multi-modal_concatenate_fusion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import os, time, datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "import random\n",
    "import logging\n",
    "tqdm.pandas()\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#NN Packages\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, random_split,DataLoader, RandomSampler, SequentialSampler\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():    \n",
    "\n",
    "    # Tell PyTorch to use the GPU.    \n",
    "    device = torch.device(\"cuda\")\n",
    "\n",
    "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
    "\n",
    "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
    "\n",
    "# If not...\n",
    "else:\n",
    "    print('No GPU available, using the CPU instead.')\n",
    "    device = torch.device(\"cpu\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_time(elapsed):\n",
    "    '''\n",
    "    Takes a time in seconds and returns a string hh:mm:ss\n",
    "    '''\n",
    "    # Round to the nearest second.\n",
    "    elapsed_rounded = int(round((elapsed)))\n",
    "    \n",
    "    # Format as hh:mm:ss\n",
    "    return str(datetime.timedelta(seconds=elapsed_rounded))\n",
    "\n",
    "class SigirPreprocess():\n",
    "    \n",
    "    def __init__(self, text_data_path):\n",
    "        self.text_data_path = text_data_path\n",
    "        self.train = None\n",
    "        self.dict_code_to_id = {}\n",
    "        self.dict_id_to_code = {}\n",
    "        self.list_tags = {}\n",
    "        self.sentences = []\n",
    "        self.labels = []\n",
    "        self.text_col = None\n",
    "        self.X_test = None\n",
    "    def prepare_data(self ):\n",
    "        catalog_eng= pd.read_csv(self.text_data_path+\"data/catalog_english_taxonomy.tsv\",sep=\"\\t\")\n",
    "        X_train= pd.read_csv(self.text_data_path+\"data/X_train.tsv\",sep=\"\\t\")\n",
    "        Y_train= pd.read_csv(self.text_data_path+\"data/Y_train.tsv\",sep=\"\\t\")\n",
    "        \n",
    "        self.list_tags = list(Y_train['Prdtypecode'].unique())\n",
    "        for i,tag in enumerate(self.list_tags):\n",
    "            self.dict_code_to_id[tag] = i \n",
    "            self.dict_id_to_code[i]=tag\n",
    "        print(self.dict_code_to_id)\n",
    "            \n",
    "        Y_train['labels']=Y_train['Prdtypecode'].map(self.dict_code_to_id)\n",
    "        train=pd.merge(left=X_train,right=Y_train,\n",
    "               how='left',left_on=['Integer_id','Image_id','Product_id'],\n",
    "               right_on=['Integer_id','Image_id','Product_id'])\n",
    "        prod_map=pd.Series(catalog_eng['Top level category'].values,\n",
    "                           index=catalog_eng['Prdtypecode']).to_dict()\n",
    "\n",
    "        train['product'] = train['Prdtypecode'].map(prod_map)\n",
    "        train['title_len']=train['Title'].progress_apply(lambda x : len(x.split()) if pd.notna(x) else 0)\n",
    "        train['desc_len']=train['Description'].progress_apply(lambda x : len(x.split()) if pd.notna(x) else 0)\n",
    "        train['title_desc_len']=train['title_len'] + train['desc_len']\n",
    "        train.loc[train['Description'].isnull(), 'Description'] = \" \"\n",
    "        train['title_desc'] = train['Title'] + \" \" + train['Description']\n",
    "        \n",
    "        self.train = train\n",
    "        \n",
    "    def get_sentences(self, text_col, remove_null_rows=False):\n",
    "        self.text_col = text_col\n",
    "        if remove_null_rows==True:\n",
    "            new_train = self.train[self.train[text_col].notnull()]\n",
    "\n",
    "        else:\n",
    "            new_train = self.train.copy()\n",
    "            \n",
    "        self.sentences = new_train[text_col].values\n",
    "        self.labels = new_train['labels'].values\n",
    "    \n",
    "    def prepare_test(self, text_col):\n",
    "        X_test=pd.read_csv(self.text_data_path+\"data/x_test_task1_phase1.tsv\",sep=\"\\t\")\n",
    "        X_test.loc[X_test['Description'].isnull(), 'Description'] = \" \"\n",
    "        X_test['title_desc'] = X_test['Title'] + \" \" + X_test['Description']\n",
    "        self.X_test = X_test\n",
