{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"vscode":{"interpreter":{"hash":"57bc2b6ce032b5f0e93daa91901b7ea38a856826ef43aa9e95b6d3999f5310df"}},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":7273989,"sourceType":"datasetVersion","datasetId":4213751}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Importing the required libraries","metadata":{}},{"cell_type":"code","source":"import torch\nimport pandas as pd\nimport numpy as np\nimport os\nimport warnings\nimport matplotlib.pyplot as plt\n\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, DistilBertForSequenceClassification, AutoModelForSeq2SeqLM\nfrom tqdm import tqdm\nfrom torchvision import models\nfrom torchvision.transforms import v2\nfrom torch.utils.data import Dataset, DataLoader\nfrom keras.preprocessing import image\nfrom torchmetrics.classification import MultilabelF1Score\nfrom sklearn.metrics import average_precision_score, ndcg_score","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:21.946543Z","iopub.execute_input":"2023-12-24T20:24:21.946928Z","iopub.status.idle":"2023-12-24T20:24:39.075031Z","shell.execute_reply.started":"2023-12-24T20:24:21.946896Z","shell.execute_reply":"2023-12-24T20:24:39.074021Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","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","output_type":"stream"}]},{"cell_type":"markdown","source":"### Setting up the environment\n***","metadata":{}},{"cell_type":"code","source":"warnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:39.077016Z","iopub.execute_input":"2023-12-24T20:24:39.077764Z","iopub.status.idle":"2023-12-24T20:24:39.082480Z","shell.execute_reply.started":"2023-12-24T20:24:39.077728Z","shell.execute_reply":"2023-12-24T20:24:39.081472Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"***","metadata":{}},{"cell_type":"markdown","source":"# Data Preprocessing","metadata":{}},{"cell_type":"code","source":"genres = [\"Crime\", \"Thriller\", \"Fantasy\", \"Horror\", \"Sci-Fi\", \"Comedy\", \"Documentary\", \"Adventure\", \"Film-Noir\", \"Animation\", \"Romance\", \"Drama\", \"Western\", \"Musical\", \"Action\", \"Mystery\", \"War\", \"Children\\'s\"]\nmapping = {}\nfor i in range(len(genres)):\n    mapping[i] = genres[i]\nmapping","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:39.083738Z","iopub.execute_input":"2023-12-24T20:24:39.084396Z","iopub.status.idle":"2023-12-24T20:24:39.102308Z","shell.execute_reply.started":"2023-12-24T20:24:39.084362Z","shell.execute_reply":"2023-12-24T20:24:39.101422Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","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\"}"},"metadata":{}}]},{"cell_type":"markdown","source":"***","metadata":{}},{"cell_type":"code","source":"trainset = pd.read_csv('/kaggle/input/ml-dataset-2023s1/trainset.csv')\ntestset = pd.read_csv('/kaggle/input/ml-dataset-2023s1/testset.csv')\ntrainset.label = trainset.label.apply(lambda x: eval(x))\ntestset.label = testset.label.apply(lambda x: eval(x))\ntrainset.img_path = trainset.img_path.apply(lambda x: x.replace('\\\\', '/'))\ntestset.img_path = testset.img_path.apply(lambda x: x.replace('\\\\', '/'))","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:39.104625Z","iopub.execute_input":"2023-12-24T20:24:39.104937Z","iopub.status.idle":"2023-12-24T20:24:39.286604Z","shell.execute_reply.started":"2023-12-24T20:24:39.104903Z","shell.execute_reply":"2023-12-24T20:24:39.285646Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"print(len(trainset), len(testset))","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:39.287752Z","iopub.execute_input":"2023-12-24T20:24:39.288008Z","iopub.status.idle":"2023-12-24T20:24:39.292769Z","shell.execute_reply.started":"2023-12-24T20:24:39.287985Z","shell.execute_reply":"2023-12-24T20:24:39.291911Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"3106 777\n","output_type":"stream"}]},{"cell_type":"code","source":"tokenizer_gen = AutoTokenizer.from_pretrained(\"MBZUAI/LaMini-Flan-T5-248M\")\nmodel_gen = AutoModelForSeq2SeqLM.from_pretrained(\"MBZUAI/LaMini-Flan-T5-248M\")","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:39.294043Z","iopub.execute_input":"2023-12-24T20:24:39.294392Z","iopub.status.idle":"2023-12-24T20:24:50.312836Z","shell.execute_reply.started":"2023-12-24T20:24:39.294360Z","shell.execute_reply":"2023-12-24T20:24:50.311944Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/2.50k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e9e15dbfbbdb420887c6c99f416b98d5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"38809e386306424c9db375f5c63ddd1b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9f4a77264da7421fb65eb048ec48ed38"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"54e22652f35644a5b9c3667a8b9757c7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/1.53k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"31dbd98619574f6b826fbbd0bf4dd60a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fe28915a8cb47ce92db01be9e53e62c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/142 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"07ca22d143154c23a743975118441d89"}},"metadata":{}}]},{"cell_type":"code","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","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:50.313910Z","iopub.execute_input":"2023-12-24T20:24:50.314190Z","iopub.status.idle":"2023-12-24T20:24:50.321223Z","shell.execute_reply.started":"2023-12-24T20:24:50.314164Z","shell.execute_reply":"2023-12-24T20:24:50.320122Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:50.322307Z","iopub.execute_input":"2023-12-24T20:24:50.322558Z","iopub.status.idle":"2023-12-24T20:24:50.358375Z","shell.execute_reply.started":"2023-12-24T20:24:50.322534Z","shell.execute_reply":"2023-12-24T20:24:50.357298Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# trainset = generate_plot(trainset, model_gen, tokenizer_gen, device)\n# testset = generate_plot(testset, model_gen, tokenizer_gen, device)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:50.359654Z","iopub.execute_input":"2023-12-24T20:24:50.359944Z","iopub.status.idle":"2023-12-24T20:24:50.369094Z","shell.execute_reply.started":"2023-12-24T20:24:50.359918Z","shell.execute_reply":"2023-12-24T20:24:50.368105Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"# Model Implementation","metadata":{}},{"cell_type":"markdown","source":"### Sub-models\n***","metadata":{}},{"cell_type":"code","source":"tokenizer1 = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\nmodel1 = DistilBertForSequenceClassification .from_pretrained(\"distilbert-base-uncased\", problem_type=\"multi_label_classification\", num_labels=18)\nmodel1.config.id2label = mapping\n\ntokenizer2 = AutoTokenizer.from_pretrained(\"dduy193/plot-classification\")\nmodel2 = AutoModelForSequenceClassification.from_pretrained(\"dduy193/plot-classification\")\nmodel2.config.id2label = mapping\n\nmodel3 = models.resnet101(pretrained=False)\nmodel3.fc = torch.nn.Linear(2048, len(genres))\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel1.to(device)\nmodel2.to(device)\nmodel3.to(device)\ndevice","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:24:50.372505Z","iopub.execute_input":"2023-12-24T20:24:50.372779Z","iopub.status.idle":"2023-12-24T20:25:02.893172Z","shell.execute_reply.started":"2023-12-24T20:24:50.372756Z","shell.execute_reply":"2023-12-24T20:25:02.892101Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2060918e07ea4f02ade0210bb57c6fcb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"38dc266d2e474345a7ae8bd86b65f803"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1110cd2434e54504817ca4be7488b161"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6a8340f64e44489b049319c7c81363c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9ace0fba2fe546ce8576aca2437795eb"}},"metadata":{}},{"name":"stderr","text":"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'pre_classifier.bias']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4eaed633fafe4d758cb8675999e5104c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3a5e902fcbda4fa09f1e5a5dfa9dc289"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33043dabaa4447ac8824784c6d568627"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fd054639c5d7483da861657f0f8c03ca"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/1.36k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"06725a0c49444e2eaf4fb5b39b44711c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"04521f8014a347779a3f746aa38247bc"}},"metadata":{}},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"device(type='cuda')"},"metadata":{}}]},{"cell_type":"markdown","source":"### Deep