{"cells":[{"cell_type":"markdown","metadata":{},"source":["Note: This code is run on Kaggle environment."]},{"cell_type":"markdown","metadata":{},"source":["# Importing the required libraries"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:20:55.138911Z","iopub.status.busy":"2023-12-25T09:20:55.138540Z","iopub.status.idle":"2023-12-25T09:21:13.497198Z","shell.execute_reply":"2023-12-25T09:21:13.496367Z","shell.execute_reply.started":"2023-12-25T09:20:55.138880Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/torchvision/datapoints/__init__.py:12: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().\n","  warnings.warn(_BETA_TRANSFORMS_WARNING)\n","/opt/conda/lib/python3.10/site-packages/torchvision/transforms/v2/__init__.py:54: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().\n","  warnings.warn(_BETA_TRANSFORMS_WARNING)\n","/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n","  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"]}],"source":["import torch\n","import pandas as pd\n","import numpy as np\n","import os\n","import warnings\n","import matplotlib.pyplot as plt\n","\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification, DistilBertForSequenceClassification, AutoModelForSeq2SeqLM\n","from tqdm import tqdm\n","from torchvision import models\n","from torchvision.transforms import v2\n","from torch.utils.data import Dataset, DataLoader\n","from keras.preprocessing import image\n","from torchmetrics.classification import MultilabelF1Score\n","from sklearn.metrics import average_precision_score, ndcg_score"]},{"cell_type":"markdown","metadata":{},"source":["### Setting up the environment\n","***"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:13.499630Z","iopub.status.busy":"2023-12-25T09:21:13.499089Z","iopub.status.idle":"2023-12-25T09:21:13.503998Z","shell.execute_reply":"2023-12-25T09:21:13.502923Z","shell.execute_reply.started":"2023-12-25T09:21:13.499602Z"},"trusted":true},"outputs":[],"source":["warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["***"]},{"cell_type":"markdown","metadata":{},"source":["# Data Preprocessing"]},{"cell_type":"markdown","metadata":{},"source":["18 Genres from file genres.txt"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:13.505808Z","iopub.status.busy":"2023-12-25T09:21:13.505429Z","iopub.status.idle":"2023-12-25T09:21:13.538838Z","shell.execute_reply":"2023-12-25T09:21:13.537882Z","shell.execute_reply.started":"2023-12-25T09:21:13.505773Z"},"trusted":true},"outputs":[{"data":{"text/plain":["{0: 'Crime',\n"," 1: 'Thriller',\n"," 2: 'Fantasy',\n"," 3: 'Horror',\n"," 4: 'Sci-Fi',\n"," 5: 'Comedy',\n"," 6: 'Documentary',\n"," 7: 'Adventure',\n"," 8: 'Film-Noir',\n"," 9: 'Animation',\n"," 10: 'Romance',\n"," 11: 'Drama',\n"," 12: 'Western',\n"," 13: 'Musical',\n"," 14: 'Action',\n"," 15: 'Mystery',\n"," 16: 'War',\n"," 17: \"Children's\"}"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["genres = [\"Crime\", \"Thriller\", \"Fantasy\", \"Horror\", \"Sci-Fi\", \"Comedy\", \"Documentary\", \"Adventure\", \"Film-Noir\", \"Animation\", \"Romance\", \"Drama\", \"Western\", \"Musical\", \"Action\", \"Mystery\", \"War\", \"Children\\'s\"]\n","mapping = {}\n","for i in range(len(genres)):\n","    mapping[i] = genres[i]\n","mapping"]},{"cell_type":"markdown","metadata":{},"source":["***"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:13.541348Z","iopub.status.busy":"2023-12-25T09:21:13.541044Z","iopub.status.idle":"2023-12-25T09:21:13.754109Z","shell.execute_reply":"2023-12-25T09:21:13.753302Z","shell.execute_reply.started":"2023-12-25T09:21:13.541322Z"},"trusted":true},"outputs":[],"source":["trainset = pd.read_csv('/kaggle/input/ml-dataset-2023s1/trainset.csv')\n","testset = pd.read_csv('/kaggle/input/ml-dataset-2023s1/testset.csv')\n","trainset.label = trainset.label.apply(lambda x: eval(x))\n","testset.label = testset.label.apply(lambda x: eval(x))\n","trainset.img_path = trainset.img_path.apply(lambda x: x.replace('\\\\', '/'))\n","testset.img_path = testset.img_path.apply(lambda x: x.replace('\\\\', '/'))"]},{"cell_type":"markdown","metadata":{},"source":["This is actually the dataset given by our lecturer. We decided to push this dataset privately on Kaggle to be able to run this code on Kaggle.