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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader\n",
"# from transformers import SegformerConfig\n",
"# from surya.model.detection.segformer import SegformerForRegressionMask\n",
"from surya.input.processing import prepare_image_detection\n",
"from surya.model.detection.segformer import load_processor , load_model\n",
"from datasets import load_dataset\n",
"from tqdm import tqdm\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"import torch.nn.functional as F\n",
"import numpy as np \n",
"from surya.layout import parallel_get_regions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initializing The Dataset And Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"dataset = load_dataset(\"vikp/publaynet_bench\", split=\"train[:100]\") # You can choose you own dataset\n",
"model = load_model(\"vikp/surya_layout2\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Helper Functions, Loss Function And Optimizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"optimizer = optim.Adam(model.parameters(), lr=0.00001)\n",
"log_dir = \"logs\"\n",
"checkpoint_dir = \"checkpoints\"\n",
"os.makedirs(log_dir, exist_ok=True)\n",
"os.makedirs(checkpoint_dir, exist_ok=True)\n",
"writer = SummaryWriter(log_dir=log_dir)\n",
"\n",
"def logits_to_bboxes(logits,image) : # This function is useful for converting the logits(mask) into bounding boxes.(The model does not provide bounding boxes.)\n",
" correct_shape = (300, 300) \n",
" logits_temp = F.interpolate(logits, size=correct_shape, mode='bilinear', align_corners=False)\n",
" logits_temp = logits_temp.cpu().detach().numpy().astype(np.float32)\n",
"\n",
" heatmap_count = logits_temp.shape[1]\n",
" heatmaps = [logits_temp[i][k] for i in range(logits_temp.shape[0]) for k in range(heatmap_count)]\n",
" regions = parallel_get_regions(heatmaps=heatmaps, orig_size=image.size, id2label=model.config.id2label)\n",
"\n",
" final_bboxes = []\n",
" for i in regions.bboxes :\n",
" final_bboxes.append(i.bbox)\n",
" return final_bboxes\n",
"\n",
"\n",
"def loss_function(): # This model does not have inbuild loss function, So we have to define it according to our dataset and the Requirements.\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fine-Tuning Process"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_epochs = 5\n",
"for epoch in range(num_epochs):\n",
" model.train()\n",
" running_loss = 0.0\n",
" avg_loss = 0.0\n",
"\n",
" for idx, item in enumerate(tqdm(dataset, desc=f\"Epoch {epoch + 1}/{num_epochs}\")):\n",
"\n",
" images = [prepare_image_detection(img=item['image'], processor=load_processor())]\n",
" images = torch.stack(images, dim=0).to(model.dtype).to(model.device)\n",
" \n",
" optimizer.zero_grad()\n",
" outputs = model(pixel_values=images)\n",
"\n",
" predicted_boxes = logits_to_bboxes(outputs.logits, item['image'])\n",
" target_boxes = item['bboxes']\n",
"\n",
" loss = loss_function(predicted_boxes,target_boxes)\n",
"\n",
" loss.backward()\n",
" optimizer.step()\n",
" running_loss += loss.item()\n",
"\n",
" avg_loss = 0.9 * avg_loss + 0.1 * loss.item() if idx > 0 else loss.item()\n",
"\n",
" avg_loss = running_loss / len(dataset)\n",
" writer.add_scalar('Training Loss', avg_loss, epoch + 1)\n",
" print(f\"Average Loss for Epoch {epoch + 1}: {avg_loss:.4f}\")\n",
"\n",
" torch.save(model.state_dict(), os.path.join(checkpoint_dir, f\"model_epoch_{epoch + 1}.pth\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading The Checkpoint "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"checkpoint_path = 'checkpoints/model_epoch_350.pth' \n",
"state_dict = torch.load(checkpoint_path,weights_only=True)\n",
"\n",
"model.load_state_dict(state_dict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.to('cpu')\n",
"model.save_pretrained(\"fine-tuned-surya-model-layout\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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