    "        self.test_sentences = X_test[text_col].values\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_col = 'title_desc'\n",
    "max_len = 256\n",
    "val_size = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Preprocess = SigirPreprocess(\"/kaggle/input/textphase1/\")\n",
    "Preprocess.prepare_data()\n",
    "Preprocess.get_sentences(text_col, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = Preprocess.sentences\n",
    "labels = Preprocess.labels\n",
    "print(\"Total number of sentences:{}, labels:{}\".format(len(sentences), len(labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to prepare input for model training\n",
    "def prep_input(sentences,labels, max_len,tokenizer):\n",
    "    input_ids = []\n",
    "    attention_masks = []\n",
    "\n",
    "    # For every sentence...\n",
    "    for sent in tqdm(sentences):\n",
    "        # `encode_plus` will:\n",
    "        #   (1) Tokenize the sentence.\n",
    "        #   (2) Prepend the `[CLS]` token to the start.\n",
    "        #   (3) Append the `[SEP]` token to the end.\n",
    "        #   (4) Map tokens to their IDs.\n",
    "        #   (5) Pad or truncate the sentence to `max_length`\n",
    "        #   (6) Create attention masks for [PAD] tokens.\n",
    "        encoded_dict = tokenizer.encode_plus(\n",
    "                            sent,                      # Sentence to encode.\n",
    "                            add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
    "                            max_length = max_len,           # Pad & truncate all sentences.\n",
    "                            pad_to_max_length = True,\n",
    "                            return_attention_mask = True,   # Construct attn. masks.\n",
    "                            return_tensors = 'pt',     # Return pytorch tensors.\n",
    "                       )\n",
    "\n",
    "        # Add the encoded sentence to the list.    \n",
    "        input_ids.append(encoded_dict['input_ids'])\n",
    "\n",
    "        # And its attention mask (simply differentiates padding from non-padding).\n",
    "        attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "    # Convert the lists into tensors.\n",
    "    input_ids = torch.cat(input_ids, dim=0)\n",
    "    attention_masks = torch.cat(attention_masks, dim=0)\n",
    "    if labels is not None:\n",
    "        labels = torch.tensor(labels)\n",
    "        return input_ids,attention_masks,labels\n",
    "    else:\n",
    "        return input_ids,attention_masks\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_input='../input/multi-modal-input-text/'\n",
    "tr_inputs_cam=torch.load(text_input+\"tr_inputs_cam.pt\")\n",
    "val_inputs_cam=torch.load(text_input+\"val_inputs_cam.pt\")\n",
    "tr_masks_cam=torch.load( text_input+\"tr_masks_cam.pt\")\n",
    "val_masks_cam=torch.load( text_input+\"val_masks_cam.pt\")\n",
    "\n",
    "tr_inputs_flau=torch.load(text_input+\"tr_inputs_flau.pt\")\n",
    "val_inputs_flau=torch.load(text_input+\"val_inputs_flau.pt\")\n",
    "tr_masks_flau=torch.load(text_input+\"tr_masks_flau.pt\")\n",
    "val_masks_flau=torch.load(text_input+\"val_masks_flau.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pretrainedmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import CamembertConfig, CamembertTokenizer, CamembertModel, CamembertForSequenceClassification, AdamW\n",
    "from transformers import FlaubertModel, FlaubertTokenizer,FlaubertForSequenceClassification,AdamW, FlaubertConfig \n",
    "from transformers.modeling_roberta import RobertaClassificationHead\n",
    "from transformers.modeling_utils import SequenceSummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import functional as F\n",
    "import torch.nn as nn\n",
    "import pretrainedmodels\n",
    "class SEResnext50_32x4d(nn.Module):\n",
    "    def __init__(self, pretrained='imagenet'):\n",
    "        super(SEResnext50_32x4d, self).__init__()\n",
    "        \n",
    "        self.base_model = pretrainedmodels.__dict__[\"se_resnext50_32x4d\"](pretrained=None)\n",
    "        if pretrained is not None:\n",
    "            self.base_model.load_state_dict(\n",
    "                torch.load(\"../input/pretrained-model-weights-pytorch/se_resnext50_32x4d-a260b3a4.pth\"\n",
