Feat: LayoutLMv3 trained on the FUNSD dataset

This notebook trains the LayoutLMv3 layout model (on kagggle to take advantage of the GPU accelerators) on the FUNSD dataset.
It also provides an inference phase where you can store your results on google drive

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install Pillow\n","!pip install pytesseract\n","!pip install datasets\n","!sudo apt-get install libtesseract-dev python3-pil tesseract-ocr-eng tesseract-ocr-script-latn\n"],"metadata":{"id":"HA_83Izr8Ay6","collapsed":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NBckcUj7Lyal","executionInfo":{"status":"ok","timestamp":1741029947610,"user_tz":0,"elapsed":8937,"user":{"displayName":"Simão Quintela","userId":"13164910078562325200"}},"outputId":"3a5002cd-79df-4bba-f64a-d83e472bf29d"},"execution_count":3,"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"]}]},{"cell_type":"code","execution_count":4,"metadata":{"id":"I-ppmx1a05gY","collapsed":true,"executionInfo":{"status":"ok","timestamp":1741029949225,"user_tz":0,"elapsed":5,"user":{"displayName":"Simão Quintela","userId":"13164910078562325200"}}},"outputs":[],"source":["import os\n","\n","os.chdir('drive/MyDrive/Colab Notebooks')"]},{"cell_type":"markdown","source":["### References\n","Apply classes to this model like in here: https://medium.com/@tejpal.abhyuday/information-extraction-part-3-9c2487ec4930"],"metadata":{"id":"-qvDPH-qlj33"}},{"cell_type":"code","source":["import numpy as np\n","from transformers import LayoutLMv3Processor, LayoutLMv3Tokenizer, LayoutLMv3ForTokenClassification, AdamW, LayoutLMv3ImageProcessor\n","from datasets import load_dataset, Dataset, Features, Sequence, ClassLabel, Value, Array2D, Array3D\n","import torch\n","from PIL import Image, ImageDraw, ImageFont\n","from tqdm.notebook import tqdm"],"metadata":{"id":"ovjLqOlFCZ1e","executionInfo":{"status":"ok","timestamp":1741029968791,"user_tz":0,"elapsed":16955,"user":{"displayName":"Simão Quintela","userId":"13164910078562325200"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# Load our dataset\n","funsd_dataset = load_dataset(\"nielsr/funsd\")\n","id2label = [\"O\", \"B-HEADER\", \"I-HEADER\", \"B-QUESTION\", \"I-QUESTION\", \"B-ANSWER\", \"I-ANSWER\"]\n","label2id = {l:i for i, l in enumerate(id2label)}\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":460,"referenced_widgets":["00ba0d7300e6417aa4350a22af12b5ad","06ad3707e73a4f9086f5705a369c87e8","948b79e1f45247c7af98ca3642f3e566","fd37f64801484c1f8e429d3fd4dbe548","32332b27299b42afa686a6c6969b29ed","91591dcb0d6f4151a0c4b47aea80c22c","4a74c348e7ca4271b6d9f267fdeaba9c","8f38c2eb73a44a658282f21ec532ce7f","7d7351eb3e224346bb8681a792cc5ae7","b5f026b7c6e448bebb72c7a9c0484d90","d004e1ea81524734a2393772ac5683d7","e5eff9cfe1c04527b10fd7a13eee5f13","4328e6656bc34727bca16f849ea0b7e1","20ead82bc5e74ebaabc64c9a382eb5a0","78fd95b671ed49a7b2c7c9f3ec8cce18","0316fa51cbca4e75a81d5a34768dabb9","36de2a0aa32b4885a8eb29501381f4bf","95bb9927e66e432b886b7f9da69df885","87d5ed49387948adb0ac63c44bd1f067","2d396af9570c44fcbb9d98b6b2dae03d","db13024b11444a62984d99b53dcd2801","3ac6424d85f545bcbec371e7bcc4e0c9","52c9a5ad220a4e9ab902391ce560d635","f0893cf8f81c43c6a89c9d22b633f82f","3de5b6203d6f42d18c957050cf804b86","63398d2907fc4fbba6732d00e5e6cb4c","d05e18efcc814b83bad62ca2da21cd22","e65d1b68f5ed46ee9554