inference.ipynb

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     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import BertModel, BertPreTrainedModel\n",
    "from transformers import AutoModel, AutoConfig\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "\n",
    "class Model(BertPreTrainedModel) :\n",
    "    def __init__(self, output_classes, config) :\n",
    "        super().__init__(config)\n",
    "        self.bert = BertModel.from_pretrained(\"skt/kobert-base-v1\", config=config)\n",
    "        self.output_layer = nn.Linear(config.hidden_size, output_classes)\n",
    "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
    "        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n",
    "\n",
    "\n",
    "    def forward(self, input_ids, attention_mask) :\n",
    "        hidden_state = self.bert(input_ids = input_ids,\n",
    "                                 attention_mask = attention_mask)\n",
    "        data = hidden_state[1]\n",
    "        output = self.dropout(data)\n",
    "        output = self.dense(output)\n",
    "        output = torch.tanh(output)\n",
    "        output = self.dropout(output)\n",
    "        output = self.output_layer(output)\n",
    "        return output\n",
    "\n",
    "conf =  AutoConfig.from_pretrained(\"skt/kobert-base-v1\")\n",
    "model = Model(output_classes = 2, config = conf).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ecd571ed-9e82-45f4-9b60-d8ee53fd6b90",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
      "The tokenizer class you load from this checkpoint is 'XLNetTokenizer'. \n",
      "The class this function is called from is 'KoBERTTokenizer'.\n"
     ]
    }
   ],
   "source": [
    "#import dataframe\n",
    "import pandas as pd\n",
    "df_inf = pd.read_csv('news01.csv')\n",
    "\n",
    "#get tokenizer\n",
    "from kobert_tokenizer import KoBERTTokenizer\n",
    "tokenizer = KoBERTTokenizer.from_pretrained('skt/kobert-base-v1', last_hidden_states=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86fc8ad1-8252-44d0-ac1d-63bc9efc398c",
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0ca5707-ead6-4052-ad6e-32f0d37fa0ff",
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   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "class CustomDataset(Dataset) :\n",
    "    def __init__(self, dataframe, tokenizer, max_length) :\n",
    "        self.dataframe = dataframe\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_length = max_length\n",
    "\n",
    "    def __getitem__(self, x) :\n",
    "        data = self.dataframe.loc[x].title + '[CLS][PAD][PAD]' + self.dataframe.loc[x].content\n",
    "        data = self.tokenize(data)\n",
    "        input_ids = data.input_ids.squeeze()\n",
    "        attention_mask = data.attention_mask.squeeze()\n",
    "\n",
    "        return input_ids, attention_mask\n",
    "\n",
    "    def tokenize(self, data) :\n",
    "        token = self.tokenizer(data, padding = 'max_length', max_length = self.max_length, truncation = True, return_tensors = \"pt\")\n",
    "        del token['token_type_ids']\n",
    "        return token\n",
    "    \n",
    "    def __len__(self) :\n",
    "        return len(self.dataframe)\n",
    "\n",
    "test_dataset = CustomDataset(dataframe = df_inf,\n",
    "                             tokenizer = tokenizer,\n",
    "                             max_length = 512)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size = 8, num_workers = 4, shuffle = False)\n",
    "\n",
    "def inference(model, dataloader) :\n",
    "    outputs = []\n",
    "    model.eval()\n",
    "    with torch.no_grad() :\n",
    "        for batch, (input_ids, attention_mask) in enumerate(tqdm(dataloader)) :\n",
    "            input_ids, attention_mask = input_ids.to(device), attention_mask.to(device)\n",
    "            outs = model(input_ids = input_ids, attention_mask = attention_mask)\n",
    "            outs = torch.softmax(outs, dim=1)\n",
    "            outputs.append(outs)\n",
    "    return outputs"
   ]
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   "cell_type": "code",
   "execution_count": 55,
   "id": "761a783a-283e-4ec9-8277-b5111e5025dc",
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     "name": "stderr",
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     "text": [
      "100%|██████████| 45/45 [00:02<00:00, 16.58it/s]\n",
      "100%|██████████| 45/45 [00:02<00:00, 16.42it/s]\n",
      "100%|██████████| 45/45 [00:02<00:00, 16.51it/s]\n",
      "100%|██████████| 45/45 [00:02<00:00, 16.50it/s]\n",
      "100%|██████████| 45/45 [00:02<00:00, 16.44it/s]\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "\n",
    "for i in range(5):\n",
    "    model.load_state_dict(torch.load(f'./models/best_w_{i}.pth'))\n",
    "    data.append(inference(model, test_dataloader))"
   ]
  },
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       "       [5.8660531e-05, 9.9994135e-01],\n",
       "       [4.3162177e-05, 9.9995685e-01],\n",
       "       [3.4848446e-05, 9.9996519e-01],\n",
       "       [2.7014319e-05, 9.9997294e-01]], dtype=float32)"
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    "data[0][0]\n"
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   "id": "e44b52da-b9c8-43e2-b4e0-42e435303ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "pro = 0\n",
    "\n",
    "\n",
    "for i in range(5) :\n",
    "    aa = []\n",
    "    for a in range(len(data[0])) :\n",
    "        aa.extend(data[0][a])\n",
    "    ai = np.array(aa)\n",
    "    pro += ai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "71289a28-8dc6-4f04-90f6-ece65f7fb47a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sss = pro/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "aff90ca9-c955-4e47-930b-fcf47f444556",
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    {
     "data": {
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       "(355, 2)"
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    "np.shape(sss)"
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   "execution_count": 99,
   "id": "f91ea4f2-6e97-4e8a-9832-08587d71f6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = pd.read_csv('inference.csv')"
   ]
  },
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   "id": "de394a8c-5081-46c1-918a-0712c7bd992a",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "\n",
    "fig = px.histogram(t, x='0', nbins= 2)\n",
    "fig.show()"
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  }
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