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@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-01.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-02.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-03.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-04.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-05.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-06.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-07.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-08.png filter=lfs diff=lfs merge=lfs -text
+example_images/splicing-11.png filter=lfs diff=lfs merge=lfs -text

demo.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "from transformers import AutoModel\n",
+    "from torchvision.transforms.functional import to_pil_image, pil_to_tensor\n",
+    "from torchmetrics.classification import BinaryF1Score, BinaryAveragePrecision\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoModel.from_pretrained(\"ductai199x/forensic-similarity-graph\", trust_remote_code=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = model.eval().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_paths = sorted(glob.glob(\"example_images/splicing-??.png\"))\n",
+    "gt_paths = sorted(glob.glob(\"example_images/splicing-??-gt.png\"))\n",
+    "image_vs_gt_paths = list(zip(image_paths, gt_paths))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    imgs = []\n",
+    "    gts = []\n",
+    "    img_preds = []\n",
+    "    loc_preds = []\n",
+    "    f1, mAP = BinaryF1Score(), BinaryAveragePrecision()\n",
+    "    for image_path, gt_path in tqdm(image_vs_gt_paths):\n",
+    "        image = pil_to_tensor(Image.open(image_path).convert(\"RGB\")).float() / 255\n",
+    "        gt = ((pil_to_tensor(Image.open(gt_path).convert(\"L\")).float() / 255) < 0.9).int()\n",
+    "        img_pred, loc_pred = model(image.unsqueeze(0).to(device))\n",
+    "        img_pred, loc_pred = img_pred[0].cpu(), loc_pred[0].cpu()\n",
+    "        f1.update(loc_pred[None, ...], gt)\n",
+    "        mAP.update(loc_pred[None, ...], gt)\n",
+    "        img_preds.append(img_pred)\n",
+    "        loc_preds.append(loc_pred)\n",
+    "        imgs.append(image)\n",
+    "        gts.append(gt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f1.compute().item(), mAP.compute().item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col = 4 * 2\n",
+    "row = -(-len(image_vs_gt_paths) // 4)\n",
+    "fig, axs = plt.subplots(row, col)\n",
+    "fig.set_size_inches(3 * col, 3 * row)\n",
+    "for i, (img, gt, img_pred, loc_pred) in enumerate(zip(imgs, gts, img_preds, loc_preds)):\n",
+    "    ax = axs[i // 4][(i % 4) * 2]\n",
+    "    ax.imshow(to_pil_image(img))\n",
+    "    ax = axs[i // 4][(i % 4) * 2 + 1]\n",
+    "    ax.imshow(to_pil_image(gt.float()))\n",
+    "    ax.imshow(loc_pred, alpha=0.5, cmap=\"coolwarm\")\n",
+    "\n",
+    "for ax in axs.flat:\n",
+    "    ax.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyt_tf2",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}