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"}}},{"cell_type":"markdown","source":"**Understanding Problem and Collecting the data**","metadata":{}},{"cell_type":"code","source":"import os\nimport numpy as np\nfrom PIL import Image\n\nbase = \"/kaggle/input/recodai-luc-scientific-image-forgery-detection\"\n\npaths = {\n    \"authentic\": os.path.join(base, \"train_images/authentic\"),\n    \"forged\": os.path.join(base, \"train_images/forged\"),\n    \"masks\": os.path.join(base, \"train_masks\"),\n    \"supp_imgs\": os.path.join(base, \"supplemental_images\"),\n    \"supp_masks\": os.path.join(base, \"supplemental_masks\"),\n    \"test\": os.path.join(base, \"test_images\"),\n}\n\n# Counting no of images\nnum_authentic = len(os.listdir(paths[\"authentic\"]))\nnum_forged = len(os.listdir(paths[\"forged\"]))\nnum_masks = len(os.listdir(paths[\"masks\"]))\n\nprint(\"Authentic images:\", num_authentic)\nprint(\"Forged images:\", num_forged)\nprint(\"Mask files:\", num_masks)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:23.614889Z","iopub.execute_input":"2025-12-10T09:24:23.615730Z","iopub.status.idle":"2025-12-10T09:24:23.748262Z","shell.execute_reply.started":"2025-12-10T09:24:23.615699Z","shell.execute_reply":"2025-12-10T09:24:23.747554Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Checking filename matching\nforged_files = sorted(os.listdir(paths[\"forged\"]))\nmask_files = sorted(os.listdir(paths[\"masks\"]))\n\nfor i in range(6):\n    print(f\"Forged: {forged_files[i]}  -> Mask: {mask_files[i]}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:23.749441Z","iopub.execute_input":"2025-12-10T09:24:23.750051Z","iopub.status.idle":"2025-12-10T09:24:23.758280Z","shell.execute_reply.started":"2025-12-10T09:24:23.750025Z","shell.execute_reply":"2025-12-10T09:24:23.757641Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Analyzing mask shapes & values\nsample_mask_path_0= os.path.join(paths[\"masks\"], mask_files[0])\nsample_mask_path_1 = os.path.join(paths[\"masks\"], mask_files[1])\nsample_mask_path_2 = os.path.join(paths[\"masks\"], mask_files[2])\nmask_0 = np.load(sample_mask_path_0)\nmask_1 = np.load(sample_mask_path_1)\nmask_2 = np.load(sample_mask_path_2)\n\nprint(\"Sample mask shape of First mask:\", mask_0.shape)\nprint(\"Sample mask shape of Second mask:\", mask_1.shape)\nprint(\"Sample mask shape of Third mask:\", mask_2.shape)\nprint(\"dtype:\", mask_0.dtype)\nprint(\"dtype:\", mask_1.dtype)\nprint(\"Unique values:\", np.unique(mask_0)[:10])\nprint(\"Unique values:\", np.unique(mask_1)[:10])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:23.758901Z","iopub.execute_input":"2025-12-10T09:24:23.759099Z","iopub.status.idle":"2025-12-10T09:24:23.874115Z","shell.execute_reply.started":"2025-12-10T09:24:23.759084Z","shell.execute_reply":"2025-12-10T09:24:23.873526Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Checking shape mismatches\nbad_shapes = []\nfor mf in mask_files:\n    m = np.load(os.path.join(paths[\"masks\"], mf))\n    if not (m.ndim == 2 or (m.ndim == 3 and 1 in m.shape)):\n        bad_shapes.append((mf, m.shape))\n\nprint(\"Masks with unusual shapes:\", bad_shapes[:10])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:23.874724Z","iopub.execute_input":"2025-12-10T09:24:23.874986Z","iopub.status.idle":"2025-12-10T09:24:53.779022Z","shell.execute_reply.started":"2025-12-10T09:24:23.874962Z","shell.execute_reply":"2025-12-10T09:24:53.778375Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Checking a few forged image shapes\nsizes = []\nfor img_name in forged_files[:10]:\n    img = Image.open(os.path.join(paths[\"forged\"], img_name))\n    sizes.append(img.size)  # (W, H)\n\nprint(\"Forged image sample sizes:\", sizes)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:53.780842Z","iopub.execute_input":"2025-12-10T09:24:53.781128Z","iopub.status.idle":"2025-12-10T09:24:53.907452Z","shell.execute_reply.started":"2025-12-10T09:24:53.781109Z","shell.execute_reply":"2025-12-10T09:24:53.906920Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Checking a few authentic image shapes\nauthentic_files = sorted(os.listdir(paths[\"authentic\"]))\nsizes = []\nfor img_name in authentic_files[:10]:\n    img = Image.open(os.path.join(paths[\"authentic\"], img_name))\n    sizes.append(img.size)  # (W, H)\n\nprint(\"authentic image sample sizes:\", sizes)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:53.908144Z","iopub.execute_input":"2025-12-10T09:24:53.908337Z","iopub.status.idle":"2025-12-10T09:24:53.987720Z","shell.execute_reply.started":"2025-12-10T09:24:53.908322Z","shell.execute_reply":"2025-12-10T09:24:53.987215Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\n\ndef load_mask(mask_path):\n    \"\"\"\n    Loads a mask and returns a list of (H,W) binary masks.