\n","The Dataset has 3106 rows in trainset and 777 rows in testset.\n","However, since generating movie plots each run costs over 2 hours to finish, we had generated it and then saved it to 2 .csv files."]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:13.755783Z","iopub.status.busy":"2023-12-25T09:21:13.755489Z","iopub.status.idle":"2023-12-25T09:21:13.760684Z","shell.execute_reply":"2023-12-25T09:21:13.759728Z","shell.execute_reply.started":"2023-12-25T09:21:13.755757Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["3106 777\n"]}],"source":["print(len(trainset), len(testset))"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:13.762059Z","iopub.status.busy":"2023-12-25T09:21:13.761750Z","iopub.status.idle":"2023-12-25T09:21:21.423701Z","shell.execute_reply":"2023-12-25T09:21:21.422523Z","shell.execute_reply.started":"2023-12-25T09:21:13.762035Z"},"trusted":true},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2b19aad246a6453daf826a28d6fc8b66","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/2.50k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6b2da802377e42b397a49608b79423a1","version_major":2,"version_minor":0},"text/plain":["spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c89684248ea14925800ba5b41c2209ca","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ca7d547bb02a4f0dbd0296ccde13e17b","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0f6dc7ead35e496f8bc9ffe386bb4045","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/1.53k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8f2524006375450ab80d6d52b4ce50ba","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0d86467317c9498fa2606fe9e87eaa89","version_major":2,"version_minor":0},"text/plain":["generation_config.json:   0%|          | 0.00/142 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["tokenizer_gen = AutoTokenizer.from_pretrained(\"MBZUAI/LaMini-Flan-T5-248M\")\n","model_gen = AutoModelForSeq2SeqLM.from_pretrained(\"MBZUAI/LaMini-Flan-T5-248M\")"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:21.425553Z","iopub.status.busy":"2023-12-25T09:21:21.425168Z","iopub.status.idle":"2023-12-25T09:21:21.435369Z","shell.execute_reply":"2023-12-25T09:21:21.434216Z","shell.execute_reply.started":"2023-12-25T09:21:21.425523Z"},"trusted":true},"outputs":[],"source":["def generate_plot(df: pd.DataFrame, model: AutoModelForSeq2SeqLM, tokenizer: AutoTokenizer, device) -> pd.DataFrame:\n","    quote = 'What is the story of the movie {}?'\n","    model_gen.to(device)\n","    model_gen.eval()\n","\n","    for i in tqdm(range(len(df))):\n","        with torch.no_grad():\n","            input_ids = tokenizer(quote.format(df.title[i]), return_tensors='pt').input_ids.to(device)\n","            output = model.generate(input_ids, max_length=256, do_sample=True, temperature=0.09)\n","            df.loc[i, 'plot'] = tokenizer.decode(output[0], skip_special_tokens=True)\n","    return df"]},{"cell_type":"markdown","metadata":{},"source":["This is our unused function to generate movie plots. Below is how we used it to generate data."]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:21.437298Z","iopub.status.busy":"2023-12-25T09:21:21.436868Z","iopub.status.idle":"2023-12-25T09:21:22.492901Z","shell.execute_reply":"2023-12-25T09:21:22.491470Z","shell.execute_reply.started":"2023-12-25T09:21:21.437264Z"},"trusted":true},"outputs":[],"source":["device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:22.495575Z","iopub.status.busy":"2023-12-25T09:21:22.494953Z","iopub.status.idle":"2023-12-25T09:21:22.612963Z","shell.execute_reply":"2023-12-25T09:21:22.611570Z","shell.execute_reply.started":"2023-12-25T09:21:22.495522Z"},"trusted":true},"outputs":[],"source":["# trainset = generate_plot(trainset, model_gen, tokenizer_gen, device)\n","# testset = generate_plot(testset, model_gen, tokenizer_gen, device)"]},{"cell_type":"markdown","metadata":{},"source":["# Model