BeIT_Mask_FineTuned.ipynb

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      "outputs": [],
      "source": [
        "%%capture\n",
        "! pip install --upgrade --force-reinstall git+https://github.com/huggingface/transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hm7MMB4fYGa",
        "outputId": "d1ac39a6-5f33-4355-adfd-c59283582fa8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/MaskDetection\n"
          ]
        }
      ],
      "source": [
        "%cd MaskDetection/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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        },
        "id": "gDFoQsohfYET",
        "outputId": "542d1816-048e-430f-e57d-ca5a6f6a4d62"
      },
      "outputs": [
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      "source": [
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "import os\n",
        "import pandas as pd\n",
        "from skimage import io\n",
        "from torch.utils.data import (\n",
        "    Dataset,\n",
        "    DataLoader\n",
        ")\n",
        "\n",
        "from transformers import BeitFeatureExtractor, BeitForImageClassification\n",
        "\n",
        "model_name_or_path = 'microsoft/beit-base-patch16-224-pt22k'\n",
        "feature_extractor = BeitFeatureExtractor.from_pretrained(model_name_or_path)\n",
        "\n",
        "class FeatureExtractor(object):\n",
        "    def __call__(self, image, target):\n",
        "        sample = feature_extractor(image, return_tensors='pt')\n",
        "        sample[\"labels\"] = target\n",
        "        return sample\n",
        "\n",
        "class MaskDataset(Dataset):\n",
        "    def __init__(self, csv_file, root_dir, transform=None):\n",
        "        self.annotations = pd.read_csv(csv_file)\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.annotations)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        img_path = os.path.join(self.root_dir, self.annotations.iloc[index, 0])\n",
        "        image = io.imread(img_path)\n",
        "        y_label = torch.tensor(int(self.annotations.iloc[index, 1]))\n",
        "        \n",
        "        data = self.transform(image,y_label)\n",
        "\n",
        "        return data\n",
        "\n",
        "train_ds = MaskDataset(\n",
        "    csv_file=\"train.csv\",\n",
        "    root_dir=\"\",\n",
        "    # transform=transforms.ToTensor(),\n",
        "    transform=FeatureExtractor(),\n",
        ")\n",
        "\n",
        "test_ds = MaskDataset(\n",
        "    csv_file=\"test.csv\",\n",
        "    root_dir=\"\",\n",
        "    # transform=transforms.ToTensor(),\n",
        "    transform=FeatureExtractor(),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjzkotEVfYB8",
        "outputId": "9d3cf56f-f218-4aee-f9de-c2f33e4a1e98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 224, 224])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ds.__getitem__(0)['pixel_values'].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MS0EwwL9ok7T",
        "outputId": "00c58112-84c3-493c-e4cc-7933c69d1bfa"
      },
      "outputs": [],
      "source": [
        "! pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "LAgb7ArkfX90",
        "outputId": "b03fa7d8-5d91-4f09-abc9-e4f281d13ecf"
      },
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      ],
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\n",
        "def compute_metrics(p):\n",
        "    return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)"
      ]
    },
    {
      "cell_type": "code",
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        },
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        "outputId": "1af75bb3-3407-4d8f-e08c-07244c0d70c6"
      },
      "outputs": [
        {
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Some weights of the model checkpoint at microsoft/beit-base-patch16-224-pt22k were not used when initializing BeitForImageClassification: ['lm_head.weight', 'lm_head.bias', 'layernorm.weight', 'layernorm.bias']\n",
            "- This IS expected if you are initializing BeitForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BeitForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BeitForImageClassification were not initialized from the model checkpoint at microsoft/beit-base-patch16-224-pt22k and are newly initialized: ['classifier.weight', 'classifier.bias', 'beit.pooler.layernorm.weight', 'beit.pooler.layernorm.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "labels = [\"No Mask\",\"Mask\"]\n",
        "\n",
        "model = BeitForImageClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=len(labels),\n",
        "    ignore_mismatched_sizes=True,\n",
        "    id2label={str(i): c for i, c in enumerate(labels)},\n",
        "    label2id={c: str(i) for i, c in enumerate(labels)}\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIT6geMLfX5L",
        "outputId": "9462ebdb-4b43-4c89-9660-83c5ac28694e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "PyTorch: setting up devices\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments\n",
        "from transformers import EarlyStoppingCallback\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "  output_dir=\"./beit-tiny-mask-finetuned\",\n",
        "  per_device_train_batch_size=16,\n",
        "  evaluation_strategy=\"steps\",\n",
        "  num_train_epochs=4,\n",
        "  save_total_limit = 4, # Only last 4 models are saved. Older ones are deleted.\n",
        "  fp16=True,\n",
        "  save_steps=100,\n",
        "  eval_steps=100,\n",
        "  logging_steps=10,\n",
        "  learning_rate=2e-4,\n",
        "  remove_unused_columns=False,\n",