\n    Handles:\n    - (1,H,W)  -> 1 instance\n    - (C,H,W)  -> C instances\n    \"\"\"\n    m = np.load(mask_path)\n\n    # ensure uint8 binary\n    m = (m > 0).astype(np.uint8)\n\n    # case: (H,W)\n    if m.ndim == 2:\n        return [m]\n\n    # case: (C,H,W)\n    if m.ndim == 3:\n        inst_list = []\n        for c in range(m.shape[0]):\n            inst = m[c]\n            if inst.sum() > 0:\n                inst_list.append(inst)\n        return inst_list\n\n    raise ValueError(f\"Unexpected mask shape: {m.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:53.988418Z","iopub.execute_input":"2025-12-10T09:24:53.988616Z","iopub.status.idle":"2025-12-10T09:24:53.993704Z","shell.execute_reply.started":"2025-12-10T09:24:53.988600Z","shell.execute_reply":"2025-12-10T09:24:53.992903Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from pathlib import Path\n\nmask_dir = Path(\"/kaggle/input/recodai-luc-scientific-image-forgery-detection/train_masks\")\n\ntest_files = [\"10.npy\", \"10070.npy\", \"10139.npy\"]\n\nfor f in test_files:\n    insts = load_mask(mask_dir / f)\n    print(f, \"instances:\", len(insts))\n    for i, m in enumerate(insts):\n        print(\"  shape:\", m.shape, \"pixels:\", m.sum())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:53.994585Z","iopub.execute_input":"2025-12-10T09:24:53.994800Z","iopub.status.idle":"2025-12-10T09:24:54.023635Z","shell.execute_reply.started":"2025-12-10T09:24:53.994785Z","shell.execute_reply":"2025-12-10T09:24:54.023105Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Data Cleaning and Preparing the data**","metadata":{}},{"cell_type":"code","source":"# Creating stratified train/val CSVs for Recod.ai LUC dataset\nimport os\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nfrom sklearn.model_selection import StratifiedShuffleSplit\n\nBASE = Path(\"/kaggle/input/recodai-luc-scientific-image-forgery-detection\")\nAUTH_DIR = BASE / \"train_images\" / \"authentic\"\nFORG_DIR = BASE / \"train_images\" / \"forged\"\nMASK_DIR = BASE / \"train_masks\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:54.024320Z","iopub.execute_input":"2025-12-10T09:24:54.024991Z","iopub.status.idle":"2025-12-10T09:24:54.644751Z","shell.execute_reply.started":"2025-12-10T09:24:54.024966Z","shell.execute_reply":"2025-12-10T09:24:54.643809Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def load_mask_list(mask_path: Path):\n    m = np.load(mask_path)\n    m = (m > 0).astype(np.uint8)\n    if m.ndim == 2:\n        insts = [m]\n    elif m.ndim == 3:\n        insts = [m[c] for c in range(m.shape[0]) if m[c].sum() > 0]\n    else:\n        raise ValueError(f\"Unexpected mask shape: {m.shape}\")\n    return insts","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:54.645827Z","iopub.execute_input":"2025-12-10T09:24:54.646223Z","iopub.status.idle":"2025-12-10T09:24:54.650744Z","shell.execute_reply.started":"2025-12-10T09:24:54.646200Z","shell.execute_reply":"2025-12-10T09:24:54.650085Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"rows = []\n# Authentic images (no masks)\n\nfor p in sorted(AUTH_DIR.iterdir()):\n    if p.suffix.lower() not in (\".png\", \".jpg\", \".jpeg\", \".tif\", \".tiff\"):\n        continue\n    with Image.open(p) as im:\n        w, h = im.size\n    rows.append({\n        \"case_id\": p.stem,\n        \"image_path\": str(p),\n        \"is_forged\": 0,\n        \"mask_path\": \"\",\n        \"num_instances\": 0,\n        \"mask_pixels\": 0,\n        \"shape\": json.dumps([h, w])\n    })\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:24:54.651504Z","iopub.execute_input":"2025-12-10T09:24:54.651692Z","iopub.status.idle":"2025-12-10T09:25:13.316419Z","shell.execute_reply.started":"2025-12-10T09:24:54.651677Z","shell.execute_reply":"2025-12-10T09:25:13.315857Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Forged images (masks exist)\nfor p in sorted(FORG_DIR.iterdir()):\n    if p.suffix.lower() not in (\".png\", \".jpg\", \".jpeg\", \".tif\", \".tiff\"):\n        continue\n    mask_file = MASK_DIR / f\"{p.stem}.npy\"\n    if not mask_file.exists():\n        # if no matching mask, warn and skip\n        print(\"Warning: missing mask for\", p.name)\n        continue\n    with Image.open(p) as im:\n        w, h = im.size\n    insts = load_mask_list(mask_file)\n    mask_pixels = sum(int(x.sum()) for x in insts)\n    rows.append({\n        \"case_id\": p.stem,\n        \"image_path\": str(p),\n        \"is_forged\": 1,\n        \"mask_path\": str(mask_file),\n        \"num_instances\": len(insts),\n        \"mask_pixels\": mask_pixels,\n        \"shape\": json.dumps([h, w])\n    })","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:13.317119Z","iopub.execute_input":"2025-12-10T09:25:13.317326Z","iopub.status.idle":"2025-12-10T09:25:51.395188Z","shell.execute_reply.started":"2025-12-10T09:25:13.317306Z","shell.execute_reply":"2025-12-10T09:25:51.394589Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ndf = pd.DataFrame(rows)\ndf = df.sample(frac=1, random_state=42).reset_index(drop=True)  # shuffle\n\n# compute mask_fraction (0 for authentic)\ndf[\"area\"] = df[\"shape\"].apply(lambda s: json.loads(s)[0] * json.loads(s)[1])\ndf[\"mask_fraction\"] = df[\"mask_pixels\"] / df[\"area\"]\n\n# create mask fraction bins for stratification\n# small epsilon to avoid zero-division; authentic images will be in bin 0\ndf[\"mask_frac_bin\"] = pd.qcut(df[\"mask_fraction\"] + 1e-12, q=10, duplicates=\"drop\", labels=False)\n\n# stratify key: combine is_forged and mask_frac_bin to preserve both class balance and mask-size distribution\ndf[\"strata\"] = df[\"is_forged\"].astype(str) + \"_\" + df[\"mask_frac_bin\"].astype(str)\n\nprint(\"Dataset size:\", len(df))\nprint(\"Forged / Authentic:\", df[\"is_forged\"].value_counts().to_dict())\nprint(\"Strata counts (sample):\")\nprint(df[\"strata\"].value_counts())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:51.395883Z","iopub.execute_input":"2025-12-10T09:25:51.396127Z","iopub.status.idle":"2025-12-10T09:25:51.493658Z","shell.execute_reply.started":"2025-12-10T09:25:51.396110Z","shell.execute_reply":"2025-12-10T09:25:51.493100Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Stratified split (80% train / 20% val)\nsss = StratifiedShuffleSplit(n_splits=1, test_size=0.20, random_state=12345)\ntrain_idx, val_idx = next(sss.split(df, df[\"strata\"]))\n\ntrain_df = df.iloc[train_idx].reset_index(drop=True)\nval_df = df.iloc[val_idx].reset_index(drop=True)\n\nprint(\"Train size:\", len(train_df), \"Val size:\", len(val_df))\nprint(\"Train forged/authentic:\", train_df[\"is_forged\"].value_counts().to_dict())\nprint(\"Val forged/authentic:\", val_df[\"is_forged\"].value_counts().to_dict())\n\n# Save csvs\nOUT_DIR = Path(\"/kaggle/working\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\ntrain_csv = OUT_DIR / \"train_split.csv\"\nval_csv = OUT_DIR / \"val_split.csv\"\ntrain_df.to_csv(train_csv, index=False)\nval_df.to_csv(val_csv, index=False)\n\nprint(\"Saved:\", train_csv, val_csv)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:51.496179Z","iopub.execute_input":"2025-12-10T09:25:51.496382Z","iopub.status.idle":"2025-12-10T09:25:51.559180Z","shell.execute_reply.started":"2025-12-10T09:25:51.496366Z","shell.execute_reply":"2025-12-10T09:25:51.558645Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\ntrain_df = pd.read_csv(\"/kaggle/working/train_split.csv\")\ntrain_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:51.559789Z","iopub.execute_input":"2025-12-10T09:25:51.560010Z","iopub.status.idle":"2025-12-10T09:25:51.598469Z","shell.execute_reply.started":"2025-12-10T09:25:51.559992Z","shell.execute_reply":"2025-12-10T09:25:51.597664Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"val_df = pd.read_csv(\"/kaggle/working/val_split.csv\")\nval_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:51.599284Z","iopub.execute_input":"2025-12-10T09:25:51.599610Z","iopub.status.idle":"2025-12-10T09:25:51.613441Z","shell.execute_reply.started":"2025-12-10T09:25:51.599586Z","shell.execute_reply":"2025-12-10T09:25:51.612873Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\ntrain_df = pd.read_csv(\"/kaggle/working/train_split.csv\")\nval_df   = pd.read_csv(\"/kaggle/working/val_split.csv\")\n\nprint(\"=== TRAIN STRATA COUNTS ===\")\nprint(train_df[\"strata\"].value_counts().sort_index())\n\nprint(\"\\n=== VAL STRATA COUNTS ===\")\nprint(val_df[\"strata\"].value_counts().sort_index())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:51.614242Z","iopub.execute_input":"2025-12-10T09:25:51.614491Z","iopub.status.idle":"2025-12-10T09:25:51.639968Z","shell.execute_reply.started":"2025-12-10T09:25:51.614471Z","shell.execute_reply":"2025-12-10T09:25:51.639448Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.figure(figsize=(12,5))\ntrain_df[\"strata\"].value_counts().sort_index().plot(kind=\"bar\", color=\"blue\", alpha=0.7)\nplt.title(\"Train Strata Distribution\")\nplt.show()\n\nplt.figure(figsize=(12,5))\nval_df[\"strata\"].value_counts().sort_index().plot(kind=\"bar\", color=\"orange\", alpha=0.7)\nplt.title(\"Validation Strata Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:51.640589Z","iopub.execute_input":"2025-12-10T09:25:51.640762Z","iopub.status.idle":"2025-12-10T09:25:52.079404Z","shell.execute_reply.started":"2025-12-10T09:25:51.640749Z","shell.execute_reply":"2025-12-10T09:25:52.078801Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Step 4: Dataset class, transforms, collate, and DataLoader preview.","metadata":{}},{"cell_type":"code","source":"import os, json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport cv2\nimport matplotlib.pyplot as plt\n\nbase_dir = Path(\"/kaggle/input/recodai-luc-scientific-image-forgery-detection\")\ntrain_csv = Path(\"/kaggle/working/train_split.csv\")\nval_csv = Path(\"/kaggle/working/val_split.csv\")\n\ndef load_mask_list_np(mask_path: Path):\n    a = np.load(mask_path)\n    a = (a > 0).astype(np.uint8)\n    if a.ndim == 2:\n        return [a]\n    if a.ndim == 3:\n        return [a[c].astype(np.uint8) for c in range(a.shape[0]) if a[c].sum() > 0]\n    raise ValueError(str(a.shape))\n\nclass ImageMaskDataset(Dataset):\n    def __init__(self, df, out_size=(512,512), augment=False):\n        self.df = df.reset_index(drop=True)\n        self.out_size = out_size\n        if augment:\n            self.tf = A.Compose([\n                A.Resize(out_size[0], out_size[1]),\n                A.HorizontalFlip(p=0.5),\n                A.VerticalFlip(p=0.2),\n                A.Rotate(limit=30, p=0.5),\n                A.RandomBrightnessContrast(p=0.5),\n                A.OneOf([A.GaussNoise(p=0.3), A.GaussianBlur(p=0.3)], p=0.3),\n                A.Normalize(),\n                ToTensorV2()\n            ])\n        else:\n            self.tf = A.Compose([\n                A.Resize(out_size[0], out_size[1]),\n                A.Normalize(),\n                ToTensorV2()\n            ])\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, idx):\n        row = self.df.iloc[idx]\n        img = np.array(Image.open(row[\"image_path\"]).convert(\"RGB\"))\n        h0, w0 = json.loads(row[\"shape\"])\n        if int(row[\"is_forged\"]) == 1:\n            insts = load_mask_list_np(Path(row[\"mask_path\"]))\n            combined = np.zeros((h0, w0), dtype=np.uint8)\n            for m in insts:\n                combined |= (m > 0).astype(np.uint8)\n        else:\n            combined = np.zeros((h0, w0), dtype=np.uint8)\n\n        aug = self.tf(image=img, mask=combined)\n\n        # Augmented image tensor (ToTensorV2 gives torch.Tensor)\n        img_t = aug[\"image\"]\n\n        # Augmented mask may be torch.Tensor or numpy array depending on ToTensorV2;\n        # convert robustly to numpy and ensure binary uint8\n        mask_aug = aug[\"mask\"]\n        if isinstance(mask_aug, torch.Tensor):\n            aug_mask_np = mask_aug.cpu().numpy()\n        else:\n            aug_mask_np = np.array(mask_aug)\n\n        aug_mask_np = (aug_mask_np > 0).astype(np.uint8)\n        mask_t = torch.tensor(aug_mask_np).unsqueeze(0).float()\n\n        # extract instance masks from augmented combined mask (already at out_size)\n        inst_masks_out = []\n        n, labels = cv2.connectedComponents(aug_mask_np, connectivity=8)\n        for lab in range(1, n):\n            m = (labels == lab).astype(np.uint8)\n            inst_masks_out.append(m)\n\n        return {\n            \"image\": img_t,\n            \"mask\": mask_t,\n            \"inst_masks\": inst_masks_out,\n            \"case_id\": row[\"case_id\"],\n            \"is_forged\": int(row[\"is_forged\"])\n        }\n\ndef collate_for_batch(batch):\n    imgs = torch.stack([b[\"image\"] for b in batch], dim=0)\n    masks = torch.stack([b[\"mask\"] for b in batch], dim=0)\n    insts = [b[\"inst_masks\"] for b in batch]\n    ids = [b[\"case_id\"] for b in batch]\n    forged_flags = torch.tensor([b[\"is_forged\"] for b in batch], dtype=torch.uint8)\n    return {\"images\": imgs, \"masks\": masks, \"insts\": insts, \"case_ids\": ids, \"is_forged\": forged_flags}\n\nprint(\"loading csvs...\")\ntrain_df = pd.read_csv(train_csv)\nval_df = pd.read_csv(val_csv)\nprint(\"train:\", len(train_df), \"val:\", len(val_df))\n\ntrain_ds = ImageMaskDataset(train_df, out_size=(512,512), augment=True)\nval_ds = ImageMaskDataset(val_df, out_size=(512,512), augment=False)\n\ntrain_loader = DataLoader(train_ds, batch_size=2, shuffle=True, collate_fn=collate_for_batch, num_workers=0, pin_memory=True)\nval_loader = DataLoader(val_ds, batch_size=2, shuffle=False, collate_fn=collate_for_batch, num_workers=0, pin_memory=True)\n\nbatch = next(iter(train_loader))\nprint(\"images:\", batch[\"images\"].shape)\nprint(\"masks:\", batch[\"masks\"].shape)\nprint(\"inst-lists:\", len(batch[\"insts\"]))\nprint(\"first id:\", batch[\"case_ids\"][0], \"is_forged:\", int(batch[\"is_forged\"][0]))\nprint(\"inst count sample0:\", len(batch[\"insts\"][0]))\nprint(\"mask pixels sample0:\", int(batch[\"masks\"][0].sum().item()))\n\n# quick visual alignment check for first sample\nimg_t = batch[\"images\"][0].permute(1,2,0).cpu().numpy()\nimg_t = (img_t * np.array([0.229,0.224,0.225]) + np.array([0.485,0.456,0.406]))\nimg_t = np.clip(img_t, 0, 1)\nimg_disp = (img_t * 255).astype(np.uint8)\n\nmask_np = batch[\"masks\"][0].squeeze(0).cpu().numpy().astype(np.uint8)\noverlay = img_disp.copy()\noverlay[mask_np==1] = (255,0,0)\n\nplt.figure(figsize=(12,6))\nplt.subplot(1,3,1); plt.imshow(img_disp); plt.title(\"image\"); plt.axis(\"off\")\nplt.subplot(1,3,2); plt.imshow(mask_np, cmap=\"gray\"); plt.title(\"combined mask\"); plt.axis(\"off\")\nplt.subplot(1,3,3); plt.imshow(overlay); plt.title(\"overlay\"); plt.axis(\"off\")\nplt.tight_layout()\nplt.show()\n\nif len(batch[\"insts\"][0]) > 0:\n    n = len(batch[\"insts\"][0])\n    cols = min(n, 5)\n    rows = (n + cols - 1)//cols\n    plt.figure(figsize=(4*cols, 3*rows))\n    for i, m in enumerate(batch[\"insts\"][0]):\n        plt.subplot(rows, cols, i+1)\n        plt.imshow(m, cmap=\"gray\"); plt.title(f\"inst {i} pixels={m.sum()}\"); plt.axis(\"off\")\n    plt.tight_layout()\n    plt.show()\nelse:\n    print(\"no instance masks in first sample\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:25:52.080142Z","iopub.execute_input":"2025-12-10T09:25:52.080442Z","iopub.status.idle":"2025-12-10T09:26:26.706929Z","shell.execute_reply.started":"2025-12-10T09:25:52.080425Z","shell.execute_reply":"2025-12-10T09:26:26.706340Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Alignment checker: compares combined mask vs union(inst_masks) and visualizes mismatches\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nfrom pathlib import Path\nimport pandas as pd\nfrom PIL import Image\n\nUSE_LOADER = True   # set False to recreate dataset locally (safer if loader not present)\nSAMPLE_LIMIT = 500  # how many samples to check (set None to check all)\nSHOW_MAX = 8        # how many mismatch examples to visualize\nMISMATCH_PIXEL_THRESHOLD = 1  # treat any non-zero pixel diff as mismatch; raise it to e.g. 5 for tolerance\n\nchecked = 0\nmismatch_examples = []\ntotal = 0\nok_count = 0\nforged_flag_violations = 