Implementation"]},{"cell_type":"markdown","metadata":{},"source":["### Sub-models\n","***"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:22.620906Z","iopub.status.busy":"2023-12-25T09:21:22.620534Z","iopub.status.idle":"2023-12-25T09:21:34.001085Z","shell.execute_reply":"2023-12-25T09:21:34.000036Z","shell.execute_reply.started":"2023-12-25T09:21:22.620875Z"},"trusted":true},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ffc2604f01a1458e874f6228924fbc11","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7c177b5a2b444c8cb7b52f06d3fca821","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8f0a74a4449542328defea509acd8e24","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d2468b0ed5434e8a85be2f2faf987ee7","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6898208cae67445a961631cc37fda87d","version_major":2,"version_minor":0},"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'pre_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"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f7ab182a4cfe475f8a0d2b393f974206","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"84f17500b602407bae355f01437c82a8","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f9434012204e4523b7899a947ae12266","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8cc89f3d59094688ac76c7144c3b5a5d","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"058575f187e24b52b0d2efe549fba600","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/1.36k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"53956b30d7be449cb2aecdfa032b8f1d","version_major":2,"version_minor":0},"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["device(type='cuda')"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["tokenizer1 = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n","model1 = DistilBertForSequenceClassification .from_pretrained(\"distilbert-base-uncased\", problem_type=\"multi_label_classification\", num_labels=18)\n","model1.config.id2label = mapping\n","\n","tokenizer2 = AutoTokenizer.from_pretrained(\"dduy193/plot-classification\")\n","model2 = AutoModelForSequenceClassification.from_pretrained(\"dduy193/plot-classification\")\n","model2.config.id2label = mapping\n","\n","model3 = models.resnet101(pretrained=False)\n","model3.fc = torch.nn.Linear(2048, len(genres))\n","\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","model1.to(device)\n","model2.to(device)\n","model3.to(device)\n","device"]},{"cell_type":"markdown","metadata":{},"source":["### Deep Fusion Multimodal Model\n","***"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.003002Z","iopub.status.busy":"2023-12-25T09:21:34.002690Z","iopub.status.idle":"2023-12-25T09:21:34.011225Z","shell.execute_reply":"2023-12-25T09:21:34.010061Z","shell.execute_reply.started":"2023-12-25T09:21:34.002960Z"},"trusted":true},"outputs":[],"source":["class Multimodal(torch.nn.Module):\n","    def __init__(self, model1, model2, model3):\n","        super().__init__()\n","        self.model1 = model1\n","        self.model2 = model2\n","        self.model3 = model3\n","        self.fc1 = torch.nn.Linear(18, 18)\n","        self.fc2 = torch.nn.Linear(18, 18)\n","        self.fc3 = torch.nn.Linear(18, 18)\n","\n","    def forward(self, \n","                title_input_ids, title_attention_mask,\n","                plot_input_ids, plot_attention_mask,\n","                image_input):\n","        title_output = self.model1(title_input_ids, title_attention_mask)\n","        plot_output = self.model2(plot_input_ids, plot_attention_mask)\n","        image_output = self.model3(image_input)\n","\n","        title_output = self.fc1(title_output.logits)\n","        plot_output = self.fc2(plot_output.logits)\n","        image_output = self.fc3(image_output)\n","        \n","        output = torch.add(title_output, plot_output)\n","        output = torch.add(output, image_output)\n","        return output"]},{"cell_type":"markdown","metadata":{},"source":["# Custom Datasets & Data Loaders"]},{"cell_type":"markdown","metadata":{},"source":["***\n","### Custom Dataset\n","***"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.012715Z","iopub.status.busy":"2023-12-25T09:21:34.012432Z","iopub.status.idle":"2023-12-25T09:21:34.031474Z","shell.execute_reply":"2023-12-25T09:21:34.030400Z","shell.execute_reply.started":"2023-12-25T09:21:34.012691Z"},"trusted":true},"outputs":[],"source":["class Poroset(torch.utils.data.Dataset):\n","    def __init__(self, df, \n","                 tokenizer1, tokenizer2, \n","                 