        "  push_to_hub=False,\n",
        "  report_to='tensorboard',\n",
        "  load_best_model_at_end=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "AduOOQd8fmOU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def collate_fn(batch):\n",
        "    return {\n",
        "        'pixel_values': torch.stack([x['pixel_values'][0] for x in batch]),\n",
        "        'labels': torch.tensor([x['labels'] for x in batch])\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "np0we5YBfoKt",
        "outputId": "fa1058da-fa2f-4afc-f145-5a2dec1f2dfb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using amp half precision backend\n"
          ]
        }
      ],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=collate_fn,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    tokenizer=feature_extractor,\n",
        "    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4wMV13uqfoIu",
        "outputId": "cd2293d0-811c-4c3c-d4b8-f81bce7789b7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 14428\n",
            "  Num Epochs = 4\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 3608\n"
          ]
        },
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='3608' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 500/3608 58:14 < 6:03:26, 0.14 it/s, Epoch 0/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.497800</td>\n",
              "      <td>0.177015</td>\n",
              "      <td>0.940211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.186300</td>\n",
              "      <td>0.080292</td>\n",
              "      <td>0.974972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.081800</td>\n",
              "      <td>0.206466</td>\n",
              "      <td>0.942436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.198200</td>\n",
              "      <td>0.168230</td>\n",
              "      <td>0.943826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.029300</td>\n",
              "      <td>0.088829</td>\n",
              "      <td>0.982481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          },
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./beit-tiny-mask-finetuned/checkpoint-100\n",
            "Configuration saved in ./beit-tiny-mask-finetuned/checkpoint-100/config.json\n",
            "Model weights saved in ./beit-tiny-mask-finetuned/checkpoint-100/pytorch_model.bin\n",
            "Feature extractor saved in ./beit-tiny-mask-finetuned/checkpoint-100/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./beit-tiny-mask-finetuned/checkpoint-200\n",
            "Configuration saved in ./beit-tiny-mask-finetuned/checkpoint-200/config.json\n",
            "Model weights saved in ./beit-tiny-mask-finetuned/checkpoint-200/pytorch_model.bin\n",
            "Feature extractor saved in ./beit-tiny-mask-finetuned/checkpoint-200/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./beit-tiny-mask-finetuned/checkpoint-300\n",
            "Configuration saved in ./beit-tiny-mask-finetuned/checkpoint-300/config.json\n",
            "Model weights saved in ./beit-tiny-mask-finetuned/checkpoint-300/pytorch_model.bin\n",
            "Feature extractor saved in ./beit-tiny-mask-finetuned/checkpoint-300/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./beit-tiny-mask-finetuned/checkpoint-400\n",
            "Configuration saved in ./beit-tiny-mask-finetuned/checkpoint-400/config.json\n",
            "Model weights saved in ./beit-tiny-mask-finetuned/checkpoint-400/pytorch_model.bin\n",
            "Feature extractor saved in ./beit-tiny-mask-finetuned/checkpoint-400/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./beit-tiny-mask-finetuned/checkpoint-500\n",
            "Configuration saved in ./beit-tiny-mask-finetuned/checkpoint-500/config.json\n",
            "Model weights saved in ./beit-tiny-mask-finetuned/checkpoint-500/pytorch_model.bin\n",
            "Feature extractor saved in ./beit-tiny-mask-finetuned/checkpoint-500/preprocessor_config.json\n",
            "Deleting older checkpoint [beit-tiny-mask-finetuned/checkpoint-100] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./beit-tiny-mask-finetuned/checkpoint-200 (score: 0.08029154688119888).\n",
            "Saving model checkpoint to ./beit-tiny-mask-finetuned\n",
            "Configuration saved in ./beit-tiny-mask-finetuned/config.json\n",
            "Model weights saved in ./beit-tiny-mask-finetuned/pytorch_model.bin\n",
            "Feature extractor saved in ./beit-tiny-mask-finetuned/preprocessor_config.json\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        0.55\n",
            "  total_flos               = 576468516GF\n",
            "  train_loss               =       0.151\n",
            "  train_runtime            =  0:58:16.56\n",
            "  train_samples_per_second =      16.505\n",
            "  train_steps_per_second   =       1.032\n"
          ]
        }
      ],
      "source": [
        "train_results = trainer.train()\n",
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "BL_e0TxofrHQ",
        "outputId": "9fbbd3a2-e8b2-43d8-92a0-a33380fb6553"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n"
          ]
        },
        {
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              "      [450/450 03:12]\n",
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            "***** eval metrics *****\n",
            "  epoch                   =       0.55\n",
            "  eval_accuracy           =      0.975\n",
            "  eval_loss               =     0.0803\n",
            "  eval_runtime            = 0:03:13.02\n",
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