Fusion Multimodal Model\n***","metadata":{}},{"cell_type":"code","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","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:02.894461Z","iopub.execute_input":"2023-12-24T20:25:02.894756Z","iopub.status.idle":"2023-12-24T20:25:02.903181Z","shell.execute_reply.started":"2023-12-24T20:25:02.894730Z","shell.execute_reply":"2023-12-24T20:25:02.901879Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"# Custom Datasets & Data Loaders","metadata":{}},{"cell_type":"markdown","source":"***\n### Custom Dataset\n***","metadata":{}},{"cell_type":"code","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        }","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:02.904483Z","iopub.execute_input":"2023-12-24T20:25:02.904756Z","iopub.status.idle":"2023-12-24T20:25:02.920222Z","shell.execute_reply.started":"2023-12-24T20:25:02.904732Z","shell.execute_reply":"2023-12-24T20:25:02.919401Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"trainset.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:02.921249Z","iopub.execute_input":"2023-12-24T20:25:02.921523Z","iopub.status.idle":"2023-12-24T20:25:02.948147Z","shell.execute_reply.started":"2023-12-24T20:25:02.921499Z","shell.execute_reply":"2023-12-24T20:25:02.947223Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"                          title                                   img_path  \\\n0      Washington Square (1997)  ml1m/content/dataset/ml1m-images/1650.jpg   \n1               Net, The (1995)   ml1m/content/dataset/ml1m-images/185.jpg   \n2         Batman Returns (1992)  ml1m/content/dataset/ml1m-images/1377.jpg   \n3  Boys from Brazil, The (1978)  ml1m/content/dataset/ml1m-images/3204.jpg   \n4             Dear Jesse (1997)  ml1m/content/dataset/ml1m-images/1901.jpg   \n\n                                               label  \\\n0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...   \n1  [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n2  [1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, ...   \n3  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n4  [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n\n                                                plot  \n0  Washington Square is a 1997 American film abou...  \n1  Net is a 1995 American film directed by James ...  \n2  Batman returns to the Batman universe after a ...  \n3  The movie Boys from Brazil, The (1978) is a ro...  \n4  Dear Jesse is a 1997 American drama film about...  ","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>"},"metadata":{}}]},{"cell_type":"code","source":"trainset = Poroset(df=trainset, tokenizer1=tokenizer1, tokenizer2=tokenizer2,\n                   max_len1=64, max_len2=256,\n                   device=device)\ntestset = Poroset(df=testset, tokenizer1=tokenizer1, tokenizer2=tokenizer2,\n                  max_len1=64, max_len2=256,\n                  device=device)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:02.949402Z","iopub.execute_input":"2023-12-24T20:25:02.949747Z","iopub.status.idle":"2023-12-24T20:25:02.958058Z","shell.execute_reply.started":"2023-12-24T20:25:02.949713Z","shell.execute_reply":"2023-12-24T20:25:02.957115Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"***\n### Custom Data Loader\n***","metadata":{}},{"cell_type":"code","source":"trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=True)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:02.959429Z","iopub.execute_input":"2023-12-24T20:25:02.959737Z","iopub.status.idle":"2023-12-24T20:25:02.967025Z","shell.execute_reply.started":"2023-12-24T20:25:02.959707Z","shell.execute_reply":"2023-12-24T20:25:02.966055Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"Check if the data loader is working properly","metadata":{}},{"cell_type":"code","source":"sample = next(iter(testloader))\n\n# First sample of the batch\nprint('Title: ', sample['title'][3])\nprint('Plot: ', sample['plot'][3])\nprint('Label: ', sample['label'][3])\nplt.imshow(sample['image_input'][3].permute(1, 