demo_custom_handwriting.ipynb

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        "id": "Ovbc34eSqrpz"
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      "source": [
        "!git clone https://github.com/ankanbhunia/Handwriting-Transformers\n",
        "%cd Handwriting-Transformers\n",
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!gdown --id 16g9zgysQnWk7-353_tMig92KsZsrcM6k && unzip files.zip && rm files.zip"
      ]
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      "execution_count": 1,
      "metadata": {
        "id": "qCgH3y4Rqnap"
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      "source": [
        "import os\n",
        "import time\n",
        "from data.dataset import load_itw_samples\n",
        "import torch\n",
        "import cv2\n",
        "import os\n",
        "import numpy as np\n",
        "from models.model import TRGAN\n",
        "from params import *\n",
        "from torch import nn\n",
        "from data.dataset import get_transform\n",
        "import pickle\n",
        "from PIL import Image\n",
        "import tqdm\n",
        "import shutil"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fPCYXZrlqnar"
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      "source": [
        "text = \"A paragraph is a series of related sentences developing a central idea, called the topic. Try to think about paragraphs in terms of thematic unity: a paragraph is a sentence or a group of sentences that supports one central, unified idea. Paragraphs add one idea at a time to your broader argument\"\n",
        "output_path = 'results'"
      ]
    },
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      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "E5Bo1qPlqnas"
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      "source": [
        "model_path = 'files/iam_model.pth'\n",
        "#model_path = 'files/cvl_model.pth'\n"
      ]
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            "(1) Loading model...\n"
          ]
        },
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            "/disk/scratch_ssd/s2514643/.conda/envs/tiger/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/disk/scratch_ssd/s2514643/.conda/envs/tiger/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
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            "initialize network with N02\n",
            "initialize network with N02\n",
            "initialize network with N02\n"
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            "/disk/scratch_ssd/s2514643/.conda/envs/tiger/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n",
            "/disk/scratch_ssd/s2514643/.conda/envs/tiger/lib/python3.8/site-packages/torchvision/models/inception.py:43: FutureWarning: The default weight initialization of inception_v3 will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11299), please set init_weights=True.\n",
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          "text": [
            "files/iam_model.pth : Model loaded Successfully\n",
            "(2) Loading text content...\n",
            "Done.\n"
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        "batch_size = 1\n",
        "print ('(1) Loading model...')\n",
        "model = TRGAN(batch_size = batch_size)\n",
        "model.netG.load_state_dict(torch.load(model_path))\n",
        "print (model_path+' : Model loaded Successfully')\n",
        "model.eval()\n",
        "print ('(2) Loading text content...')\n",
        "text_encode =  [j.encode() for j in text.split(' ')]\n",
        "eval_text_encode, eval_len_text = model.netconverter.encode(text_encode)\n",
        "eval_text_encode = eval_text_encode.to('cuda:0').repeat(batch_size, 1, 1)\n",
        "print ('Done.')\n"
      ]
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        "## load your *cropped word images* of the custom handwriting here. Images do NOT need to be scaled, resized or padded. load_itw_samples(.) will take care of them.\n",
        "\n",
        "style_inputs, width_length = load_itw_samples('custom_data_1/style_examples/')\n",
        "\n",
        "page_val = model._generate_page(style_inputs.to(DEVICE).clone(), width_length, eval_text_encode, eval_len_text)\n",
        "\n",
        "cv2.imwrite('output.png', page_val*255)"
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