    "                )\n",
    "            )\n",
    "        self.l0 = nn.Linear(2048, 27)\n",
    "    \n",
    "    def forward(self, image):\n",
    "        batch_size, _, _, _ = image.shape\n",
    "        \n",
    "        x = self.base_model.features(image)\n",
    "        x = F.adaptive_avg_pool2d(x, 1).reshape(batch_size, -1)\n",
    "        \n",
    "        out = self.l0(x)\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Identity(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Identity, self).__init__()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class vec_output_CamembertForSequenceClassification(CamembertModel):\n",
    "    config_class = CamembertConfig\n",
    "\n",
    "    def __init__(self, config):\n",
    "        super().__init__(config)\n",
    "        self.num_labels = config.num_labels\n",
    "\n",
    "        self.roberta = CamembertModel(config)\n",
    "        self.dense = nn.Linear(256*config.hidden_size, config.hidden_size)\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)\n",
    "        self.init_weights()\n",
    "\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        input_ids=None,\n",
    "        attention_mask=None,\n",
    "        token_type_ids=None,\n",
    "        position_ids=None,\n",
    "        head_mask=None,\n",
    "        inputs_embeds=None,\n",
    "        labels=None,\n",
    "        output_attentions=None,\n",
    "        output_hidden_states=None,\n",
    "    ):\n",
    "        outputs = self.roberta(\n",
    "            input_ids,\n",
    
    "            attention_mask=attention_mask,\n",
    "            token_type_ids=token_type_ids,\n",
    "            position_ids=position_ids,\n",
    "            head_mask=head_mask,\n",
    "            inputs_embeds=inputs_embeds,\n",
    "#             output_attentions=output_attentions,\n",
    "#             output_hidden_states=output_hidden_states,\n",
    "        )\n",
    "        sequence_output = outputs[0] #(B,256,768)\n",
    "        x = sequence_output.view(sequence_output.shape[0], 256*768)\n",
    "#         x = sequence_output[:, 0, :]  # take <s> token (equiv. to [CLS])-> #(B,768) Image -> (B,2048)\n",
    "        x = self.dense(x)  # 768 -> 768\n",
    "        feat= torch.tanh(x) \n",
    "        logits = self.out_proj(feat) # 768 -> 27\n",
    "        outputs = (logits,) + outputs[2:]\n",
    "\n",
    "        return outputs,feat  # (loss), logits, (hidden_states), (attentions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 27\n",
    "\n",
    "class vec_output_FlaubertForSequenceClassification(FlaubertModel):\n",
    "    \n",
    "    config_class = FlaubertConfig\n",
    "    \n",
    "\n",
    "    def __init__(self, config):\n",
    "        super().__init__(config)\n",
    "        self.transformer = FlaubertModel(config)\n",
    "        self.sequence_summary = SequenceSummary(config)\n",
    "        self.init_weights()\n",
    "        self.dropout =  torch.nn.Dropout(0.1)\n",
    "        self.classifier = torch.nn.Linear(config.hidden_size, num_classes)\n",
    "\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        input_ids=None,\n",
    "        attention_mask=None,\n",
    "        langs=None,\n",
    "        token_type_ids=None,\n",
    "        position_ids=None,\n",
    "        lengths=None,\n",
    "        cache=None,\n",
    "        head_mask=None,\n",
    "        inputs_embeds=None,\n",
    "        labels=None,\n",
    "    ):\n",
    "        \n",
    "        \n",
    "        transformer_outputs = self.transformer(\n",
    "            input_ids,\n",
    "            attention_mask=attention_mask,\n",
    "            langs=langs,\n",
    "            token_type_ids=token_type_ids,\n",
    "            position_ids=position_ids,\n",
    "            lengths=lengths,\n",
    "            cache=cache,\n",
    "            head_mask=head_mask,\n",
    "            inputs_embeds=inputs_embeds,\n",
    "        )\n",
    "\n",
    "        #output = self.dropout(output)\n",
    "        output = transformer_outputs[0]\n",
    "        vec = output[:,0]\n",
    "        \n",
    "        \n",
    "        #logits\n",
    "        dense = self.dropout(vec)\n",
    "        \n",
    "        #classifier\n",
