name": "conv2d_10", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_10", "inbound_nodes": [[["max_pooling2d_9", 0, 0, {}]]], "shared_object_id": 7}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_10", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_10", "inbound_nodes": [[["conv2d_10", 0, 0, {}]]], "shared_object_id": 8}, {"class_name": "Conv2D", "config": {"name": "conv2d_11", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 9}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 10}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_11", "inbound_nodes": [[["max_pooling2d_10", 0, 0, {}]]], "shared_object_id": 11}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_11", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_11", "inbound_nodes": [[["conv2d_11", 0, 0, {}]]], "shared_object_id": 12}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 13}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 14}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 16}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization", "inbound_nodes": [[["max_pooling2d_11", 0, 0, {}]]], "shared_object_id": 17}, {"class_name": "Flatten", "config": {"name": "flatten_3", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten_3", "inbound_nodes": [[["batch_normalization", 0, 0, {}]]], "shared_object_id": 18}, {"class_name": "Dropout", "config": {"name": "dropout_12", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}, "name": "dropout_12", "inbound_nodes": [[["flatten_3", 0, 0, {}]]], "shared_object_id": 19}, {"class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 1024, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 20}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 21}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_6", "inbound_nodes": [[["dropout_12", 0, 0, {}]]], "shared_object_id": 22}, {"class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 29, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 23}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 24}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_7", "inbound_nodes": [[["dense_6", 0, 0, {}]]], "shared_object_id": 25}], "input_layers": [["input_4", 0, 0]], "output_layers": [["dense_7", 0, 0]]}}, "training_config": {"loss": "categorical_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 28}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 0.0010000000474974513, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2 + root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_4", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}}2 + +root.layer_with_weights-0"_tf_keras_layer* +{"name": "conv2d_9", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_9", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_4", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 3}}, "shared_object_id": 29}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 32, 3]}}2 + root.layer-2"_tf_keras_layer*{"name": "max_pooling2d_9", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_9", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_9", 0, 0, {}]]], "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 30}}2 + +root.layer_with_weights-1"_tf_keras_layer* +{"name": "conv2d_10", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_10", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_9", 0, 0, {}]]], "shared_object_id": 7, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 31}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 64]}}2 + root.layer-4"_tf_keras_layer*{"name": "max_pooling2d_10", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_10", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_10", 0, 0, {}]]], "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 32}}2 + root.layer_with_weights-2"_tf_keras_layer* +{"name": "conv2d_11", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_11", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 32, 32, 3]}, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 9}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 10}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_10", 0, 0, {}]]], "shared_object_id": 11, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 33}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 8, 8, 128]}}2 + root.layer-6"_tf_keras_layer*{"name": "max_pooling2d_11", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_11", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_11", 0, 