donut/notepads/donut-eval.ipynb

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   "source": [
    "from transformers import DonutProcessor, VisionEncoderDecoderModel\n",
    "from datasets import load_dataset\n",
    "import re\n",
    "import json\n",
    "import torch\n",
    "from tqdm.auto import tqdm\n",
    "import numpy as np\n",
    "\n",
    "from donut import JSONParseEvaluator"
   ]
  },
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   "source": [
    "\n",
    "processor = DonutProcessor.from_pretrained(\"Zombely/plwiki-test\")\n",
    "model = VisionEncoderDecoderModel.from_pretrained(\"Zombely/plwiki-test\")"
   ]
  },
  {
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   "execution_count": 3,
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    {
     "name": "stderr",
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     "text": [
      "Using custom data configuration Zombely--pl-text-images-f3f66e614f4d9a7a\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset None/None to /home/pc/.cache/huggingface/datasets/Zombely___parquet/Zombely--pl-text-images-f3f66e614f4d9a7a/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
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      "   "
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      "text/plain": [
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      ]
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     "name": "stdout",
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     "text": [
      "Dataset parquet downloaded and prepared to /home/pc/.cache/huggingface/datasets/Zombely___parquet/Zombely--pl-text-images-f3f66e614f4d9a7a/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
     ]
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   ],
   "source": [
    "dataset = load_dataset(\"Zombely/pl-text-images\", split=\"validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "{'accuracies': [0, 0.9791666666666666, 0.9156626506024097, 1.0, 0.9836552748885586, 1.0, 0.7335359675785207, 0.9512987012987013, 0.396732788798133, 0.9908675799086758, 0.9452954048140044], 'mean_accuracy': 0.8087468213232427} length : 11\n",
      "Mean accuracy: 0.8087468213232427\n"
     ]
    }
   ],
   "source": [
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "model.eval()\n",
    "model.to(device)\n",
    "\n",
    "output_list = []\n",
    "accs = []\n",
    "\n",
    "\n",
    "for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):\n",
    "    # prepare encoder inputs\n",
    "    pixel_values = processor(sample[\"image\"].convert(\"RGB\"), return_tensors=\"pt\").pixel_values\n",
    "    pixel_values = pixel_values.to(device)\n",
    "    # prepare decoder inputs\n",
    "    task_prompt = \"<s_cord-v2>\"\n",
    "    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
    "    decoder_input_ids = decoder_input_ids.to(device)\n",
    "    \n",
    "    # autoregressively generate sequence\n",
    "    outputs = model.generate(\n",
    "            pixel_values,\n",
    "            decoder_input_ids=decoder_input_ids,\n",
    "            max_length=model.decoder.config.max_position_embeddings,\n",
    "            early_stopping=True,\n",
    "            pad_token_id=processor.tokenizer.pad_token_id,\n",
    "            eos_token_id=processor.tokenizer.eos_token_id,\n",
    "            use_cache=True,\n",
    "            num_beams=1,\n",
    "            bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
    "            return_dict_in_generate=True,\n",
    "        )\n",
    "\n",
    "    # turn into JSON\n",
    "    seq = processor.batch_decode(outputs.sequences)[0]\n",
    "    seq = seq.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
    "    seq = re.sub(r\"<.*?>\", \"\", seq, count=1).strip()  # remove first task start token\n",
    "    seq = processor.token2json(seq)\n",
    "\n",
    "    ground_truth = json.loads(sample[\"ground_truth\"])\n",
    "    ground_truth = ground_truth[\"gt_parse\"]\n",
    "    evaluator = JSONParseEvaluator()\n",
    "    score = evaluator.cal_acc(seq, ground_truth)\n",
    "\n",
    "    accs.append(score)\n",
    "    output_list.append(seq)\n",
    "\n",
    "scores = {\"accuracies\": accs, \"mean_accuracy\": np.mean(accs)}\n",
    "print(scores, f\"length : {len(accs)}\")\n",
    "print(\"Mean accuracy:\", np.mean(accs))"
   ]
  }
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