    "        logits = self.classifier(dense)\n",
    "        \n",
    "        outputs = (logits,) + transformer_outputs[1:]  # Keep new_mems and attention/hidden states if they are here\n",
    "       \n",
    "        \n",
    "        return outputs,dense\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog_eng= pd.read_csv(\"/kaggle/input/textphase1/data/catalog_english_taxonomy.tsv\",sep=\"\\t\")\n",
    "X_train= pd.read_csv(\"/kaggle/input/textphase1/data/X_train.tsv\",sep=\"\\t\")\n",
    "Y_train= pd.read_csv(\"/kaggle/input/textphase1/data/Y_train.tsv\",sep=\"\\t\")\n",
    "X_test=pd.read_csv(\"/kaggle/input/textphase1/data/x_test_task1_phase1.tsv\",sep=\"\\t\")\n",
    "dict_code_to_id = {}\n",
    "dict_id_to_code={}\n",
    "list_tags = list(Y_train['Prdtypecode'].unique())\n",
    "\n",
    "for i,tag in enumerate(list_tags):\n",
    "    dict_code_to_id[tag] = i \n",
    "    dict_id_to_code[i]=tag\n",
    "Y_train['labels']=Y_train['Prdtypecode'].map(dict_code_to_id)\n",
    "train=pd.merge(left=X_train,right=Y_train,\n",
    "               how='left',left_on=['Integer_id','Image_id','Product_id'],\n",
    "               right_on=['Integer_id','Image_id','Product_id'])\n",
    "prod_map=pd.Series(catalog_eng['Top level category'].values,index=catalog_eng['Prdtypecode']).to_dict()\n",
    "train['product']=train['Prdtypecode'].map(prod_map)\n",
    "\n",
    "def get_img_path(img_id,prd_id,path):\n",
    "    \n",
    "    pattern = 'image'+'_'+str(img_id)+'_'+'product'+'_'+str(prd_id)+'.jpg'\n",
    "    return path + pattern\n",
    "train_img = train[['Image_id','Product_id','labels','product']]\n",
    "\n",
    "train_img['image_path']=train_img.progress_apply(lambda x: get_img_path(x['Image_id'],x['Product_id'],\n",
    "                                                                path = '/kaggle/input/imagetrain/image_training/'),axis=1)\n",
    "X_test['image_path']=X_test.progress_apply(lambda x: get_img_path(x['Image_id'],x['Product_id'],\n",
    "                                                    path='/kaggle/input/imagetest/image_test/image_test_task1_phase1/'),axis=1)\n",
    "train_df, val_df, _, _ = train_test_split(train_img, train_img['labels'],random_state=2020, test_size = 0.1, stratify=train_img['labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 224 # for Resnt\n",
    "# Applying Transforms to the Data\n",
    "from torchvision import datasets, models, transforms\n",
    "\n",
    "image_transforms = { \n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),\n",
    "        transforms.RandomRotation(degrees=15),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.Resize(size=256),\n",
    "        transforms.CenterCrop(size=input_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                             [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'valid': transforms.Compose([\n",
    "        transforms.Resize(size=256),\n",
    "        transforms.CenterCrop(size=input_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                             [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.Resize(size=256),\n",
    "        transforms.CenterCrop(size=input_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                             [0.229, 0.224, 0.225])\n",
    "    ])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader, Subset\n",
    "import cv2\n",
    "from PIL import Image\n",
    "\n",
    "class FusionDataset(Dataset):\n",
    "    \n",
    "    def __init__(self,df,inputs_cam,masks_cam,inputs_flau,masks_flau,transform=None,mode='train'):\n",
    "        self.df = df\n",
    "        self.transform=transform\n",
    "        self.mode=mode\n",
    "        self.inputs_cam=inputs_cam\n",
    "        self.masks_cam=masks_cam\n",
    "        self.inputs_flau=inputs_flau\n",
    "        self.masks_flau=masks_flau\n",
    "         \n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    \n",
    "    def __getitem__(self,idx):\n",
    "        \n",
    "        im_path = self.df.iloc[idx]['image_path']\n",
    "        img = cv2.imread(im_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        img=Image.fromarray(img)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        img=img.cuda()\n",