0, {}]]], "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 34}}2 + root.layer_with_weights-3"_tf_keras_layer* {"name": "batch_normalization", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 13}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 14}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 16}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["max_pooling2d_11", 0, 0, {}]]], "shared_object_id": 17, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 256}}, "shared_object_id": 35}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4, 4, 256]}}2 +  root.layer-8"_tf_keras_layer*{"name": "flatten_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_3", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "inbound_nodes": [[["batch_normalization", 0, 0, {}]]], "shared_object_id": 18, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 36}}2 + + root.layer-9"_tf_keras_layer*{"name": "dropout_12", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_12", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}, "inbound_nodes": [[["flatten_3", 0, 0, {}]]], "shared_object_id": 19}2 + root.layer_with_weights-4"_tf_keras_layer*{"name": "dense_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 1024, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 20}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 21}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_12", 0, 0, {}]]], "shared_object_id": 22, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 4096}}, "shared_object_id": 37}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4096]}}2 + root.layer_with_weights-5"_tf_keras_layer*{"name": "dense_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 29, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 23}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 24}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_6", 0, 0, {}]]], "shared_object_id": 25, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 1024}}, "shared_object_id": 38}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1024]}}2 +root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 39}2 +root.keras_api.metrics.1"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 28}2 \ No newline at end of file diff --git a/colemano/train/ASL/static/models/asl_static_B/saved_model.pb b/colemano/train/ASL/static/models/asl_static_B/saved_model.pb new file mode 100644 index 0000000..c54b4c8 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_B/saved_model.pb differ diff --git a/colemano/train/ASL/static/models/asl_static_B/variables/variables.data-00000-of-00001 b/colemano/train/ASL/static/models/asl_static_B/variables/variables.data-00000-of-00001 new file mode 100644 index 0000000..71853e3 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_B/variables/variables.data-00000-of-00001 differ diff --git a/colemano/train/ASL/static/models/asl_static_B/variables/variables.index b/colemano/train/ASL/static/models/asl_static_B/variables/variables.index new file mode 100644 index 0000000..bede9c2 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_B/variables/variables.index differ diff --git a/colemano/train/ASL/static/models/asl_static_C.h5 b/colemano/train/ASL/static/models/asl_static_C.h5 new file mode 100644 index 0000000..8f3a4f1 Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_C.h5 differ diff --git a/colemano/train/ASL/static/models/asl_static_D.h5 b/colemano/train/ASL/static/models/asl_static_D.h5 new file mode 100644 index 0000000..c9c8c1f Binary files /dev/null and b/colemano/train/ASL/static/models/asl_static_D.h5 differ diff --git a/colemano/train/ASL/static/requirements.txt b/colemano/train/ASL/static/requirements.txt new file mode 100644 index 0000000..aae2f32 --- /dev/null +++ b/colemano/train/ASL/static/requirements.txt @@ -0,0 +1,11 @@ +tqdm +pandas +numpy +seaborn +matplotlib +cv2 +sklearn +keras +tensorflow +torch +torchvision diff --git a/colemano/train/ASL/static/static_detection.ipynb b/colemano/train/ASL/static/static_detection.ipynb new file mode 100644 index 0000000..ac0ac1b --- /dev/null +++ b/colemano/train/ASL/static/static_detection.ipynb @@ -0,0 +1,1208 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# cosa c'è in questo notebook \n", + "\n", + "Training di 4 reti neurali in grado di riconoscere dei set di gesti associati alla lingua americana dei segni (AmericanSignLanguage), tramite l'analisi di immagini statiche e metodi di image recognition.