max_len1=64, max_len2=256,\n","                 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')):\n","        self.df = df\n","        self.tokenizer1 = tokenizer1\n","        self.tokenizer2 = tokenizer2\n","        self.max_len1 = max_len1\n","        self.max_len2 = max_len2\n","        self.device = device\n","        self.transform = v2.Compose([\n","            v2.Resize((224, 224)),\n","            v2.ToTensor(),\n","            v2.Normalize(mean=[0.485, 0.456, 0.406],\n","                                    std=[0.229, 0.224, 0.225])\n","        ])\n","\n","    def __len__(self):\n","        return len(self.df)\n","    \n","    def __getitem__(self, idx):\n","        row = self.df.iloc[idx]\n","        \n","        title = row['title']\n","        # Truncate title if it is too long\n","        if len(title) > self.max_len1:\n","            title = title[:self.max_len1]\n","\n","        plot = row['plot']\n","        # Truncate plot if it is too long\n","        if len(plot) > self.max_len2:\n","            plot = plot[:self.max_len2]\n","\n","        label = row['label']\n","        title_encoding = self.tokenizer1(title, truncation=True, padding='max_length', max_length=self.max_len1, return_tensors='pt')\n","        plot_encoding = self.tokenizer2(plot, truncation=True, padding='max_length', max_length=self.max_len2, return_tensors='pt')\n","        \n","        image_path = '/kaggle/input/ml-dataset-2023s1/ml1m/' + row['img_path']\n","        if os.path.exists(image_path):\n","            image_input = image.load_img(image_path)\n","            image_input = self.transform(image_input)\n","        else:\n","            image_input = torch.zeros((3, 224, 224))\n","        \n","        return {\n","            'title': title,\n","            'plot': plot,\n","            'title_input_ids': title_encoding['input_ids'].squeeze(),\n","            'title_attention_mask': title_encoding['attention_mask'].squeeze(),\n","            'plot_input_ids': plot_encoding['input_ids'].squeeze(),\n","            'plot_attention_mask': plot_encoding['attention_mask'].squeeze(),\n","            'image_input': image_input,\n","            'label': torch.FloatTensor(label)\n","        }"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.033238Z","iopub.status.busy":"2023-12-25T09:21:34.032817Z","iopub.status.idle":"2023-12-25T09:21:34.058795Z","shell.execute_reply":"2023-12-25T09:21:34.057816Z","shell.execute_reply.started":"2023-12-25T09:21:34.033210Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>title</th>\n","      <th>img_path</th>\n","      <th>label</th>\n","      <th>plot</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Washington Square (1997)</td>\n","      <td>ml1m/content/dataset/ml1m-images/1650.jpg</td>\n","      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...</td>\n","      <td>Washington Square is a 1997 American film abou...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Net, The (1995)</td>\n","      <td>ml1m/content/dataset/ml1m-images/185.jpg</td>\n","      <td>[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>Net is a 1995 American film directed by James ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Batman Returns (1992)</td>\n","      <td>ml1m/content/dataset/ml1m-images/1377.jpg</td>\n","      <td>[1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, ...</td>\n","      <td>Batman returns to the Batman universe after a ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Boys from Brazil, The (1978)</td>\n","      <td>ml1m/content/dataset/ml1m-images/3204.jpg</td>\n","      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>The movie Boys from Brazil, The (1978) is a ro...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Dear Jesse (1997)</td>\n","      <td>ml1m/content/dataset/ml1m-images/1901.jpg</td>\n","      <td>[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>Dear Jesse is a 1997 American drama film about...