    "        input_id_cam=self.inputs_cam[idx].cuda()\n",
    "        input_mask_cam=self.masks_cam[idx].cuda()\n",
    "        input_id_flau=self.inputs_flau[idx].cuda()\n",
    "        input_mask_flau=self.masks_flau[idx].cuda()\n",
    "        \n",
    "        if self.mode=='test':\n",
    "            return img,input_id_cam,input_mask_cam,input_id_flau,input_mask_flau\n",
    "        else:\n",
    "#             labels = torch.tensor(self.df.iloc[idx]['labels'])\n",
    "            labels = torch.tensor(self.df.iloc[idx]['labels']).cuda()             \n",
    "\n",
    "            return img,input_id_cam,input_mask_cam,input_id_flau,input_mask_flau,labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = torch.randn(3,10,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_dim=nn.Conv1d(in_channels = 10 , out_channels = 1 , kernel_size= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_dim(a1).view(3,10).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class vector_fusion(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(vector_fusion, self).__init__()\n",
    "        self.img_model = SEResnext50_32x4d(pretrained=None)\n",
    "        self.img_model.load_state_dict(torch.load('../input/seresnext2048/best_model.pt'))\n",
    "        self.img_model.l0=Identity()\n",
    "        for params in self.img_model.parameters():\n",
    "            params.requires_grad=False\n",
    "\n",
    "        self.cam_model= vec_output_CamembertForSequenceClassification.from_pretrained(\n",
    "    'camembert-base', # Use the 12-layer BERT model, with an uncased vocab.\n",
    "    num_labels = len(Preprocess.dict_code_to_id), # The number of output labels--2 for binary classification.\n",
    "                    # You can increase this for multi-class tasks.   \n",
    "    output_attentions = False, # Whether the model returns attentions weights.\n",
    "    output_hidden_states = False,) # Whether the model returns all hidden-states.\n",
    "        \n",
    "        \n",
    "        cam_model_path = '../input/camembert-vec-256m768-10ep/best_model.pt'\n",
    "        checkpoint = torch.load(cam_model_path)\n",
    "        # model = checkpoint['model']\n",
    "        self.cam_model.load_state_dict(checkpoint)\n",
    "        for param in self.cam_model.parameters():\n",
    "            param.requires_grad=False\n",
    "        self.cam_model.out_proj=Identity()\n",
    "        \n",
    "        self.flau_model=vec_output_FlaubertForSequenceClassification.from_pretrained(\n",
    "        'flaubert/flaubert_base_cased', \n",
    "        num_labels = len(Preprocess.dict_code_to_id), \n",
    "        output_attentions = False,\n",
    "        output_hidden_states = False,)\n",
    "        flau_model_path='../input/flaubert-8933/best_model.pt'\n",
    "        checkpoint = torch.load(flau_model_path)\n",
    "        self.flau_model.load_state_dict(checkpoint)\n",
    "        for param in self.flau_model.parameters():\n",
    "            param.requires_grad=False\n",
    "        self.flau_model.classifier=Identity()\n",
    "        \n",
    "\n",
    "        self.reduce_dim=nn.Conv1d(in_channels = 2048 , out_channels = 768 , kernel_size= 1)\n",
    "        self.reduce_dim2=nn.Conv1d(in_channels = 768 , out_channels = 1 , kernel_size= 1)\n",
    "        self.out=nn.Linear(768*3, 27)\n",
    "        \n",
    "        #gamma\n",
    "#         self.w1 = nn.Parameter(torch.zeros(1))\n",
    "#         self.w2 = nn.Parameter(torch.zeros(1))\n",
    "#         self.w3 = nn.Parameter(torch.zeros(1))\n",
    "        \n",
    "    def forward(self,img,input_id_cam,input_mask_cam,input_id_flau,input_mask_flau):\n",
    "        \n",
    "        cam_emb,vec1 =self.cam_model(input_id_cam, \n",
    "                     token_type_ids=None, \n",
    "                     attention_mask=input_mask_cam)\n",
    "        flau_emb,vec2 =self.flau_model(input_id_flau, \n",
    "                     token_type_ids=None, \n",
    "                     attention_mask=input_mask_flau)\n",
    "        \n",
    "        #Projecting the image embedding to lower dimension\n",
    "        img_emb=self.img_model(img)\n",
    "        img_emb=img_emb.view(img_emb.shape[0],img_emb.shape[1],1)\n",