\n", + "\n", + "Ho considerato la ASL anzichè la LIS (Lingua Italiana dei Segni) dato che ho trovato molti piu esempi da scopiazzare per questa fase iniziale ma sopratutto perchè la quantita di dati disponibile è (forse?) ovviamente maggiore.\n", + "\n", + "non credere all'hype, più che intelligenti le AI sono ingorde: + DATI + RISULTATO, qualsiasi la struttura del dato\n", + "detto questo se +dato comporta +risultato, allora perchè non cambiare il dato ;-)\n", + "\n", + "Ciò nonostante il meccanismo è del tutto analogo, tanto dal punto di vista piu astratto di relazione di insiemi quanto da quello piu pratico per cui tra l'italiano e l'inglese-americano _comuni_ c'è un buon grado di traducibilità; ovvero dal punto di vista semiotico il residuo comunicativo non è eccessivamente significativo in quanto appartanenti alla stessa cultura occidentale ed aventi una simile origine a livello geografico e linguistico.\n", + "\n", + "Il linguaggio utilizzato è __python3__ ed il framework AI/ML di paciocco è __tensorflow__ (di google) con __keras__ come API di alto livello.\n", + "\n", + "I 4 modelli sotto allenati e pacioccati sono ordinati per crescente profondità di architettura (sono tutti modelli ConvolutionalNeuralNetwork) e quantita/complessità di dato, quindi si parte da scemo-piccolo e si arriva a saccente-grande.\n", + "\n", + "PS per sapere cosa sono CNN framework LIS, ASL ecc ecc dai un occhiata al wiki:\n", + "https://git.tropici.net/agropunx/geografia/wiki/_pages\n", + "\n", + "PPS2\n", + "IO HO GIA SCARICATO PROCESSATO I DATI QUINDI IN CASO PROVA A CONTATTARMI DIRETTAMENTE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__General Imports__" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# general\n", + "import os, random,pickle\n", + "from time import time\n", + "from tqdm import tqdm\n", + "\n", + "#numeric and df\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "#plot\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# image and computer vision\n", + "import cv2\n", + "from skimage.transform import resize\n", + "from IPython.display import Image\n", + "\n", + "# data shuffling and metrics\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "\n", + "# ML/AI stuff\n", + "import keras\n", + "from keras.layers import MaxPooling2D,MaxPool2D, Dense, Flatten, Dropout\n", + "from keras.callbacks import TensorBoard, ReduceLROnPlateau\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.applications.inception_v3 import InceptionV3\n", + "\n", + "from tensorflow.keras.layers import Conv2D\n", + "from tensorflow.keras.optimizers import RMSprop, Adam, SGD\n", + "from tensorflow.keras.utils import to_categorical" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# modello ASL_static_A: 3-layer-CNN con dataset MNIST-AmericanSignLanguage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"asl_static_A\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 28, 28, 1)] 0 \n", + " \n", + " conv2d_2 (Conv2D) (None, 26, 26, 128) 1280 \n", + " \n", + " max_pooling2d_2 (MaxPooling (None, 13, 13, 128) 0 \n", + " 2D) \n", + " \n", + " dropout_2 (Dropout) (None, 13, 13, 128) 0 \n", + " \n", + " flatten (Flatten) (None, 21632) 0 \n", + " \n", + " dense (Dense) (None, 512) 11076096 \n", + " \n", + " dropout_3 (Dropout) (None, 512) 0 \n", + " \n", + " dense_1 (Dense) (None, 25) 12825 \n", + " \n", + "=================================================================\n", + "Total params: 11,090,201\n", + "Trainable params: 11,090,201\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "#Defining the Convolutional Neural Network\n", + "\n", + "img_input = keras.Input(shape=(28, 28, 1))\n", + "x = Conv2D(32, (3, 3), input_shape = (28,28,1), activation='relu')(img_input)\n", + "x = MaxPooling2D(pool_size = (2, 2))(x)\n", + "x = Dropout(0.25)(x)\n", + "\n", + "x = Conv2D(64, (3, 3), input_shape = (28,28,1), activation='relu')(img_input)\n", + "x = MaxPooling2D(pool_size = (2, 2))(x)\n", + "x = Dropout(0.25)(x)\n", + "\n", + "x = Conv2D(128, (3, 3), input_shape = (28,28,1), activation='relu')(img_input)\n", + "x = MaxPooling2D(pool_size = (2, 2))(x)\n", + "x = Dropout(0.25)(x)\n", + "\n", + "x = Flatten()(x)\n", + "x = units = Dense(512, activation = 'relu')(x)\n", + "x = Dropout(0.25)(x)\n", + "out = Dense(units = 25, activation = 'softmax')(x)\n", + "\n", + "asl_static_A = keras.Model(inputs=img_input,outputs=out, name='asl_static_A')\n", + "\n", + "asl_static_A.