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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['HF_HOME'] = '/data2/ketan/orc/HF_Cache'\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader\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\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initializing The Dataset And Model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda:3\" if torch.cuda.is_available() else \"cpu\")\n",
"dataset = load_dataset(\"vikp/publaynet_bench\", split=\"train[:100]\") # You can choose you own dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded detection model vikp/surya_layout2 on device cuda with dtype torch.float16\n"
]
},
{
"data": {
"text/plain": [
"'.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = load_model(\"vikp/surya_layout2\").to(device)\n",
"model.to(torch.float32)\n",
"\".\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Helper Functions, Loss Function And Optimizer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"optimizer = optim.Adam(model.parameters(), lr=1e-4)\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)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def logits_to_mask(logits, labels, bboxes, original_size=(1200, 1200)):\n",
" batch_size, num_classes, height, width = logits.shape\n",
" mask = torch.zeros((batch_size, num_classes, height, width), dtype=torch.float32).to(logits.device)\n",
"\n",
" for bbox, class_id in zip(bboxes, labels):\n",
" x_min, y_min, x_max, y_max = bbox\n",
"\n",
" x_min = int(x_min * width / original_size[0])\n",
" y_min = int(y_min * height / original_size[1])\n",
" x_max = int(x_max * width / original_size[0])\n",
" y_max = int(y_max * height / original_size[1])\n",
"\n",
" x_min = max(0, min(x_min, width - 1))\n",
" y_min = max(0, min(y_min, height - 1))\n",
" x_max = max(0, min(x_max, width - 1))\n",
" y_max = max(0, min(y_max, height - 1))\n",
"\n",
" if x_min < x_max and y_min < y_max:\n",
" mask[:, class_id, y_min:y_max, x_min:x_max] = torch.maximum(\n",
" mask[:, class_id, y_min:y_max, x_min:x_max], torch.tensor(1.0).to(logits.device)\n",
" )\n",
" else:\n",
" print(f\"Invalid bounding box after adjustment: {bbox}, adjusted to: {(x_min, y_min, x_max, y_max)}\")\n",
"\n",
" return mask\n",
"\n",
"\n",
"def loss_function(logits, mask):\n",
" loss_fn = torch.nn.CrossEntropyLoss() \n",
" loss = loss_fn(logits, mask)\n",
" return loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fine-Tuning Process"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 1/5: 100%|██████████| 100/100 [01:46<00:00, 1.07s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Loss for Epoch 1: 0.3322\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 2/5: 100%|██████████| 100/100 [01:51<00:00, 1.11s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Loss for Epoch 2: 0.3311\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 3/5: 100%|██████████| 100/100 [01:51<00:00, 1.12s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Loss for Epoch 3: 0.3197\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 4/5: 100%|██████████| 100/100 [01:42<00:00, 1.03s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Loss for Epoch 4: 0.3106\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 5/5: 100%|██████████| 100/100 [01:46<00:00, 1.06s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Loss for Epoch 5: 0.3160\n"
]
}
],
"source": [
"num_epochs = 5\n",
"\n",
"for param in model.parameters():\n",
" param.requires_grad = True\n",
"\n",
"\n",
"model.train()\n",
"with torch.autograd.set_detect_anomaly(True):\n",
"\n",
" for epoch in range(num_epochs):\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",
" 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",
"\n",
" logits = outputs.logits\n",
"\n",
" bboxes = item['bboxes']\n",
" labels = item['category_ids']\n",
" mask = logits_to_mask(logits, labels, bboxes)\n",
"\n",
" logits = logits.to(torch.float32)\n",
" mask = mask.to(torch.float32)\n",
" loss = loss_function(logits, mask)\n",
"\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" avg_loss = 0.9 * avg_loss + 0.1 * loss.item() if idx > 0 else loss.item()\n",
"\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\"))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading The Checkpoint "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"checkpoint_path = '/data2/ketan/orc/surya-layout-fine-tune/checkpoints/model_epoch_5.pth' \n",
"state_dict = torch.load(checkpoint_path,weights_only=True)\n",
"\n",
"model.load_state_dict(state_dict)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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",
"version": "3.10.14"
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"nbformat": 4,
"nbformat_minor": 2
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