    "        img_emb=self.reduce_dim(img_emb)\n",
    "        img_emb=img_emb.view(img_emb.shape[0],img_emb.shape[1]) ###### bs * 768 \n",
    "        \n",
    "        #summing up the vectors\n",
    "        #text_emb = cam_emb[0] + flau_emb[0]\n",
    "        \n",
    "        #Bilinear\n",
    "        #text_emb = text_emb.view(text_emb.shape[0],1,text_emb.shape[1])  ##### bs * 1 * 768\n",
    "        \n",
    "        #Bilinear Pooling\n",
    "        #pool_emb = torch.bmm(img_emb,text_emb) ### bs * 768 * 768\n",
    "        #pool_emb = self.reduce_dim2(pool_emb).view(text_emb.shape[0],768)  #### bs * 1 * 768\n",
    "        fuse= torch.cat([img_emb,cam_emb[0],flau_emb[0]],axis=1)\n",
    "        \n",
    "        logits=self.out(fuse)\n",
    "        return logits\n",
    "        \n",
    "        \n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=vector_fusion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=FusionDataset(train_df,tr_inputs_cam,tr_masks_cam,tr_inputs_flau,tr_masks_flau,transform=image_transforms['test'])\n",
    "val_dataset=FusionDataset(val_df,val_inputs_cam,val_masks_cam,val_inputs_flau,val_masks_flau,transform=image_transforms['test'])\n",
    "# test_dataset=FusionDataset(X_test,test_inputs,test_makss,transform=image_transforms['test'],mode='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=64\n",
    "train_dataloader=DataLoader(train_dataset,batch_size=batch_size,shuffle=True)\n",
    "validation_dataloader=DataLoader(val_dataset,batch_size=batch_size,shuffle=False)\n",
    "# test_data=DataLoader(test_dataset,batch_size=batch_size,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = AdamW(model.parameters(),\n",
    "                  lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
    "                  eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "count_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import get_linear_schedule_with_warmup\n",
    "\n",
    "# Number of training epochs. The BERT authors recommend between 2 and 4. \n",
    "# We chose to run for 4, but we'll see later that this may be over-fitting the\n",
    "# training data.\n",
    "epochs = 3\n",
    "\n",
    "# Total number of training steps is [number of batches] x [number of epochs]. \n",
    "# (Note that this is not the same as the number of training samples).\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "# Create the learning rate scheduler.\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
    "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
    "                                            num_training_steps = total_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "loss_criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flat_accuracy(preds, labels):\n",
    "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "seed_val = 42\n",
    "\n",
    "random.seed(seed_val)\n",
    "np.random.seed(seed_val)\n",
    "torch.manual_seed(seed_val)\n",
    "torch.cuda.manual_seed_all(seed_val)\n",
    "\n",
    "# We'll store a number of quantities such as training and validation loss, \n",
    "# validation accuracy, and timings.\n",
    "training_stats = []\n",
    "\n",
    "# Measure the total training time for the whole run.\n",
    "total_t0 = time.time()\n",
    "\n",
    "\n",
    "# For each epoch...\n",
    "for epoch_i in range(0, epochs):\n",
    "    \n",
    "    # ========================================\n",
    "    #               Training\n",
    "    # ========================================\n",
    "    \n",
    "    # Perform one full pass over the training set.\n",
    "\n",
    "    print(\"\")\n",
    "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
    "    print('Training...')\n",
    "    \n",
    "    #tr and val\n",
    "#     vec_output_tr = []\n",
    "#     vec_output_val =[]\n",
    "\n",
    "    # Measure how long the training epoch takes.\n",
    "    t0 = time.time()\n",
    "\n",
    "    # Reset the total loss for this epoch.\n",
    "    total_train_loss = 0\n",
    "\n",
    "    # Put the model into training mode. Don't be mislead--the call to \n",
    "    # `train` just changes the *mode*, it doesn't *perform* the training.\n",
    "    # `dropout` and `batchnorm` layers behave differently during training\n",
