{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNGW7SKhuYSWVy08d6N46tj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["# Install and Preparation..."],"metadata":{"id":"8oJW8II0V30G"}},{"cell_type":"code","source":["!pip install gcsfs==2023.6.0\n","!pip install fsspec==2023.6.0"],"metadata":{"collapsed":true,"id":"kufKx-VMEFWN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1731433831603,"user_tz":-480,"elapsed":6923,"user":{"displayName":"Timothy CHAN","userId":"13045196819212164142"}},"outputId":"21151377-3bda-44c8-8189-79d86af1898d"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: gcsfs==2023.6.0 in /usr/local/lib/python3.10/dist-packages (2023.6.0)\n","Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (3.10.10)\n","Requirement already satisfied: decorator>4.1.2 in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (4.4.2)\n","Requirement already satisfied: fsspec==2023.6.0 in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (2023.6.0)\n","Requirement already satisfied: google-auth>=1.2 in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (2.27.0)\n","Requirement already satisfied: google-auth-oauthlib in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (1.2.1)\n","Requirement already satisfied: google-cloud-storage in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (2.8.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from gcsfs==2023.6.0) (2.32.3)\n","Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs==2023.6.0) 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mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->pytorch-lightning>=1.6.4->donut-python) (1.3.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets[vision]->donut-python) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets[vision]->donut-python) (2024.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets[vision]->donut-python) (2024.2)\n","Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml->sconf>=0.2.3->donut-python) (0.2.12)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.12.0->aiohttp->datasets[vision]->donut-python) (0.2.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->pytorch-lightning>=1.6.4->donut-python) (2.1.5)\n"]}],"source":["!pip install gradio\n","!pip install sentence-transformers==2.2.2\n","!pip install transformers==4.25.1\n","!pip install pytorch-lightning==1.9.0\n","!pip install timm==0.5.4\n","!pip install donut-python"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","%cd \"/content/drive/MyDrive/Colab Notebooks/Donut/donut/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NiKtmoiqIsuS","executionInfo":{"status":"ok","timestamp":1731433871854,"user_tz":-480,"elapsed":2853,"user":{"displayName":"Timothy CHAN","userId":"13045196819212164142"}},"outputId":"86a17e4d-4265-41d0-b36c-6d6503c09fe4"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","/content/drive/MyDrive/Colab Notebooks/Donut/donut\n"]}]},{"cell_type":"code","source":["import gradio as gr\n","import argparse\n","import torch\n","from PIL import Image\n","from donut import DonutModel"],"metadata":{"id":"PnjyQXNBDmH6","executionInfo":{"status":"ok","timestamp":1731433871855,"user_tz":-480,"elapsed":19,"user":{"displayName":"Timothy CHAN","userId":"13045196819212164142"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["def demo_process(input_img):\n","    global model, task_prompt, task_name\n","    input_img = Image.fromarray(input_img)\n","    output = model.inference(image=input_img, prompt=task_prompt)[\"predictions\"][0]\n","    return output"],"metadata":{"id":"8_alWsH5UXWi","executionInfo":{"status":"ok","timestamp":1731433871855,"user_tz":-480,"elapsed":18,"user":{"displayName":"Timothy CHAN","userId":"13045196819212164142"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["parser = argparse.ArgumentParser()\n","parser.add_argument(\"--task\", type=str, default=\"Booking\")\n","parser.add_argument(\"--pretrained_path\", type=str, default=\"result/train_booking/20241112_150925\")\n","args, left_argv = parser.parse_known_args()\n","task_name = args.task\n","task_prompt = f\"<s_{task_name}>\"\n","\n","model = DonutModel.from_pretrained(\"./result/train_booking/20241112_150925\")\n","if torch.cuda.is_available():\n","    model.half()\n","    device = torch.device(\"cuda\")\n","    model.to(device)\n","else:\n","    model.encoder.to(torch.bfloat16)\n","\n","model.eval()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B1vfuxJbRWeQ","executionInfo":{"status":"ok","timestamp":1731433888326,"user_tz":-480,"elapsed":16488,"user":{"displayName":"Timothy CHAN","userId":"13045196819212164142"}},"outputId":"ec1e2f31-7058-45df-bb83-731ad318670d"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:399: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  