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def train(model, X_in, y_in, optimizer = Adam(learning_rate=.001), loss='categorical_crossentropy', metrics=['accuracy'],\n", + " batch_size = 512, epochs = 50, verbose = 1):\n", + "\n", + " model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n", + "\n", + " start = time()\n", + " history = model.fit(X_in['train'], y_in['train'], batch_size=batch_size, epochs=epochs, validation_split=0.1, shuffle = True, verbose=1)\n", + " train_time = time() - start\n", + "\n", + " #model.summary()\n", + " plt.figure(figsize=(12, 12))\n", + " plt.subplot(3, 2, 1)\n", + " plt.plot(history.history['accuracy'], label = 'train_accuracy')\n", + " plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n", + " plt.xlabel('epoch')\n", + " plt.ylabel('accuracy')\n", + " plt.legend()\n", + " plt.subplot(3, 2, 2)\n", + " plt.plot(history.history['loss'], label = 'train_loss')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dataset source\n", + "\n", + "MNIST dataset for the American Sign Language (ASL)\n", + "\n", + "warnnn download tocca loggarsi\n", + "\n", + "url https://www.kaggle.com/datamunge/sign-language-mnist\n", + "\n", + "messo in ./data/mnist" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Hand gestures of ASL\n", + "Image('data/mnist/amer_sign2.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labelpixel1pixel2pixel3pixel4pixel5pixel6pixel7pixel8pixel9...pixel775pixel776pixel777pixel778pixel779pixel780pixel781pixel782pixel783pixel784
03107118127134139143146150153...207207207207206206206204203202
16155157156156156157156158158...691491288794163175103135149
22187188188187187186187188187...202201200199198199198195194195
32211211212212211210211210210...235234233231230226225222229163
413164167170172176179180184185...92105105108133163157163164179
\n", + "

5 rows × 785 columns

\n", + "
" + ], + "text/plain": [ + " label pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\n", + "0 3 107 118 127 134 139 143 146 150 \n", + "1 6 155 157 156 156 156 157 156 158 \n", + "2 2 187 188 188 187 187 186 187 188 \n", + "3 2 211 211 212 212 211 210 211 210 \n", + "4 13 164 167 170 172 176 179 180 184 \n", + "\n", + " pixel9 ... pixel775 pixel776 pixel777 pixel778 pixel779 pixel780 \\\n", + "0 153 ... 207 207 207 207 206 206 \n", + "1 158 ... 69 149 128 87 94 163 \n", + "2 187 ... 202 201 200 199 198 199 \n", + "3 210 ... 235 234 233 231 230 226 \n", + "4 185 ... 92 105 105 108 133 163 \n", + "\n", + " pixel781 pixel782 pixel783 pixel784 \n", + "0 206 204 203 202 \n", + "1 175 103 135 149 \n", + "2 198 195 194 195 \n", + "3 225 222 229 163 \n", + "4 157 163 164 179 \n", + "\n", + "[5 rows x 785 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df = pd.read_csv('data/mnist/sign_mnist_train.csv')\n", + "test_df = pd.read_csv('data/mnist/sign_mnist_test.csv')\n", + "\n", + "train_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__prepara il dataset da allenare__" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#labels\n", + "class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y' ]\n", + "X = {\n", + " 'train': train_df.values[:, 1:] / 255,\n", + " 'test': test_df.values[:, 1:] / 255\n", + "}\n", + "\n", + "y = {\n", + " 'train': train_df.values[:, 0],\n", + " 'test' : test_df.values[:,0]\n", + "}\n", + "\n", + "\n", + "X['train'] = X['train'].reshape(X['train'].shape[0], *(28, 28, 1))\n", + "X['test'] = X['test'].reshape(X['test'].shape[0], *(28, 28, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__nota__: 28x28x1 ----> immagini 28x28 con 1 canale cromatico (b/n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Train and Evaluate__" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "49/49 [==============================] - 24s 480ms/step - loss: 2.3351 - accuracy: 0.3831 - val_loss: 1.1610 - val_accuracy: 0.6934\n", + "Epoch 2/30\n", + " 7/49 [===>..........................] - ETA: 19s - loss: 1.2259 - accuracy: 0.6562" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Internal Python error in the inspect module.\n", + "Below is the traceback from this internal error.