eval_colab.ipynb

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      "metadata": {
        "id": "eJSnnT1suCSL"
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      "source": [
        "from transformers import pipeline\n",
        "from datasets import Image as ImageFeature\n",
        "from datasets import load_dataset"
      ]
    },
    {
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      "execution_count": 2,
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          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n",
            "WARNING:datasets.builder:Using custom data configuration competitions--aiornot-f559c639a48093b2\n",
            "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/competitions___parquet/competitions--aiornot-f559c639a48093b2/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
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      "source": [
        "pipe = pipeline('image-classification', model='sunilSabnis/vit-large-ai-or-not', device=0)\n",
        "dataset = load_dataset('competitions/aiornot').cast_column('image', ImageFeature())"
      ]
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        "outputId": "3743b91d-c811-4ed0-f7c6-46d6903df0a4"
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          "metadata": {},
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      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "from transformers.pipelines.pt_utils import KeyDataset\n",
        "\n",
        "preds = []\n",
        "for out in tqdm(pipe(KeyDataset(dataset['test'], \"image\"))):\n",
        "    preds.append(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
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        },
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              "({0: 0.9996787309646606, 1: 0.0003212423180229962},\n",
              " [{'score': 0.9996787309646606, 'label': 'LABEL_0'},\n",
              "  {'score': 0.0003212423180229962, 'label': 'LABEL_1'}])"
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        "len(preds)\n",
        "preds[0]\n",
        "scores = {0:None, 1: None}\n",
        "for score_dict in preds[0]:\n",
        "  if score_dict['label'] == 'LABEL_0':\n",
        "    scores[0] = score_dict['score']\n",
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      "execution_count": 18,
      "metadata": {
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      "source": [
        "import csv\n",
        "with open('test.csv','w') as file:\n",
        "    writer=csv.writer(file, delimiter=',',lineterminator='\\n',)\n",
        "    for i in range(len(dataset['test'])):\n",
        "        \n",
        "        scores = {0:None, 1: None}\n",
        "        for score_dict in preds[i]:\n",
        "          if score_dict['label'] == 'LABEL_0':\n",
        "            scores[0] = score_dict['score']\n",
        "          else:\n",
        "            scores[1] = score_dict['score']\n",
        "  \n",
        "        row = [dataset['test'][i]['id']] + [float(scores[0])] + [float(scores[1])]\n",
        "        writer.writerow(row)"
      ]
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