a875420bc93c","c3d85e8cd4784a73aaaa74043f4f38eb","a85fe3a65ca14dbea1fd994b3447da4c","c94c9de83f5c48edb8656543e50ec481","35e7a858fe2344e692bd98b9a7cc5271","8a6a8beeb73743ff9b294b10f039c300","59c1be8d68834c7bb6d9c619b1600d87","22d681db15b244b2b5df1183d80c0e50","2e096f49905648bc8502cc3bc9e9dc58","4c3837188a70491b89d667434f678d29","1bafa89ad5a44fe8b4262b462c1135e9","1f198aee8a11462ab41cf8f7b1b2b830","c44f34b40b694472a29cbcd612301dcf","8fc55f57c7e84d4daa38e144560504ab","d9e27c356d6b4bf082e267b1a6dca601","587654adb3a84e2490b8690ecb230ecc","d71dada534d947b292a075d47c823b12"]},"id":"3IHHKf0aCb8n","executionInfo":{"status":"ok","timestamp":1741029999486,"user_tz":0,"elapsed":30689,"user":{"displayName":"Simão Quintela","userId":"13164910078562325200"}},"outputId":"07795595-53f5-4916-918c-d08bfbbe2915"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["funsd.py: 0%| | 0.00/4.54k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"00ba0d7300e6417aa4350a22af12b5ad"}},"metadata":{}},{"name":"stdout","output_type":"stream","text":["The repository for nielsr/funsd contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/nielsr/funsd.\n","You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n","\n","Do you wish to run the custom code? [y/N] y\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data: 0%| | 0.00/16.8M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5eff9cfe1c04527b10fd7a13eee5f13"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"52c9a5ad220a4e9ab902391ce560d635"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"59c1be8d68834c7bb6d9c619b1600d87"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'O': 0,\n"," 'B-HEADER': 1,\n"," 'I-HEADER': 2,\n"," 'B-QUESTION': 3,\n"," 'I-QUESTION': 4,\n"," 'B-ANSWER': 5,\n"," 'I-ANSWER': 6}"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# Processor and model\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","processor = LayoutLMv3Processor(\n"," image_processor=LayoutLMv3ImageProcessor(apply_ocr=True),\n"," tokenizer=LayoutLMv3Tokenizer.from_pretrained(\"microsoft/layoutlmv3-base\"),\n",")\n","model = LayoutLMv3ForTokenClassification.from_pretrained(\n"," \"microsoft/layoutlmv3-base\", num_labels=len(id2label)\n",")\n","model.to(device);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["add91e2650f14a3ca81156aea71d861e","09b1e833c8ff4603b9a8dd13a8fe6796","f79a49d991724e6698cbb4ec9eab8e0e","45eff6094e5a40ef952c6ec318341720","073d225e234c411f8725a30bf8745cc2","c24a82a9f790491594107fc31bb28db4","e8989c5562494e509dff9312883ebd48","3823367257854f489e0540614c6b7149","0bca305d55f4406484e1baf435a76532","7e4d76dea0174e428ac534e3836be531","eff9fd977c8c4886b3ad8f16462f2c1e","8e3b23940bca43a5bd25fd9a2cf677a7","6dde07f8479e441aa9f7f60a654a271c","43c7e7e92dfe4db5b05c5b8678c2a168","1d8680e25a854f3e9e19efc8e1321c29","9ed2c2aaf9f1443aa0464fbbf1a85e2a","bd248ee9d16b476b985ce19f51c7c7e3","d34f1544a5b24a359b9c5a0db52c0ec6","3c03fbb415b446f1a0e0185ab0ef0c15","e491a1db6f14427c9ba93f148b2f8db6","365b80a8f39e417ca24b7758237ae856","ae837f8fae6d467dae865fc63a47b93c","38fb4fd17e5b4bdead971850673ca247","0e139fa3ce1c436b899e9282ad02f542","389c8a27e6e74cd283585b36c7ae20ac","948a5155aaa24f4c92f7468617a7cae1","afddba665dfd40fcbffaed723e82849a","28f12040b42a40ef84366b174bae01cf","d1b1596c6f8c4ab4aace17e3f5c3d856","a21427de566842ab94bd8e2190ec