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py\", line 3331, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"\", line 11, in \n", + " train(asl_static_A, X,y, **params)\n", + " File \"\", line 7, in train\n", + " history = model.fit(X_in['train'], y_in['train'], batch_size=batch_size, epochs=epochs, validation_split=0.1, shuffle = True, verbose=1)\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n", + " return fn(*args, **kwargs)\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/keras/engine/training.py\", line 1216, in fit\n", + " tmp_logs = self.train_function(iterator)\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py\", line 150, in error_handler\n", + " return fn(*args, **kwargs)\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\", line 910, in __call__\n", + " result = self._call(*args, **kwds)\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\", line 942, in _call\n", + " return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\", line 3130, in __call__\n", + " return graph_function._call_flat(\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\", line 1959, in _call_flat\n", + " return self._build_call_outputs(self._inference_function.call(\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\", line 598, in call\n", + " outputs = execute.execute(\n", + " File \"/home/agropunx/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\", line 58, in quick_execute\n", + " tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n", + "KeyboardInterrupt\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3/dist-packages/IPython/core/ultratb.py\", line 1148, in get_records\n", + " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", + " File \"/usr/lib/python3/dist-packages/IPython/core/ultratb.py\", line 316, in wrapped\n", + " return f(*args, **kwargs)\n", + " File \"/usr/lib/python3/dist-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n", + " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", + " File \"/usr/lib/python3.8/inspect.py\", line 1515, in getinnerframes\n", + " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", + " File \"/usr/lib/python3.8/inspect.py\", line 1473, in getframeinfo\n", + " filename = getsourcefile(frame) or getfile(frame)\n", + " File \"/usr/lib/python3.8/inspect.py\", line 708, in getsourcefile\n", + " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", + " File \"/usr/lib/python3.8/inspect.py\", line 754, in getmodule\n", + " os.path.realpath(f)] = module.__name__\n", + " File \"/usr/lib/python3.8/posixpath.py\", line 391, in realpath\n", + " path, ok = _joinrealpath(filename[:0], filename, {})\n", + " File \"/usr/lib/python3.8/posixpath.py\", line 425, in _joinrealpath\n", + " if not islink(newpath):\n", + " File \"/usr/lib/python3.8/posixpath.py\", line 167, in islink\n", + " st = os.lstat(path)\n", + "KeyboardInterrupt\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m" + ] + } + ], + "source": [ + "params = {\n", + " 'optimizer': Adam(learning_rate=.001),\n", + " 'loss' : 'sparse_categorical_crossentropy',\n", + " 'metrics' : ['accuracy'],\n", + " 'batch_size' : 512,\n", + " 'epochs' : 30,\n", + " 'verbose': 1, \n", + "}\n", + "\n", + "train(asl_static_A, X,y, **params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "asl_static_A.save(\"models/asl_static_A\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# modello ASL_static_B: 3-layer-CNN con dataset kaggle-AmericanSignLanguage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "il modello è analogo a quello di prima (ASL_static_B) a parte che in questo caso aggiungo altri due canali in ingresso al modello (fondamentalmente prima b/n adesso RGB).\n", + "\n", + "Il dataset invece è molto piu grande rispetto prima, quindi ci aspettiamo buoni risultati con poche epoche (ogni esempio viene visto dal modello tot epoche per training) prima dell'overfitting (momento in cui ri allenare sullo stesso dato diventa ridondante).\n", + "\n", + "riduco quindi a 4 epoche, prima erano 30 per comun regola a casaccio\n", + "\n", + "al dataset vengono aggiunti anche i segni relativi a _spazio_ , _cancella_ e _niente_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dataset source:\n", + "\n", + "American Sign Language (ASL)\n", + "\n", + "url https://www.kaggle.com/grassknoted/asl-alphabet\n", + " \n", + "warnnnnn download ---> size~1GB\n", + "\n", + "messo in ./data/asl\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "train_dir = 'data/asl/asl_alphabet_train/asl_alphabet_train'\n", + "test_dir = 'data/asl/asl_alphabet_test/asl_alphabet_test'\n", + "classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', \n", + " 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', \n", + " 'W', 'X', 'Y', 'Z', 'nothing', 'space', 'del']\n", + "plt.figure(figsize=(11, 11))\n", + "for i in range (0,29):\n", + " plt.subplot(7,7,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " path = train_dir + \"/{0}/{0}1.jpg\".format(classes[i])\n", + " img = plt.imread(path)\n", + " plt.imshow(img)\n", + " plt.xlabel(classes[i])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "size=(32,32)\n", + "\n", + "images = []\n", + "labels = []\n", + "index = -1\n", + "for folder in os.listdir(train_dir):\n", + " index +=1\n", + " for image in os.listdir(train_dir + \"/\" + folder):\n", + " temp_img = cv2.imread(train_dir + '/' + folder + '/' + image)\n", + " temp_img = cv2.resize(temp_img, size)\n", + " images.append(temp_img)\n", + " labels.append(index)\n", + " \n", + "images = np.array(images)\n", + "images = images.astype('float32')/255.0\n", + "labels = tensorflow.keras.utils.to_categorical(labels)\n", + "x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size = 0.1)\n", + " \n", + "print('Loaded', len(x_train),'images for training,','Train data shape =', x_train.shape)\n", + "print('Loaded', len(x_test),'images for testing','Test data shape =', x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_img = keras.Input(shape=(32,32,3))\n", + "\n", + "x = Conv2D(64, (3, 3), padding='same', input_shape=(32, 32, 3), activation='relu')(input_img)\n", + "x = MaxPooling2D(pool_size=(2, 2))(x)\n", + "x = Conv2D(128, (3, 3), padding='same', input_shape=(32, 32, 3), activation='relu')(x)\n", + "x = MaxPooling2D(pool_size=(2, 2))(x)\n", + "\n", + "x = Conv2D(256, (3, 3), padding='same', input_shape=(32, 32, 3), activation='relu')(x)\n", + "x = MaxPooling2D(pool_size=(2, 2))(x)\n", + "x = keras.layers.BatchNormalization()(x)\n", + "\n", + "x = Flatten()(x)\n", + "x = Dropout(0.5)(x)\n", + "x = Dense(1024, activation='sigmoid')(x)\n", + "out = Dense(len(classes), activation='softmax')(x)\n", + "\n", + "asl_static_B = keras.Model(inputs=input_img,outputs=out)\n", + "\n", + "X = {\n", + " 'train':x_train,\n", + " 'test':x_test\n", + "}\n", + "y={\n", + " 'train':y_train,\n", + " 'test':y_test\n", + "}\n", + "params = {\n", + " 'optimizer': Adam(learning_rate=.001),\n", + " 'loss' : 'categorical_crossentropy',\n", + " 'metrics' : ['accuracy'],\n", + " 'batch_size' : 64,\n", + " 'epochs' : 4,\n", + " 'verbose': 1,\n", + " \n", + "}\n", + "\n", + "train(asl_static_B, X,y, **params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "asl_static_B.save('models/asl_static_B')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# modello ASL_static_C: 3-layer-CNN con dataset kaggle-AmericanSignLanguage\n", + "\n", + "dataset source analog al precedente, ma con qualche parola intera in piu rispetto al solo vocabolario composto da alfabeto e numeri, in totale ci sono 51 labels qua\n", + "\n", + "il dataset è un po piu cicciotto quindi lancio il training differentemente, facendo batching degli esempi dalla cartella di origine (non ce la faccio a caricarlo tuttassieme nella ram)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "train_dir='data/asl/sign_digit_fewWords'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ImageDataGenerator' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m data_generator = ImageDataGenerator(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msamplewise_center\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msamplewise_std_normalization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mbrightness_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mzoom_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'ImageDataGenerator' is not defined" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " samplewise_center=True, \n", + " samplewise_std_normalization=True,\n", + " brightness_range=[0.8, 1.0],\n", + " zoom_range=[1.0, 1.2],\n", + " validation_split=0.1\n", + ")\n", + "\n", + "train_generator = data_generator.flow_from_directory(train_dir, target_size=(200,200), shuffle=True, seed=13,\n", + " class_mode='categorical', batch_size=64, subset=\"training\")\n", + "\n", + "validation_generator = data_generator.flow_from_directory(train_dir, target_size=(200, 200), shuffle=True, seed=13,\n", + " class_mode='categorical', batch_size=64, subset=\"validation\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers import BatchNormalization\n", + "from tensorflow.keras import regularizers\n", + "input_img = keras.Input(shape=(200,200,3))\n", + " \n", + "x = Conv2D(64, kernel_size = [3,3], padding = 'same', activation = 'relu', input_shape = (200,200,3))(input_img)\n", + "x = MaxPool2D(pool_size = [3,3])(x)\n", + " \n", + "x = Conv2D(128, kernel_size = [5,5], padding = 'same', activation = 