return torch.load(checkpoint_file, map_location=\"cpu\")\n"]},{"output_type":"execute_result","data":{"text/plain":["DonutModel(\n","  (encoder): SwinEncoder(\n","    (model): SwinTransformer(\n","      (patch_embed): PatchEmbed(\n","        (proj): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))\n","        (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","      )\n","      (pos_drop): Dropout(p=0.0, inplace=False)\n","      (layers): Sequential(\n","        (0): BasicLayer(\n","          dim=128, input_resolution=(320, 240), depth=2\n","          (blocks): ModuleList(\n","            (0): SwinTransformerBlock(\n","              (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","              (attn): WindowAttention(\n","                (qkv): Linear(in_features=128, out_features=384, bias=True)\n","                (attn_drop): Dropout(p=0.0, inplace=False)\n","                (proj): Linear(in_features=128, out_features=128, bias=True)\n","                (proj_drop): Dropout(p=0.0, inplace=False)\n","                (softmax): Softmax(dim=-1)\n","              )\n","              (drop_path): Identity()\n","              (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","              (mlp): Mlp(\n","                (fc1): Linear(in_features=128, out_features=512, bias=True)\n","                (act): GELU(approximate='none')\n","                (drop1): Dropout(p=0.0, inplace=False)\n","                (fc2): Linear(in_features=512, out_features=128, bias=True)\n","                (drop2): Dropout(p=0.0, inplace=False)\n","              )\n","            )\n","            (1): SwinTransformerBlock(\n","              (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","              (attn): WindowAttention(\n","                (qkv): Linear(in_features=128, out_features=384, bias=True)\n","                (attn_drop): Dropout(p=0.0, inplace=False)\n","                (proj): Linear(in_features=128, out_features=128, bias=True)\n","                (proj_drop): Dropout(p=0.0, inplace=False)\n","                (softmax): Softmax(dim=-1)\n","              )\n","              (drop_path): DropPath()\n","              (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","              (mlp): Mlp(\n","                (fc1): Linear(in_features=128, out_features=512, bias=True)\n","                (act): GELU(approximate='none')\n","                (drop1): Dropout(p=0.0, inplace=False)\n","                (fc2): Linear(in_features=512, out_features=128, bias=True)\n","                (drop2): Dropout(p=0.0, inplace=False)\n","              )\n","            )\n","          )\n","          (downsample): PatchMerging(\n","            input_resolution=(320, 240), dim=128\n","            (reduction): Linear(in_features=512, out_features=256, bias=False)\n","            (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n","          )\n","        )\n","        (1): BasicLayer(\n","          dim=256, input_resolution=(160, 120), depth=2\n","          (blocks): ModuleList(\n","            (0-1): 2 x SwinTransformerBlock(\n","              (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n","              (attn): WindowAttention(\n","                (qkv): Linear(in_features=256, out_features=768, bias=True)\n","                (attn_drop): Dropout(p=0.0, inplace=False)\n","                (proj): Linear(in_features=256, out_features=256, bias=True)\n","                (proj_drop): Dropout(p=0.0, inplace=False)\n","                (softmax): Softmax(dim=-1)\n","              )\n","              (drop_path): DropPath()\n","              (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n","              (mlp): Mlp(\n","                (fc1): Linear(in_features=256, out_features=1024, bias=True)\n","                (act): GELU(approximate='none')\n","                (drop1): Dropout(p=0.0, inplace=False)\n","                (fc2): Linear(in_features=1024, out_features=256, bias=True)\n","                (drop2): Dropout(p=0.0, inplace=False)\n","              )\n","            )\n","          )\n","          (downsample): PatchMerging(\n","            input_resolution=(160, 120), dim=256\n","            (reduction): Linear(in_features=1024, out_features=512, bias=False)\n","            (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","          )\n","        )\n","        (2): BasicLayer(\n","          dim=512, input_resolution=(80, 60), depth=14\n","          (blocks): ModuleList(\n","            (0-13): 14 x SwinTransformerBlock(\n","              (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n","              (attn): WindowAttention(\n","                (qkv): Linear(in_features=512, out_features=1536, bias=True)\n","                (attn_drop): Dropout(p=0.0, inplace=False)\n","                (proj): Linear(in_features=512, out_features=512, bias=True)\n","                (proj_drop): Dropout(p=0.0, inplace=False)\n","                (softmax): Softmax(dim=-1)\n","              )\n","              (drop_path): DropPath()\n","              (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n","              (mlp): Mlp(\n","                (fc1): Linear(in_features=512, out_features=2048, bias=True)\n","                (act): GELU(approximate='none')\n","                (drop1): Dropout(p=0.0, inplace=False)\n","                (fc2): Linear(in_features=2048, out_features=512, bias=True)\n","                (drop2): Dropout(p=0.0, inplace=False)\n","              )\n","            )\n","          )\n","          (downsample): PatchMerging(\n","            input_resolution=(80, 60), dim=512\n","            (reduction): Linear(in_features=2048, out_features=1024, bias=False)\n","            (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n","          )\n","        )\n","        (3): BasicLayer(\n","          dim=1024, input_resolution=(40, 30), depth=2\n","          (blocks): ModuleList(\n","            (0-1): 2 x SwinTransformerBlock(\n","              (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","              (attn): WindowAttention(\n","                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n","                (attn_drop): Dropout(p=0.0, inplace=False)\n","                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (proj_drop): Dropout(p=0.0, inplace=False)\n","                (softmax): Softmax(dim=-1)\n","              )\n","              (drop_path): DropPath()\n","              (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","              (mlp): Mlp(\n","                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n","                (act): GELU(approximate='none')\n","                (drop1): Dropout(p=0.0, inplace=False)\n","                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n","                (drop2): Dropout(p=0.0, inplace=False)\n","              )\n","            )\n","          )\n","        )\n","      )\n","      (norm): None\n","      (avgpool): AdaptiveAvgPool1d(output_size=1)\n","      (head): Identity()\n","    )\n","  )\n","  (decoder): BARTDecoder(\n","    (model): MBartForCausalLM(\n","      (model): MBartDecoderWrapper(\n","        (decoder): MBartDecoder(\n","          (embed_tokens): Embedding(57554, 1024, padding_idx=1)\n","          (embed_positions): MBartLearnedPositionalEmbedding(770, 1024)\n","          (layers): ModuleList(\n","            (0-3): 4 x MBartDecoderLayer(\n","              (self_attn): MBartAttention(\n","                (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","              )\n","              (activation_fn): GELUActivation()\n","              (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","              (encoder_attn): MBartAttention(\n","                (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","                (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n","              )\n","              (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","              (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n","              (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n","              (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","            )\n","          )\n","          (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n","        )\n","      )\n","      (lm_head): Linear(in_features=1024, out_features=57554, bias=False)\n","    )\n","  )\n",")"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["# Start gradio..."],"metadata":{"id":"FTwdNk0yWOqs"}},{"cell_type":"code","source":["demo = gr.Interface(fn=demo_process,inputs=\"image\",outputs=\"json\", title=f\"Donut 🍩 demonstration for `{task_name}` task\",)\n","demo.launch(debug=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":680},"id":"6qccbLFaQuIU","executionInfo":{"status":"ok","timestamp":1731434081244,"user_tz":-480,"elapsed":181196,"user":{"displayName":"Timothy CHAN","userId":"13045196819212164142"}},"outputId":"95e572b7-cff1-457c-aa39-6027daba4a86"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n","\n","Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n","* Running on public URL: https://fce912f4f29f9f530a.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://fce912f4f29f9f530a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Keyboard interruption in main thread... closing server.\n","Killing tunnel 127.0.0.1:7860 <> https://fce912f4f29f9f530a.gradio.live\n"]},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":17}]}]}