0\nshape_violations = 0\n\ndef visualize_mismatch(img_np, mask_np, union_np, diff_np, case_id, info):\n    plt.figure(figsize=(12,6))\n    plt.subplot(1,4,1); plt.imshow(img_np); plt.title(f\"Image: {case_id}\"); plt.axis(\"off\")\n    plt.subplot(1,4,2); plt.imshow(mask_np, cmap=\"gray\"); plt.title(\"dataset combined mask\"); plt.axis(\"off\")\n    plt.subplot(1,4,3); plt.imshow(union_np, cmap=\"gray\"); plt.title(\"union(inst_masks)\"); plt.axis(\"off\")\n    plt.subplot(1,4,4); plt.imshow(diff_np, cmap=\"hot\"); plt.title(f\"diff (xor) | {info}\"); plt.axis(\"off\")\n    plt.tight_layout()\n    plt.show()\n\n# Try to use existing train_loader\nif USE_LOADER and 'train_loader' in globals():\n    loader = train_loader\n    it = iter(loader)\n    while True:\n        try:\n            batch = next(it)\n        except StopIteration:\n            break\n        imgs = batch[\"images\"].cpu()           # (B,3,H,W)\n        masks = batch[\"masks\"].cpu()           # (B,1,H,W)\n        insts = batch[\"insts\"]                 # list length B, each is list of (H,W) arrays\n        case_ids = batch[\"case_ids\"]\n        is_forged = batch[\"is_forged\"].cpu().numpy()\n        bsize = imgs.shape[0]\n        for i in range(bsize):\n            total += 1\n            img_np = imgs[i].permute(1,2,0).numpy()\n            # try to un-normalize (approx) if values look normalized (-2..2)\n            if img_np.max() <= 2.5:\n                img_np = (img_np * np.array([0.229,0.224,0.225]) + np.array([0.485,0.456,0.406]))\n                img_np = np.clip(img_np, 0, 1)\n            img_disp = (img_np*255).astype(np.uint8)\n\n            mask_np = masks[i].squeeze(0).numpy().astype(np.uint8)  # (H,W)\n            inst_list = insts[i]  # list of numpy arrays already at same size\n\n            # union of instance masks\n            if len(inst_list) == 0:\n                union_np = np.zeros_like(mask_np, dtype=np.uint8)\n            else:\n                union_np = np.zeros_like(mask_np, dtype=np.uint8)\n                for m in inst_list:\n                    union_np |= (m > 0).astype(np.uint8)\n\n            # checks\n            if mask_np.shape != union_np.shape or mask_np.shape[0] != img_disp.shape[0] or mask_np.shape[1] != img_disp.shape[1]:\n                shape_violations += 1\n                info = f\"shape mismatch: mask {mask_np.shape}, img {img_disp.shape[:2]}\"\n                if len(mismatch_examples) < SHOW_MAX:\n                    diff_np = np.ones_like(mask_np)  # full diff (placeholder)\n                    visualize_mismatch(img_disp, mask_np, union_np, diff_np, case_ids[i], info)\n                continue\n\n            diff = (mask_np ^ union_np).astype(np.uint8)\n            diff_pixels = diff.sum()\n            # forged flag check: if is_forged==0 but mask has pixels, or vice versa\n            if is_forged[i] == 0 and mask_np.sum() > 0:\n                forged_flag_violations += 1\n            if is_forged[i] == 1 and mask_np.sum() == 0:\n                forged_flag_violations += 1\n\n            if diff_pixels > MISMATCH_PIXEL_THRESHOLD:\n                mismatch_examples.append({\n                    \"case_id\": case_ids[i],\n                    \"diff_pixels\": int(diff_pixels),\n                    \"mask_pixels\": int(mask_np.sum()),\n                    \"union_pixels\": int(union_np.sum()),\n                    \"is_forged\": int(is_forged[i])\n                })\n                if len(mismatch_examples) <= SHOW_MAX:\n                    info = f\"diff_pixels={int(diff_pixels)}, mask_px={int(mask_np.sum())}, union_px={int(union_np.sum())}\"\n                    visualize_mismatch(img_disp, mask_np, union_np, diff, case_ids[i], info)\n            else:\n                ok_count += 1\n\n            checked += 1\n            if SAMPLE_LIMIT and checked >= SAMPLE_LIMIT:\n                break\n        if SAMPLE_LIMIT and checked >= SAMPLE_LIMIT:\n            break\n\nelse:\n    # fallback: build a tiny dataset using train_split.csv and simple Dataset (no workers)\n    df = pd.read_csv(\"/kaggle/working/train_split.csv\")\n    from PIL import Image\n    def load_sample(row):\n        img = np.array(Image.open(row[\"image_path\"]).convert(\"RGB\"))\n        h0,w0 = json.loads(row[\"shape\"])\n        mask = np.zeros((h0,w0), dtype=np.uint8)\n        if int(row[\"is_forged\"])==1:\n            arr = np.load(row[\"mask_path\"])\n            if arr.ndim==2:\n                insts = [ (arr>0).astype(np.uint8) ]\n            else:\n                insts = [ (arr[c]>0).astype(np.uint8) for c in range(arr.shape[0]) ]\n            for m in insts:\n                mask |= m\n        return img, mask, insts\n    for idx,row in df.sample(frac=1, random_state=0).iterrows():\n        img, mask_np, insts = load_sample(row)\n        union_np = np.zeros_like(mask_np, dtype=np.uint8)\n        for m in insts:\n            union_np |= (m>0).astype(np.uint8)\n        diff = (mask_np ^ union_np).astype(np.uint8)\n        diff_pixels = diff.sum()\n        total += 1\n        if diff_pixels > MISMATCH_PIXEL_THRESHOLD and len(mismatch_examples) < SHOW_MAX:\n            visualize_mismatch(img, mask_np, union_np, diff, row[\"case_id\"], f\"diff={diff_pixels}\")\n        if SAMPLE_LIMIT and total >= SAMPLE_LIMIT:\n            break\n\n# Summary\nprint(\"Checked samples:\", total)\nprint(\"OK count (no diff):\", ok_count)\nprint(\"Mismatches found:\", len(mismatch_examples))\nprint(\"Forged flag violations (is_forged vs mask):\", forged_flag_violations)\nprint(\"Shape violations:\", shape_violations)\nif len(mismatch_examples) > 0:\n    print(\"First mismatch examples (summary):\")\n    for ex in mismatch_examples[:10]:\n        print(ex)\nelse:\n    print(\"No mismatches found (union(inst_masks) == combined mask) in checked samples.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:26:26.707728Z","iopub.execute_input":"2025-12-10T09:26:26.707994Z","iopub.status.idle":"2025-12-10T09:26:57.228225Z","shell.execute_reply.started":"2025-12-10T09:26:26.707969Z","shell.execute_reply":"2025-12-10T09:26:57.227479Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"vizualizations","metadata":{}},{"cell_type":"code","source":"import json, random\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\nfrom PIL import Image\n\nBASE = Path(\"/kaggle/input/recodai-luc-scientific-image-forgery-detection\")\nTRAIN_CSV = Path(\"/kaggle/working/train_split.csv\")\nMASK_DIR = BASE / \"train_masks\"\n\ndef load_mask_list(mask_path):\n    m = np.load(mask_path)\n    m = (m > 0).astype(np.uint8)\n    if m.ndim == 2:\n        return [m]\n    if m.ndim == 3:\n        return [m[c].astype(np.uint8) for c in range(m.shape[0]) if m[c].sum() > 0]\n    raise ValueError(str(m.shape))\n\ndf = pd.read_csv(TRAIN_CSV)\ndf_sample_forged = df[df[\"is_forged\"]==1].sample(3, random_state=2).reset_index(drop=True)\ndf_sample_auth = df[df[\"is_forged\"]==0].sample(2, random_state=3).reset_index(drop=True)\n\ndef show_sample(row):\n    img_path = Path(row[\"image_path\"])\n    img = np.array(Image.open(img_path).convert(\"RGB\"))\n    h,w = json.loads(row[\"shape\"])\n    if int(row[\"is_forged\"])==1:\n        insts = load_mask_list(Path(row[\"mask_path\"]))\n        combined = np.zeros((h,w), dtype=np.uint8)\n        for m in insts:\n            combined |= (m>0).astype(np.uint8)\n    else:\n        insts = []\n        combined = np.zeros((h,w), dtype=np.uint8)\n\n    fig, axs = plt.subplots(1, 2 + max(1, len(insts)), figsize=(4*(2+len(insts)), 4))\n    axs[0].imshow(img); axs[0].axis(\"off\"); axs[0].set_title(f\"Image: {row['case_id']}\")\n    axs[1].imshow(combined, cmap=\"gray\"); axs[1].axis(\"off\"); axs[1].set_title(f\"Combined mask\\npixels={int(combined.sum())}\")\n    # overlay\n    overlay = img.copy()\n    if combined.sum()>0:\n        overlay[combined==1] = (255,0,0)\n    axs[1].imshow(combined, cmap=\"gray\", alpha=0.45)\n    axs[0].imshow(overlay); axs[0].set_title(f\"Overlay (red = mask)\")\n\n    # show instance masks (if any)\n    if len(insts)==0:\n        axs[2].text(0.5,0.5,\"No instance masks\", ha='center', va='center'); axs[2].axis('off')\n    else:\n        for i, m in enumerate(insts):\n            ax = axs[2+i]\n            ax.imshow(m, cmap=\"gray\"); ax.axis(\"off\")\n            ax.set_title(f\"Inst {i+1} pixels={int(m.sum())}\")\n    plt.tight_layout()\n    