1869","e7503f9bde2941a99659b7257ac95cdb","0f6e623197b449d9b98a7bda6a16d33e","136f84bfc8b44e509b471b793ad2ee49","95ccbcf2180d4bb7ae24634dbfe2af13","134b31acfc0d4e37952a5f9bf7bb05b0","cb4fb059f54844f58e3f9ff7bbe267e9","4fc63f8517064b14ae52cd2ee21e430c","c408161098a44f7ca30a1c21e05a217e","cfe54473555e4b18a96f34ffdb202f69","b066466e85f3418281d3a80f838f99a8","a76e59b5987148f385b36ecbd2c5b0b9","05304f88942e46d087bf3c8a604e07ea","95550ab008af4324b92d80318d2e7ac3","a2c7b850a49942bbb69ef966c8877594","1da5ab56c33846d7b48123fb847e013e","89d60ab1ba8a4cf1b1ffe729a6f7683a","6d6fbf757a76464782dc05b75f105995","2fcffd859f994190862853155c07ec10","b727f17253574b1497a181314d287073","61ad8c45cd7b46c4be5fc038391aeb6d","1697325176c849fe85caaa3d6c81dc9d","ae4c9adefaff4ffba6008aa7f27c00ca","c97cbfc717d04379b59df38149bb7d14","94c22dee94684f149e74b92cf5090acb","1633d2801472443090c5b959874fea55"]},"id":"5QoQUskfCkM9","executionInfo":{"status":"ok","timestamp":1741030017165,"user_tz":0,"elapsed":7041,"user":{"displayName":"Simão Quintela","userId":"13164910078562325200"}},"outputId":"309f6802-884a-4887-b3cb-3a1d8af57f48"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/1.14k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"add91e2650f14a3ca81156aea71d861e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e3b23940bca43a5bd25fd9a2cf677a7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"38fb4fd17e5b4bdead971850673ca247"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/856 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"95ccbcf2180d4bb7ae24634dbfe2af13"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/501M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1da5ab56c33846d7b48123fb847e013e"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of LayoutLMv3ForTokenClassification were not initialized from the model checkpoint at microsoft/layoutlmv3-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["def encode_training_example(examples):\n"," images = [Image.open(path).convert(\"RGB\") for path in examples['image_path']]\n","\n"," words = examples['words']\n"," boxes = examples['bboxes']\n"," word_labels = examples['ner_tags']\n","\n"," encoded_inputs = processor(\n"," images, words, boxes=boxes, word_labels=word_labels, padding=\"max_length\", truncation=True\n"," )\n","\n"," print(\"encoded inputs\", encoded_inputs.keys())\n","\n"," return encoded_inputs\n","\n","training_features = Features({\n"," 'pixel_values': Array3D(dtype=\"float32\", shape=(3, 224, 224)),\n"," 'input_ids': Sequence(feature=Value(dtype='int64')),\n"," 'attention_mask': Sequence(Value(dtype='int64')),\n"," 'bbox': Array2D(dtype=\"int64\", shape=(512, 4)),\n"," 'labels': Sequence(ClassLabel(names=id2label)),\n","})\n","\n","def training_dataloader_from_dataset(dataset):\n"," encoded_data = dataset.map(\n"," encode_training_example, batched=True, remove_columns=funsd_dataset['train'].column_names,\n"," features=training_features\n"," )\n"," encoded_data.set_format(type='torch', device=device)\n"," dataloader = torch.utils.data.DataLoader(encoded_data, batch_size=4, shuffle=True)\n"," batch = next(iter(dataloader))\n","\n"," return dataloader\n","train_dataloader = training_dataloader_from_dataset(funsd_dataset['train'])\n","valid_dataloader = training_dataloader_from_dataset(funsd_dataset['test'])"],"metadata":{"id":"rbrQAvShCmTA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Training the model\n","optimizer = AdamW(model.parameters(), lr=5e-5)\n","num_epochs = 200\n","\n","training_loss_history = []\n","validation_loss_history = []\n","\n","saved_models = 0\n","\n","for epoch in range(num_epochs):\n"," print(\"Epoch: \", epoch)\n"," training_loss = 0.0\n"," model.train()\n"," for batch in tqdm(train_dataloader):\n"," outputs = model(**batch)\n"," loss = outputs.loss\n","\n"," training_loss += loss.item()\n","\n"," loss.backward()\n"," optimizer.step()\n"," optimizer.zero_grad()\n"," # Save the model and optimizer state after each epoch\n"," if ((training_loss / batch[\"input_ids\"].shape[0]) < 5) and saved_models < 8:\n"," save_path = f\"/kaggle/working/model_checkpoint_epoch_{epoch}.pt\"\n"," torch.save({\n"," 'epoch': epoch,\n"," 'model_state_dict': model.state_dict(),\n"," 'optimizer_state_dict': optimizer.state_dict(),\n"," 'training_loss': training_loss,\n"," }, save_path)\n"," print(f\"Checkpoint saved: {save_path}\")\n"," saved_models+=1\n","\n"," training_loss_history.append(training_loss)\n"," print(\"Training Loss: \", training_loss / batch[\"input_ids\"].shape[0])\n"," validation_loss = 0.0\n"," for batch in tqdm(valid_dataloader):\n"," outputs = model(**batch)\n"," loss = outputs.loss\n"," validation_loss += loss.item()\n","\n"," print(\"Validation Loss: \", validation_loss)\n"," validation_loss_history.append(validation_loss)\n"],"metadata":{"id":"05_xdowfCoeg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Inference phase"],"metadata":{"id":"the6M-xJCtAl"}},{"cell_type":"code","source":["import torch\n","torch.cuda.empty_cache()\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","model = LayoutLMv3ForTokenClassification.from_pretrained('../Tese/LayoutLMv3_0/')\n","model.to(device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"2cciCSgNCuQT","executionInfo":{"status":"ok","timestamp":1741030033862,"user_tz":0,"elapsed":8031,"user":{"displayName":"Simão Quintela","userId":"13164910078562325200"}},"outputId":"5f90aded-801c-403b-933c-565c89c81c89"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LayoutLMv3ForTokenClassification(\n"," (layoutlmv3): LayoutLMv3Model(\n"," (embeddings): LayoutLMv3TextEmbeddings(\n"," (word_embeddings): Embedding(50265, 768, padding_idx=1)\n"," (token_type_embeddings): Embedding(1, 768)\n"," (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (position_embeddings): Embedding(514, 768, padding_idx=1)\n"," (x_position_embeddings): Embedding(1024, 128)\n"," (y_position_embeddings): Embedding(1024, 128)\n"," (h_position_embeddings): Embedding(1024, 128)\n"," (w_position_embeddings): Embedding(1024, 128)\n"," )\n"," (patch_embed): LayoutLMv3PatchEmbeddings(\n"," (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n"," )\n"," (pos_drop): Dropout(p=0.0, inplace=False)\n"," (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n"," (encoder): LayoutLMv3Encoder(\n"," (layer): ModuleList(\n"," (0-11): 12 x