2, 0))","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:02.968253Z","iopub.execute_input":"2023-12-24T20:25:02.968584Z","iopub.status.idle":"2023-12-24T20:25:03.652422Z","shell.execute_reply.started":"2023-12-24T20:25:02.968500Z","shell.execute_reply":"2023-12-24T20:25:03.651364Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Title:  Sunshine (1999)\nPlot:  Sunshine (1999) is a romantic comedy film about a young woman named Lily who falls in love with a man named Jack. They fall in love and fall in love, but their relationship is complicated by Jack's past and his past. The movie explores themes of love, loss\nLabel:  tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])\n","output_type":"stream"},{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x790e716ec6d0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Setting up the Trainer","metadata":{}},{"cell_type":"markdown","source":"***\n### GPU & Model Configuration\n***","metadata":{}},{"cell_type":"code","source":"model = Multimodal(model1, model2, model3)\nmodel.to(device)\ndevice\n\n# Freeze layers\nfor param in model.model2.parameters():\n    param.requires_grad = False","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:03.653777Z","iopub.execute_input":"2023-12-24T20:25:03.654082Z","iopub.status.idle":"2023-12-24T20:25:03.668357Z","shell.execute_reply.started":"2023-12-24T20:25:03.654040Z","shell.execute_reply":"2023-12-24T20:25:03.667573Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"***\n### Setting up loss function & optimizer\n***","metadata":{}},{"cell_type":"code","source":"def loss_fn(outputs, targets):\n    return torch.nn.BCEWithLogitsLoss()(outputs, targets)\n\noptimizer = torch.optim.Adam(params=model.parameters(), lr=2e-5)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:03.669413Z","iopub.execute_input":"2023-12-24T20:25:03.669760Z","iopub.status.idle":"2023-12-24T20:25:03.677737Z","shell.execute_reply.started":"2023-12-24T20:25:03.669733Z","shell.execute_reply":"2023-12-24T20:25:03.676800Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"***\n### Trainer & Validation\n***","metadata":{}},{"cell_type":"code","source":"history_loss = []\nhistory_f1 = []\nhistory_mapk = []\nhistory_ndcg = []\ndef 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)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:03.679150Z","iopub.execute_input":"2023-12-24T20:25:03.679480Z","iopub.status.idle":"2023-12-24T20:25:03.692032Z","shell.execute_reply.started":"2023-12-24T20:25:03.679445Z","shell.execute_reply":"2023-12-24T20:25:03.691115Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"# Training Loop","metadata":{}},{"cell_type":"code","source":"for epoch in range(32):\n    train(epoch)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:25:03.693194Z","iopub.execute_input":"2023-12-24T20:25:03.693501Z","iopub.status.idle":"2023-12-24T20:59:41.837498Z","shell.execute_reply.started":"2023-12-24T20:25:03.693477Z","shell.execute_reply":"2023-12-24T20:59:41.836012Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stderr","text":"100%|██████████| 98/98 [01:18<00:00,  1.24it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 0, Train Loss: 0.6380023956298828, Train F1: 0.1615493893623352, Train MAP: 0.2868538138656695, Train NDCG: 0.4733425494117361\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 1, Train Loss: 0.20452113449573517, Train F1: 0.15911796689033508, Train MAP: 0.36970567560642276, Train NDCG: 0.5412537637140072\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 2, Train Loss: 1.219512939453125, Train F1: 0.14611569046974182, Train MAP: 0.43003280816429457, Train NDCG: 0.5899738681466264\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:03<00:00,  1.55it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 3, Train Loss: 0.49733319878578186, Train F1: 0.15879061818122864, Train MAP: 0.4537453964145543, Train NDCG: 0.6086232671325396\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:03<00:00,  1.55it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 4, Train Loss: 0.19800424575805664, Train F1: 0.17705440521240234, Train MAP: 0.49058972804015605, Train NDCG: 0.6372679951339031\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 