'relu')(x)\n", + "x = MaxPool2D(pool_size = [3,3])(x)\n", + " \n", + "x = Conv2D(256, kernel_size = [3,3], padding = 'same', activation = 'relu')(x)\n", + "x = MaxPool2D(pool_size = [3,3])(x)\n", + " \n", + "x = BatchNormalization()(x)\n", + "x = Flatten()(x)\n", + "x = Dropout(0.5)(x)\n", + " \n", + "x = Dense(1024, activation = 'relu', kernel_regularizer = regularizers.l2(0.001))(x)\n", + "x = Dense(512, activation = 'relu', kernel_regularizer = regularizers.l2(0.001))(x)\n", + "out = Dense(51, activation = 'softmax')(x)\n", + "asl_static_C = keras.Model(inputs=input_img,outputs=out)\n", + "\n", + "asl_static_C.compile(optimizer = 'adam', loss = keras.losses.categorical_crossentropy, metrics = [\"accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_hist = asl_static_C.fit_generator(train_generator,\n", + " validation_data=validation_generator,\n", + " steps_per_epoch=200,\n", + " validation_steps=50,\n", + " epochs=15,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "asl_static_C.save('models/asl_static_C.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ASL_static_D - inception transfer learning\n", + "\n", + "Qua si prende un modello noto di google chiamato __inceptionv3__ https://keras.io/api/applications/inceptionv3/ come punto di partenza per il successivo training del task di classificazione immagini.\n", + "in questo caso si parla di transfer-learning.\n", + "\n", + "il dataset è identico a quello di prima (asl_static_C)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "training_dir=train_dir\n", + "content=sorted(os.listdir(training_dir))\n", + "print(content)\n", + "len(content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "WEIGHTS_FILE = './inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n", + "\n", + "inception_v3_model = keras.applications.inception_v3.InceptionV3(\n", + " input_shape = (200, 200, 3), \n", + " include_top = False, \n", + " weights = 'imagenet'\n", + ")\n", + "\n", + "inception_v3_model.summary()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inception_output_layer = inception_v3_model.get_layer('mixed7')\n", + "print('Inception model output shape:', inception_output_layer.output_shape)\n", + "\n", + "inception_output = inception_v3_model.output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import layers\n", + "x = layers.GlobalAveragePooling2D()(inception_output)\n", + "x = layers.Dense(1024, activation='relu')(x) \n", + "x = layers.Dense(51, activation='softmax')(x) \n", + "\n", + "asl_static_D = Model(inception_v3_model.input, x) \n", + "\n", + "asl_static_D.compile(\n", + " optimizer=SGD(lr=0.0001, momentum=0.9),\n", + " loss='categorical_crossentropy',\n", + " metrics=['acc']\n", + ")\n", + "for layer in model.layers[:249]:\n", + " layer.trainable = False\n", + "for layer in model.layers[249:]:\n", + " layer.trainable = True\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "LOSS_THRESHOLD = 0.2\n", + "ACCURACY_THRESHOLD = 0.95\n", + "\n", + "class ModelCallback(tf.keras.callbacks.Callback):\n", + " def on_epoch_end(self, epoch, logs={}):\n", + " if logs.get('val_loss') <= LOSS_THRESHOLD and logs.get('val_acc') >= ACCURACY_THRESHOLD:\n", + " print(\"\\nReached\", ACCURACY_THRESHOLD * 100, \"accuracy, Stopping!\")\n", + " self.model.stop_training = True\n", + "\n", + "callback = ModelCallback()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "history = asl_static_D.fit_generator(\n", + " train_generator,\n", + " validation_data=validation_generator,\n", + " steps_per_epoch=200,\n", + " validation_steps=50,\n", + " epochs=20,\n", + " callbacks=[callback]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "asl_static_D.save('models/asl_static_D.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acc = history.history['acc']\n", + "val_acc = history.history['val_acc']\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(acc))\n", + "\n", + "plt.plot(epochs, acc, 'r', label='Training accuracy')\n", + "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n", + "plt.title('Training and validation accuracy')\n", + "plt.legend(loc=0)\n", + "plt.figure()\n", + "\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# asl_static_C VS asl_static_D\n", + "\n", + "Considerando lo stesso dataset e labelset, confronto tra il modello C (3Layer-3Channel shallow CNN) e D (transfer learning da modello con milamilioni di paramentri super sblindato di google)\n", + "\n", + "Qual'è meglio?\n", + "\n", + "A che prezzo?