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                          title                                   img_path  \\\n","0      Washington Square (1997)  ml1m/content/dataset/ml1m-images/1650.jpg   \n","1               Net, The (1995)   ml1m/content/dataset/ml1m-images/185.jpg   \n","2         Batman Returns (1992)  ml1m/content/dataset/ml1m-images/1377.jpg   \n","3  Boys from Brazil, The (1978)  ml1m/content/dataset/ml1m-images/3204.jpg   \n","4             Dear Jesse (1997)  ml1m/content/dataset/ml1m-images/1901.jpg   \n","\n","                                               label  \\\n","0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...   \n","1  [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","2  [1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, ...   \n","3  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","4  [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","\n","                                                plot  \n","0  Washington Square is a 1997 American film abou...  \n","1  Net is a 1995 American film directed by James ...  \n","2  Batman returns to the Batman universe after a ...  \n","3  The movie Boys from Brazil, The (1978) is a ro...  \n","4  Dear Jesse is a 1997 American drama film about...  "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["trainset.head()"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.060635Z","iopub.status.busy":"2023-12-25T09:21:34.060306Z","iopub.status.idle":"2023-12-25T09:21:34.066557Z","shell.execute_reply":"2023-12-25T09:21:34.065422Z","shell.execute_reply.started":"2023-12-25T09:21:34.060608Z"},"trusted":true},"outputs":[],"source":["trainset = Poroset(df=trainset, tokenizer1=tokenizer1, tokenizer2=tokenizer2,\n","                   max_len1=64, max_len2=256,\n","                   device=device)\n","testset = Poroset(df=testset, tokenizer1=tokenizer1, tokenizer2=tokenizer2,\n","                  max_len1=64, max_len2=256,\n","                  device=device)\n"]},{"cell_type":"markdown","metadata":{},"source":["***\n","### Custom Data Loader\n","***"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.068107Z","iopub.status.busy":"2023-12-25T09:21:34.067729Z","iopub.status.idle":"2023-12-25T09:21:34.077514Z","shell.execute_reply":"2023-12-25T09:21:34.076655Z","shell.execute_reply.started":"2023-12-25T09:21:34.068080Z"},"trusted":true},"outputs":[],"source":["trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)\n","testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=True)"]},{"cell_type":"markdown","metadata":{},"source":["Check if the data loader is working properly"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.079355Z","iopub.status.busy":"2023-12-25T09:21:34.078713Z","iopub.status.idle":"2023-12-25T09:21:34.808056Z","shell.execute_reply":"2023-12-25T09:21:34.807055Z","shell.execute_reply.started":"2023-12-25T09:21:34.079321Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Title:  Children of the Corn III (1994)\n","Plot:  The movie \"Children of the Corn III\" (1994) is about a young boy named Jack who is a sailor and a fisherman who is stranded on a deserted island. He is rescued by a group of fishermen who are trying to find him and rescue him.\n","Label:  tensor([0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n"]},{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x79a2467447f0>"]},"execution_count":16,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sample = next(iter(testloader))\n","\n","# First sample of the batch\n","print('Title: ', sample['title'][3])\n","print('Plot: ', sample['plot'][3])\n","print('Label: ', sample['label'][3])\n","plt.imshow(sample['image_input'][3].permute(1, 2, 0))"]},{"cell_type":"markdown","metadata":{},"source":["# Setting up the Trainer"]},{"cell_type":"markdown","metadata":{},"source":["***\n","### GPU & Model Configuration\n","***"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.810062Z","iopub.status.busy":"2023-12-25T09:21:34.809720Z","iopub.status.idle":"2023-12-25T09:21:34.826041Z","shell.execute_reply":"2023-12-25T09:21:34.824838Z","shell.execute_reply.started":"2023-12-25T09:21:34.810032Z"},"trusted":true},"outputs":[{"data":{"text/plain":["device(type='cuda')"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["model = Multimodal(model1, model2, model3)\n","model.to(device)\n","device\n","\n","# Freeze layers\n","# for param in model.model2.parameters():\n","#     param.requires_grad = False"]},{"cell_type":"markdown","metadata":{},"source":["***\n","### Setting