5, Train Loss: 0.3164418637752533, Train F1: 0.20260636508464813, Train MAP: 0.5290702834175857, Train NDCG: 0.6674661722072143\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 6, Train Loss: 0.39157184958457947, Train F1: 0.238995760679245, Train MAP: 0.5748992597827514, Train NDCG: 0.7030228074493886\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 7, Train Loss: 0.2953750789165497, Train F1: 0.26984903216362, Train MAP: 0.6273160231636236, Train NDCG: 0.7428451603366092\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 8, Train Loss: 0.2243884652853012, Train F1: 0.3123369812965393, Train MAP: 0.6789898592391395, Train NDCG: 0.7818296582118703\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 9, Train Loss: 0.32021474838256836, Train F1: 0.34886544942855835, Train MAP: 0.7322112659986463, Train NDCG: 0.8211641006008771\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 10, Train Loss: 0.3273795545101166, Train F1: 0.3910631537437439, Train MAP: 0.7698327318140015, Train NDCG: 0.8487160315573549\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 11, Train Loss: 0.12368684262037277, Train F1: 0.4239617884159088, Train MAP: 0.7958919490435816, Train NDCG: 0.8674028678139855\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 12, Train Loss: 0.11783574521541595, Train F1: 0.45414406061172485, Train MAP: 0.8281815465545486, Train NDCG: 0.8895786835700069\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 13, Train Loss: 0.018345143646001816, Train F1: 0.47877901792526245, Train MAP: 0.8442021542670666, Train NDCG: 0.8999711572264576\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 14, Train Loss: 0.2691822052001953, Train F1: 0.5042015314102173, Train MAP: 0.8619598414975721, Train NDCG: 0.9118576297210965\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:03<00:00,  1.54it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 15, Train Loss: 0.077192023396492, Train F1: 0.5223862528800964, Train MAP: 0.8766882777098299, Train NDCG: 0.9220998452894026\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 16, Train Loss: 0.17467060685157776, Train F1: 0.5472869873046875, Train MAP: 0.8856665151265972, Train NDCG: 0.927612956990511\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 17, Train Loss: 0.018982166424393654, Train F1: 0.5827990770339966, Train MAP: 0.9010396060844197, Train NDCG: 0.9374411336603918\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 18, Train Loss: 0.011844775639474392, Train F1: 0.5997107625007629, Train MAP: 0.9068465227221998, Train NDCG: 0.9412081686248377\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 19, Train Loss: 0.24724435806274414, Train F1: 0.624536395072937, Train MAP: 0.917960087971901, Train NDCG: 0.9483359033618628\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 20, Train Loss: 0.01606348715722561, Train F1: 0.627023458480835, Train MAP: 0.9198990444325612, Train NDCG: 0.9498596343964368\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 21, Train Loss: 0.12419483810663223, Train F1: 0.6510038375854492, Train MAP: 0.9280116949580938, Train NDCG: 0.954751975930422\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 22, Train Loss: 0.051102686673402786, Train F1: 0.661782443523407, Train MAP: 0.9308463532346387, Train NDCG: 0.9565931105894429\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 23, Train Loss: 0.018615007400512695, Train F1: 0.6879494786262512, Train MAP: 0.9415343017119702, Train NDCG: 0.9636913810200178\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 24, Train Loss: 0.12504427134990692, Train F1: 0.7012491226196289, Train MAP: 0.9438102784923456, Train NDCG: 0.964992289689749\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 25, Train Loss: 0.023185165598988533, Train F1: 0.729793906211853, Train MAP: 0.95247773323573, Train NDCG: 0.970489132830624\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 26, Train Loss: 0.015627283602952957, Train F1: 0.7474939227104187, Train MAP: 0.954580217977272, Train NDCG: 0.9719341255003784\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 27, Train Loss: 0.0014103105058893561, Train F1: 0.7486421465873718, Train MAP: 0.9586121382016425, Train NDCG: 