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "asl_static_C = keras.models.load_model('models/asl_static_C.h5')\n", + "asl_static_D = keras.models.load_model('models/asl_static_D.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_generator = ImageDataGenerator(\n", + " samplewise_center=True, \n", + " samplewise_std_normalization=True,\n", + " brightness_range=[0.8, 1.0],\n", + " zoom_range=[1.0, 1.2],\n", + " validation_split=0.1\n", + ")\n", + "\n", + "test_generator = data_generator.flow_from_directory(train_dir, target_size=(200, 200), shuffle=False, seed=13,\n", + " class_mode='categorical', batch_size=64, subset=\"validation\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "predictions_C = asl_static_C.predict(test_generator, workers=-1, use_multiprocessing=True, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "predictions_D = asl_static_D.predict(test_generator, workers=-1, use_multiprocessing=True, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert predictions_D.shape==predictions_C.shape\n", + "predictions_D.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!du models/asl_static_C.h5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!du models/asl_static_D.h5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yp_C = np.argmax(predictions_C,axis=1)\n", + "yp_D = np.argmax(predictions_D,axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "confusion_matrix_C = confusion_matrix(test_generator.classes, yp_C)\n", + "confusion_matrix_D = confusion_matrix(test_generator.classes, yp_D)\n", + "\n", + "fig,axs = plt.subplots(1,2,figsize=(20,10))\n", + "sns.heatmap(confusion_matrix_C,ax=axs[0])\n", + "axs[0].set_title('asl_static_C')\n", + "sns.heatmap(confusion_matrix_D,ax=axs[1])\n", + "axs[1].set_title('asl_static_D - tf inception')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "piu stai sulla diagonale meglio è (considerate le stesse 51 classi, l'asse delle y corrisponde ai valori predetti, l'asse delle x invece ai valori attuali per i sample di test (ground_truth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mapx={v:k for k,v in test_generator.class_indices.items()}\n", + "upto=5\n", + "print(\"asl_static_C prediction for 5 samples of '1' gesture samples:\")\n", + "print([mapx[xx] for xx in yp_C[:5]])\n", + "print('\\nsame for model asl_static_D:')\n", + "print([mapx[xx] for xx in yp_D[:5]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('these were the original photos')\n", + "fig,axs = plt.subplots(1,5,figsize=(50,10))\n", + "for filename,ax in zip(test_generator.filenames[:5],axs.flatten()):\n", + " img = cv2.imread(f'{train_dir}/{filename}')\n", + " ax.imshow(img)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__conclusione__\n", + "\n", + "asl_static_D è il vincitore, ha 4 volte piu parametri (che in scala sono comunque molti anche per asl_static_C ~5'000'000) ma è molto piu sicuro." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/colemano/train/ASL/static/static_detection.pdf b/colemano/train/ASL/static/static_detection.pdf new file mode 100644 index 0000000..9200ee1 Binary files /dev/null and b/colemano/train/ASL/static/static_detection.pdf differ diff --git a/geografia_research.yaml b/geografia_research.yaml new file mode 100644 index 0000000..dcd5710 --- /dev/null +++ b/geografia_research.yaml @@ -0,0 +1,17 @@ +name: geografia_research + +dependencies: + - pip + - python=3.8 + - numpy + - matplotlib + - ipykernel + - pytorch + - transformers + - torchvision + - cudatoolkit=10.2 + - pip: + - imutils==0.5.4 + - spacy==3.3.0 + - opencv-python==4.2.0.32 + - tensorflow==2.8.0 diff --git a/setup.sh b/setup.sh new file mode 100755 index 0000000..b7bb748 --- /dev/null +++ b/setup.sh @@ -0,0 +1,28 @@ +#!/usr/bin/env bash +set -euo pipefail + +# to install anaconda +# https://docs.anaconda.com/anaconda/install/linux/ + +source /home/agropunx/anaconda3/etc/profile.d/conda.sh +conda create -y --name geografia_research python=3.8 pip +conda activate geografia + +# conda env +conda install -c anaconda ipykernel +python -m ipykernel install --user --name=geografia_research #-f ./geografia_research.yml + +# general +pip install flask flask_bootstrap numpy pandas scipy +pip install protobuf==3.20 + + +# colemano +pip install imutils==0.5.4 +pip install tensorflow==2.8.0 +pip install opencv-python==4.2.0.32 + +# ccq +pip install transformers==4.14.1 +conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch # cudatoolkit not present in pip +pip install spacy==3.3.0