up loss function & optimizer\n","***"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.827695Z","iopub.status.busy":"2023-12-25T09:21:34.827357Z","iopub.status.idle":"2023-12-25T09:21:34.835831Z","shell.execute_reply":"2023-12-25T09:21:34.834801Z","shell.execute_reply.started":"2023-12-25T09:21:34.827668Z"},"trusted":true},"outputs":[],"source":["def loss_fn(outputs, targets):\n","    return torch.nn.BCEWithLogitsLoss()(outputs, targets)\n","\n","optimizer = torch.optim.Adam(params=model.parameters(), lr=2e-5)"]},{"cell_type":"markdown","metadata":{},"source":["***\n","### Trainer & Validation\n","***"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.837395Z","iopub.status.busy":"2023-12-25T09:21:34.837113Z","iopub.status.idle":"2023-12-25T09:21:34.849791Z","shell.execute_reply":"2023-12-25T09:21:34.848620Z","shell.execute_reply.started":"2023-12-25T09:21:34.837372Z"},"trusted":true},"outputs":[],"source":["history_loss = []\n","history_f1 = []\n","history_mapk = []\n","history_ndcg = []\n","def train(epoch):\n","    model.train()\n","    f1 = MultilabelF1Score(num_labels=18, threshold=0.5, average='macro')\n","    f1.to(device)\n","\n","    actual = []\n","    predicted = []\n","    for _, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n","        title_input_ids = data['title_input_ids'].to(device)\n","        title_attention_mask = data['title_attention_mask'].to(device)\n","        plot_input_ids = data['plot_input_ids'].to(device)\n","        plot_attention_mask = data['plot_attention_mask'].to(device)\n","        image_input = data['image_input'].to(device)\n","        label = data['label'].to(device)\n","\n","        optimizer.zero_grad()\n","        outputs = model(\n","            title_input_ids, title_attention_mask,\n","            plot_input_ids, plot_attention_mask,\n","            image_input\n","        )\n","        \n","        loss = loss_fn(outputs, label)\n","        loss.backward()\n","        optimizer.step()\n","\n","        f1.update(outputs.sigmoid(), label)\n","        \n","        probabilities = outputs.sigmoid().cpu().detach().numpy()\n","\n","        actual.append(label.cpu().detach().numpy())\n","        predicted.append(probabilities)\n","    actual_flat = np.vstack(actual)\n","    predicted_flat = np.vstack(predicted)\n","    maps = average_precision_score(actual_flat, predicted_flat, average=\"samples\")\n","    \n","    ndcg = ndcg_score(actual_flat, predicted_flat)\n","    \n","    print(f'Epoch: {epoch}, Train Loss: {loss.item()}, Train F1: {f1.compute().item()}, Train MAP: {maps}, Train NDCG: {ndcg}')\n","    history_loss.append(loss.item())\n","    history_f1.append(f1.compute().item())\n","    history_mapk.append(maps)\n","    history_ndcg.append(ndcg)"]},{"cell_type":"markdown","metadata":{},"source":["# Training Loop"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:21:34.851304Z","iopub.status.busy":"2023-12-25T09:21:34.850953Z","iopub.status.idle":"2023-12-25T09:45:57.830031Z","shell.execute_reply":"2023-12-25T09:45:57.829039Z","shell.execute_reply.started":"2023-12-25T09:21:34.851277Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:53<00:00,  1.16s/it]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 0, Train Loss: 0.2604980766773224, Train F1: 0.1412104368209839, Train MAP: 0.5009175700170833, Train NDCG: 0.6407130546740236\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 1, Train Loss: 0.14701153337955475, Train F1: 0.09471239894628525, Train MAP: 0.6386329269650214, Train NDCG: 0.7483783894197124\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 2, Train Loss: 0.1106482595205307, Train F1: 0.21269632875919342, Train MAP: 0.7242214790297368, Train NDCG: 0.8110983966105387\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 3, Train Loss: 0.08019614219665527, Train F1: 0.4034595489501953, Train MAP: 0.8227135659888679, Train NDCG: 0.881951669632758\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.12it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 4, Train Loss: 0.10751357674598694, Train F1: 0.5388647317886353, Train MAP: 0.8910779870639581, Train NDCG: 0.9296395797091079\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 5, Train Loss: 