LayoutLMv3Layer(\n"," (attention): LayoutLMv3Attention(\n"," (self): LayoutLMv3SelfAttention(\n"," (query): Linear(in_features=768, out_features=768, bias=True)\n"," (key): Linear(in_features=768, out_features=768, bias=True)\n"," (value): Linear(in_features=768, out_features=768, bias=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (output): LayoutLMv3SelfOutput(\n"," (dense): Linear(in_features=768, out_features=768, bias=True)\n"," (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," (intermediate): LayoutLMv3Intermediate(\n"," (dense): Linear(in_features=768, out_features=3072, bias=True)\n"," (intermediate_act_fn): GELUActivation()\n"," )\n"," (output): LayoutLMv3Output(\n"," (dense): Linear(in_features=3072, out_features=768, bias=True)\n"," (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (rel_pos_bias): Linear(in_features=32, out_features=12, bias=False)\n"," (rel_pos_x_bias): Linear(in_features=64, out_features=12, bias=False)\n"," (rel_pos_y_bias): Linear(in_features=64, out_features=12, bias=False)\n"," )\n"," )\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (classifier): Linear(in_features=768, out_features=7, bias=True)\n",")"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["\n","from pprint import pprint\n","def unnormalize_box(bbox, width, height):\n"," return [\n"," width * (bbox[0] / 1000),\n"," height * (bbox[1] / 1000),\n"," width * (bbox[2] / 1000),\n"," height * (bbox[3] / 1000),\n"," ]\n","\n","def iob_to_label(label):\n"," label = label[2:]\n"," if not label:\n"," return 'other'\n"," return label\n","\n","def inference(apply_ocr=False, save=False):\n"," for i, image_ocr in enumerate(funsd_dataset['test']):\n"," image = Image.open(image_ocr['image_path'])\n"," image = image.convert(\"RGB\")\n"," width, height = image.size\n","\n"," if apply_ocr:\n"," encoded_inputs = processor(\n"," image,\n"," padding=\"max_length\", truncation=True, return_tensors=\"pt\", return_token_type_ids=True\n"," ).to(device)\n"," else:\n"," encoded_inputs = processor(\n"," image, image_ocr['words'], boxes=image_ocr['bboxes'], word_labels=image_ocr['ner_tags'],\n"," padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n"," ).to(device)\n","\n"," # Decode the words so it is easier to debug\n"," input_ids = encoded_inputs.input_ids\n"," words = processor.tokenizer.decode(input_ids[0], skip_special_tokens=False).split()\n","\n"," # Inference\n"," outputs = model(**encoded_inputs)\n"," predictions = outputs.logits.argmax(-1).squeeze().tolist()\n"," token_boxes = encoded_inputs.bbox.squeeze().tolist()\n","\n"," true_predictions = [id2label[prediction] for prediction in predictions]\n"," true_boxes = [unnormalize_box(box, width, height) for box in token_boxes]\n"," draw = ImageDraw.Draw(image)\n","\n"," font = ImageFont.load_default()\n"," label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}\n","\n"," # Draw the output\n"," for prediction, box in zip(true_predictions, true_boxes):\n"," predicted_label = iob_to_label(prediction).lower()\n"," draw.rectangle(box, outline=label2color[predicted_label])\n"," draw.text(\n"," (box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font\n"," )\n","\n"," # Save prediction in drive\n"," if save:\n"," if apply_ocr:\n"," save_path = f'../Tese/LayoutLMv3_0/TestDataWithOcr/funds_test_{i}.png'\n"," else:\n"," save_path = f'../Tese/LayoutLMv3_0/TestDataWithoutOcr/funds_test_{i}.png'\n","\n"," os.makedirs(os.path.dirname(save_path), exist_ok=True)\n"," image.save(save_path)\n","\n","\n","\n","#ocrless_inference()\n","inference(apply_ocr=True, save=False)"],"metadata":{"collapsed":true,"id":"jv5ieoYdFKUZ"},"execution_count":null,"outputs":[]}]}