    "    # vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)\n",
    "    best_f1 = 0\n",
    "    model.train()\n",
    "\n",
    "    # For each batch of training data...\n",
    "    for step, batch in tqdm(enumerate(train_dataloader)):\n",
    "        \n",
    "        # Unpack this training batch from our dataloader. \n",
    "        #\n",
    "        \n",
    "        # As we unpack the batch, we'll also copy each tensor to the GPU using the \n",
    "        # `to` method.\n",
    "        #\n",
    "        # `batch` contains three pytorch tensors:\n",
    "        #   [0]: input ids \n",
    "        #   [1]: attention masks\n",
    "        #   [2]: labels \n",
    "#         return img,input_id_cam,input_mask_cam,input_id_flau,input_mask_flau\n",
    "\n",
    "        b_img=batch[0].to(device)\n",
    "\n",
    "        b_input_id_cam = batch[1].to(device)\n",
    "        b_input_mask_cam = batch[2].to(device)\n",
    "        b_input_id_flau = batch[3].to(device)\n",
    "        b_input_mask_flau = batch[4].to(device)\n",
    "\n",
    "        b_labels = batch[5].to(device)\n",
    "        \n",
    "        \n",
    "        model.zero_grad()        \n",
    "\n",
    "        \n",
    "        logits = model(b_img,b_input_id_cam ,b_input_mask_cam,b_input_id_flau,b_input_mask_flau)\n",
    "                            \n",
    "        #Defining the loss\n",
    "        loss = loss_criterion(logits, b_labels)\n",
    "        \n",
    "        #saving the features_tr\n",
    "#         vec = vec.detach().cpu().numpy()\n",
    "#         vec_output_tr.extend(vec)\n",
    "        \n",
    "        # Accumulate the training loss over all of the batches so that we can\n",
    "        # calculate the average loss at the end. `loss` is a Tensor containing a\n",
    "        # single value; the `.item()` function just returns the Python value \n",
    "        # from the tensor.\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "        # Perform a backward pass to calculate the gradients.\n",
    "        loss.backward()\n",
    "\n",
    "        # Clip the norm of the gradients to 1.0.\n",
    "        # This is to help prevent the \"exploding gradients\" problem.\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "        # Update parameters and take a step using the computed gradient.\n",
    "        # The optimizer dictates the \"update rule\"--how the parameters are\n",
    "        # modified based on their gradients, the learning rate, etc.\n",
    "        optimizer.step()\n",
    "\n",
    "        # Update the learning rate.\n",
    "        scheduler.step()\n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)            \n",
    "    \n",
    "    # Measure how long this epoch took.\n",
    "    training_time = format_time(time.time() - t0)\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"  Average training loss: {0:.2f} \".format(avg_train_loss))\n",
    "    print(\"  Training epcoh took: {:} \".format(training_time))\n",
    "        \n",
    "    # ========================================\n",
    "    #               Validation\n",
    "    # ========================================\n",
    "    # After the completion of each training epoch, measure our performance on\n",
    "    # our validation set.\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"Running Validation...\")\n",
    "\n",
    "    t0 = time.time()\n",
    "\n",
    "    # Put the model in evaluation mode--the dropout layers behave differently\n",
    "    # during evaluation.\n",
    "    model.eval()\n",
    "\n",
    "    # Tracking variables \n",
    "    total_eval_accuracy = 0\n",
    "    total_eval_loss = 0\n",
    "    nb_eval_steps = 0\n",
    "    predictions=[]\n",
    "    true_labels=[]\n",
    "    \n",
    "\n",
    "    # Evaluate data for one epoch\n",
    "    for batch in tqdm(validation_dataloader):\n",
    "        \n",
    "        # Unpack this training batch from our dataloader. \n",
    "        #\n",
    "        # As we unpack the batch, we'll also copy each tensor to the GPU using \n",
    "        # the `to` method.\n",
    "        #\n",
    "        # `batch` contains three pytorch tensors:\n",
    "        #   [0]: input ids \n",
    "        #   [1]: attention masks\n",