0.03833933547139168, Train F1: 0.6491369009017944, Train MAP: 0.9325851658864376, Train NDCG: 0.9571298040476531\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 6, Train Loss: 0.007258483208715916, Train F1: 0.7642349004745483, Train MAP: 0.9643119578068065, Train NDCG: 0.977951414700054\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.12it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 7, Train Loss: 0.11018259078264236, Train F1: 0.8283388614654541, Train MAP: 0.9795396693883495, Train NDCG: 0.9875275768866687\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 8, Train Loss: 0.06776639074087143, Train F1: 0.8822757601737976, Train MAP: 0.9885511656905733, Train NDCG: 0.9933993613848717\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 9, Train Loss: 0.042212847620248795, Train F1: 0.9065127968788147, Train MAP: 0.9920478089199918, Train NDCG: 0.9952726808979728\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 10, Train Loss: 0.003549717366695404, Train F1: 0.9325761795043945, Train MAP: 0.9941357357495478, Train NDCG: 0.9964811796156123\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 11, Train Loss: 0.0052366540767252445, Train F1: 0.9543657302856445, Train MAP: 0.9971657135702533, Train NDCG: 0.9984174474916241\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 12, Train Loss: 0.002149149775505066, Train F1: 0.9623235464096069, Train MAP: 0.998189372336185, Train NDCG: 0.9989966146013325\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:27<00:00,  1.12it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 13, Train Loss: 0.005435428116470575, Train F1: 0.9737388491630554, Train MAP: 0.9990420486717976, Train NDCG: 0.9994954470900846\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 14, Train Loss: 0.0027075856924057007, Train F1: 0.9833641052246094, Train MAP: 0.9991724159077668, Train NDCG: 0.9995094824151739\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 98/98 [01:28<00:00,  1.11it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Epoch: 15, Train Loss: 0.002378197619691491, Train F1: 0.9826716184616089, Train MAP: 0.9993217428632755, Train NDCG: 0.9996160653714945\n"]}],"source":["for epoch in range(16):\n","    train(epoch)"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:45:57.833306Z","iopub.status.busy":"2023-12-25T09:45:57.831945Z","iopub.status.idle":"2023-12-25T09:45:59.334229Z","shell.execute_reply":"2023-12-25T09:45:59.333165Z","shell.execute_reply.started":"2023-12-25T09:45:57.833270Z"},"trusted":true},"outputs":[],"source":["# Save model\n","torch.save(model.state_dict(), 'multimodel.pt')"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:45:59.336429Z","iopub.status.busy":"2023-12-25T09:45:59.335802Z","iopub.status.idle":"2023-12-25T09:45:59.596195Z","shell.execute_reply":"2023-12-25T09:45:59.595257Z","shell.execute_reply.started":"2023-12-25T09:45:59.336378Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Visualize\n","plt.plot(history_loss)\n","plt.plot(history_f1)\n","plt.plot(history_mapk)\n","plt.plot(history_ndcg)\n","plt.title('Model Loss')\n","plt.ylabel('Loss')\n","plt.xlabel('Epoch')\n","plt.legend(['loss', 'F1-Macro', 'MAP', 'NDCG'], loc='upper left')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Testing the model"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:45:59.598022Z","iopub.status.busy":"2023-12-25T09:45:59.597616Z","iopub.status.idle":"2023-12-25T09:45:59.610359Z","shell.execute_reply":"2023-12-25T09:45:59.609302Z","shell.execute_reply.started":"2023-12-25T09:45:59.597965Z"},"trusted":true},"outputs":[],"source":["# Validation\n","def test(testing_loader):\n","    model.eval()\n","    f1 = MultilabelF1Score(num_labels=18, threshold=0.5, average='macro')\n","    f1.to(device)\n","\n","    actual = []\n","    predicted = []\n","\n","    with torch.no_grad():\n","        for _, data in tqdm(enumerate(testing_loader, 0), total=len(testing_loader)):\n","            title_input_ids = data['title_input_ids'].to(device)\n","            title_attention_mask = data['title_attention_mask'].to(device)\n","            plot_input_ids = data['plot_input_ids'].to(device)\n","            plot_attention_mask = data['plot_attention_mask'].to(device)\n","            