0.9743495262728257\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.56it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 28, Train Loss: 0.29243651032447815, Train F1: 0.7783758640289307, Train MAP: 0.9637231583702125, Train NDCG: 0.9776394597921105\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 29, Train Loss: 0.0036198075395077467, Train F1: 0.7662091255187988, Train MAP: 0.9661608561628684, Train NDCG: 0.9793606050835744\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 30, Train Loss: 0.039602745324373245, Train F1: 0.8106442093849182, Train MAP: 0.9716163712155341, Train NDCG: 0.9828310330609948\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 98/98 [01:02<00:00,  1.57it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch: 31, Train Loss: 0.11838477104902267, Train F1: 0.8107895255088806, Train MAP: 0.9711294013366614, Train NDCG: 0.9822425226720071\n","output_type":"stream"}]},{"cell_type":"code","source":"# Save model\ntorch.save(model.state_dict(), 'partially_frozen_multimodel.pt')","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:59:41.840272Z","iopub.execute_input":"2023-12-24T20:59:41.840804Z","iopub.status.idle":"2023-12-24T20:59:42.963481Z","shell.execute_reply.started":"2023-12-24T20:59:41.840758Z","shell.execute_reply":"2023-12-24T20:59:42.962655Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"# Visualize\nplt.plot(history_loss)\nplt.plot(history_f1)\nplt.plot(history_mapk)\nplt.plot(history_ndcg)\nplt.title('Model Loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['loss', 'F1-Macro', 'MAP', 'NDCG'], loc='upper left')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:59:42.964677Z","iopub.execute_input":"2023-12-24T20:59:42.964940Z","iopub.status.idle":"2023-12-24T20:59:43.225008Z","shell.execute_reply.started":"2023-12-24T20:59:42.964917Z","shell.execute_reply":"2023-12-24T20:59:43.224107Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Testing the model","metadata":{}},{"cell_type":"code","source":"# Validation\ndef 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}')","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:59:43.226179Z","iopub.execute_input":"2023-12-24T20:59:43.226440Z","iopub.status.idle":"2023-12-24T20:59:43.237814Z","shell.execute_reply.started":"2023-12-24T20:59:43.226416Z","shell.execute_reply":"2023-12-24T20:59:43.236798Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"test(testloader)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:59:43.239164Z","iopub.execute_input":"2023-12-24T20:59:43.239487Z","iopub.status.idle":"2023-12-24T20:59:56.202013Z","shell.execute_reply.started":"2023-12-24T20:59:43.239455Z","shell.execute_reply":"2023-12-24T20:59:56.201117Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stderr","text":"100%|██████████| 25/25 [00:12<00:00,  1.93it/s]","output_type":"stream"},{"name":"stdout","text":"Test F1: 0.27938681840896606, Test MAP: 0.5135465297230003, Test NDCG: 0.6681248928775978\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","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","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:59:56.203479Z","iopub.execute_input":"2023-12-24T20:59:56.203848Z","iopub.status.idle":"2023-12-24T20:59:56.213514Z","shell.execute_reply.started":"2023-12-24T20:59:56.203815Z","shell.execute_reply":"2023-12-24T20:59:56.212555Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"inference(sample['title'][1], sample['plot'][1], sample['image_input'][1])","metadata":{"execution":{"iopub.status.busy":"2023-12-24T20:59:56.214641Z","iopub.execute_input":"2023-12-24T20:59:56.214916Z","iopub.status.idle":"2023-12-24T20:59:56.581004Z","shell.execute_reply.started":"2023-12-24T20:59:56.214887Z","shell.execute_reply":"2023-12-24T20:59:56.580130Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Exorcist III, The (1990)\nExorcist III is a movie about a group of rebels who rebel against the oppressive government of the Soviet Union. They are forced to flee their homes and seek refuge in a remote island in the Pacific Ocean. Along the way, they encounter various obstacles an\n","output_type":"stream"},{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"['Horror']"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}