    "        #   [2]: labels \n",
    "        b_img=batch[0].to(device)\n",
    "\n",
    "        b_input_id_cam = batch[1].to(device)\n",
    "        b_input_mask_cam = batch[2].to(device)\n",
    "        b_input_id_flau = batch[3].to(device)\n",
    "        b_input_mask_flau = batch[4].to(device)\n",
    "\n",
    "        b_labels = batch[5].to(device)\n",
    "        \n",
    "        \n",
    "        # Tell pytorch not to bother with constructing the compute graph during\n",
    "        # the forward pass, since this is only needed for backprop (training).\n",
    "        with torch.no_grad():       \n",
    "        \n",
    "\n",
    "            # Forward pass, calculate logit predictions.\n",
    "            # token_type_ids is the same as the \"segment ids\", which \n",
    "            # differentiates sentence 1 and 2 in 2-sentence tasks.\n",
    "            # The documentation for this `model` function is here: \n",
    "            # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
    "            # Get the \"logits\" output by the model. The \"logits\" are the output\n",
    "            # values prior to applying an activation function like the softmax.\n",
    "            logits = model(b_img,b_input_id_cam ,b_input_mask_cam,b_input_id_flau,b_input_mask_flau)\n",
    "            \n",
    "        #new\n",
    "        \n",
    "        #defining the val loss\n",
    "        loss = loss_criterion(logits, b_labels)\n",
    "        \n",
    "        \n",
    "        # Accumulate the validation loss.\n",
    "        total_eval_loss += loss.item()\n",
    "\n",
    "        # Move logits and labels to CPU\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "\n",
    "        # Move logits and labels to CPU\n",
    "        predicted_labels=np.argmax(logits,axis=1)\n",
    "        predictions.extend(predicted_labels)\n",
    "        label_ids = b_labels.to('cpu').numpy()\n",
    "        true_labels.extend(label_ids)\n",
    "        \n",
    "        #saving the features_tr\n",
    "#         vec = vec.detach().cpu().numpy()\n",
    "#         vec_output_val.extend(vec)\n",
    "        \n",
    "\n",
    "        # Calculate the accuracy for this batch of test sentences, and\n",
    "        # accumulate it over all batches.\n",
    "        total_eval_accuracy += flat_accuracy(logits, label_ids)\n",
    "        \n",
    "\n",
    "    # Report the final accuracy for this validation run.\n",
    "    avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
    "    print(\"  Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
    "\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
    "    \n",
    "    # Measure how long the validation run took.\n",
    "    validation_time = format_time(time.time() - t0)\n",
    "    \n",
    "    print(\"  Validation Loss: {0:.2f}\".format(avg_val_loss))\n",
    "    print(\"  Validation took: {:}\".format(validation_time))\n",
    "    print(\"Validation F1-Score: {}\".format(f1_score(true_labels,predictions,average='macro')))\n",
    "    curr_f1=f1_score(true_labels,predictions,average='macro')\n",
    "    if curr_f1 > best_f1:\n",
    "        best_f1=curr_f1\n",
    "        torch.save(model.state_dict(), 'best_model.pt')\n",
    "#         np.save('best_vec_train_model_train.npy',vec_output_tr)\n",
    "#         np.save('best_vec_val.npy',vec_output_val)\n",
    "        \n",
    "    # Record all statistics from this epoch.\n",
    "#     training_stats.append(\n",
    "#         {\n",
    "#             'epoch': epoch_i + 1,\n",
    "#             'Training Loss': avg_train_loss,\n",
    "#             'Valid. Loss': avg_val_loss,\n",
    "#             'Valid. Accur.': avg_val_accuracy,\n",
    "#             'Training Time': training_time,\n",
    "#             'Validation Time': validation_time\n",
    "#         }\n",
    "#     )\n",
    "\n",
    "print(\"\")\n",
    "print(\"Training complete!\")\n",
    "\n",
    "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation F1-Score: 0.9093939540881769\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(\"Validation F1-Score: {}\".format(f1_score(true_labels,predictions,average='macro')))"
   ]
  }
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