image_input = data['image_input'].to(device)\n","            label = data['label'].to(device)\n","\n","            outputs = model(\n","                title_input_ids, title_attention_mask,\n","                plot_input_ids, plot_attention_mask,\n","                image_input\n","            )\n","            f1.update(outputs.sigmoid(), label)\n","        \n","        probabilities = outputs.sigmoid().cpu().detach().numpy()\n","\n","        actual.append(label.cpu().detach().numpy())\n","        predicted.append(probabilities)\n","    actual_flat = np.vstack(actual)\n","    predicted_flat = np.vstack(predicted)\n","    mapp = average_precision_score(actual_flat, predicted_flat, average=\"samples\")\n","    \n","    ndcg = ndcg_score(actual_flat, predicted_flat)\n","    \n","    print(f'Test F1: {f1.compute().item()}, Test MAP: {mapp}, Test NDCG: {ndcg}')"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:45:59.611851Z","iopub.status.busy":"2023-12-25T09:45:59.611491Z","iopub.status.idle":"2023-12-25T09:46:13.795754Z","shell.execute_reply":"2023-12-25T09:46:13.794718Z","shell.execute_reply.started":"2023-12-25T09:45:59.611825Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 25/25 [00:14<00:00,  1.77it/s]"]},{"name":"stdout","output_type":"stream","text":["Test F1: 0.701256513595581, Test MAP: 0.9259259259259258, Test NDCG: 0.9522935243283689\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["test(testloader)"]},{"cell_type":"markdown","metadata":{},"source":["Let's try inferencing a sample from the batch that we already saw earlier."]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:46:13.797949Z","iopub.status.busy":"2023-12-25T09:46:13.797292Z","iopub.status.idle":"2023-12-25T09:46:13.808634Z","shell.execute_reply":"2023-12-25T09:46:13.807368Z","shell.execute_reply.started":"2023-12-25T09:46:13.797910Z"},"trusted":true},"outputs":[],"source":["def inference(title, plot, image_input, tokenizer1=tokenizer1, tokenizer2=tokenizer2, model=model, genres=genres, device=device):\n","    title_input = tokenizer1(title, return_tensors='pt', padding=True, truncation=True)\n","    title_input_ids = title_input['input_ids'].to(device)\n","    title_attention_mask = title_input['attention_mask'].to(device)\n","\n","    plot_input = tokenizer2(plot, return_tensors='pt', padding=True, truncation=True)\n","    plot_input_ids = plot_input['input_ids'].to(device)\n","    plot_attention_mask = plot_input['attention_mask'].to(device)\n","\n","    image_input = image_input.to(device)\n","    print(title)\n","    print(plot)\n","    plt.imshow(image_input.permute(1, 2, 0).cpu().detach().numpy())\n","\n","    output = model(title_input_ids, title_attention_mask, plot_input_ids, plot_attention_mask, image_input.unsqueeze(0))\n","    output = torch.sigmoid(output)\n","    output = output.cpu().detach().numpy()\n","    output = np.where(output > 0.5, 1, 0)\n","    output = output.squeeze()\n","    output = np.where(output == 1)[0]\n","    output = [genres[i] for i in output]\n","    return output"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2023-12-25T09:52:50.403156Z","iopub.status.busy":"2023-12-25T09:52:50.402719Z","iopub.status.idle":"2023-12-25T09:52:50.758702Z","shell.execute_reply":"2023-12-25T09:52:50.757801Z","shell.execute_reply.started":"2023-12-25T09:52:50.403121Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Children of the Corn III (1994)\n","The movie \"Children of the Corn III\" (1994) is about a young boy named Jack who is a sailor and a fisherman who is stranded on a deserted island. He is rescued by a group of fishermen who are trying to find him and rescue him.\n"]},{"data":{"text/plain":["['Horror']"]},"execution_count":27,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["inference(sample['title'][3], sample['plot'][3], sample['image_input'][3])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4213751,"sourceId":7273989,"sourceType":"datasetVersion"}],"dockerImageVersionId":30627,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"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":"3.10.12"},"vscode":{"interpreter":{"hash":"57bc2b6ce032b5f0e93daa91901b7ea38a856826ef43aa9e95b6d3999f5310df"}}},"nbformat":4,"nbformat_minor":4}