plt.show()\n\nprint(\"---- Forged examples ----\")\nfor _, r in df_sample_forged.iterrows():\n    show_sample(r)\n\nprint(\"---- Authentic examples ----\")\nfor _, r in df_sample_auth.iterrows():\n    show_sample(r)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:26:57.229003Z","iopub.execute_input":"2025-12-10T09:26:57.229277Z","iopub.status.idle":"2025-12-10T09:27:02.507015Z","shell.execute_reply.started":"2025-12-10T09:26:57.229251Z","shell.execute_reply":"2025-12-10T09:27:02.506430Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Baseline Model","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass DoubleConv(nn.Module):\n    # (conv => BN => ReLU) * 2\n    def __init__(self, in_ch, out_ch):\n        super().__init__()\n        self.net = nn.Sequential(\n            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1, bias=False),\n            nn.BatchNorm2d(out_ch),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1, bias=False),\n            nn.BatchNorm2d(out_ch),\n            nn.ReLU(inplace=True),\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\nclass Down(nn.Module):\n    # downsampling: maxpool + double conv\n    def __init__(self, in_ch, out_ch):\n        super().__init__()\n        self.pool = nn.MaxPool2d(2)\n        self.conv = DoubleConv(in_ch, out_ch)\n\n    def forward(self, x):\n        x = self.pool(x)\n        x = self.conv(x)\n        return x\n\nclass Up(nn.Module):\n    # upsampling: transposed conv + concat skip + double conv\n    def __init__(self, in_ch, out_ch):\n        super().__init__()\n        self.up = nn.ConvTranspose2d(in_ch, out_ch, kernel_size=2, stride=2)\n        self.conv = DoubleConv(in_ch, out_ch)  # in_ch = skip_ch + up_ch\n\n    def forward(self, x, skip):\n        x = self.up(x)\n        # pad if needed to match skip size\n        diffY = skip.size(2) - x.size(2)\n        diffX = skip.size(3) - x.size(3)\n        if diffY != 0 or diffX != 0:\n            x = F.pad(\n                x,\n                [diffX // 2, diffX - diffX // 2,\n                 diffY // 2, diffY - diffY // 2],\n            )\n        x = torch.cat([skip, x], dim=1)\n        x = self.conv(x)\n        return x\n\nclass UNet(nn.Module):\n    def __init__(self, in_ch=3, out_ch=1, base_ch=32):\n        super().__init__()\n        self.inc   = DoubleConv(in_ch, base_ch)\n        self.down1 = Down(base_ch, base_ch * 2)\n        self.down2 = Down(base_ch * 2, base_ch * 4)\n        self.down3 = Down(base_ch * 4, base_ch * 8)\n        self.down4 = Down(base_ch * 8, base_ch * 16)\n\n        self.up1 = Up(base_ch * 16, base_ch * 8)\n        self.up2 = Up(base_ch * 8,  base_ch * 4)\n        self.up3 = Up(base_ch * 4,  base_ch * 2)\n        self.up4 = Up(base_ch * 2,  base_ch)\n\n        self.outc = nn.Conv2d(base_ch, out_ch, kernel_size=1)\n\n    def forward(self, x):\n        x1 = self.inc(x)      # (B, base)\n        x2 = self.down1(x1)   # (B, 2*base)\n        x3 = self.down2(x2)   # (B, 4*base)\n        x4 = self.down3(x3)   # (B, 8*base)\n        x5 = self.down4(x4)   # (B, 16*base)\n\n        x = self.up1(x5, x4)\n        x = self.up2(x,  x3)\n        x = self.up3(x,  x2)\n        x = self.up4(x,  x1)\n\n        logits = self.outc(x)  # (B, out_ch, H, W)\n        return logits","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:27:02.507687Z","iopub.execute_input":"2025-12-10T09:27:02.507864Z","iopub.status.idle":"2025-12-10T09:27:02.521083Z","shell.execute_reply.started":"2025-12-10T09:27:02.507850Z","shell.execute_reply":"2025-12-10T09:27:02.520388Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# CELL 1: TRAINING (LOSS-ONLY) — using local UNet (no pip)\nimport os, json, gc, time\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import AdamW\nfrom tqdm.auto import tqdm\n\n# (UNet class must already be defined above this cell)\n\n# -------------------------\n# Hyperparams & paths\n# -------------------------\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nEPOCHS = 1\nBATCH_SIZE = 2       # used when you create DataLoaders\nLR = 1e-4\nWEIGHT_DECAY = 1e-6\nCHECKPOINT_DIR = Path(\"/kaggle/working/checkpoints\")\nCHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)\n\n# loss weights\nWEIGHT_BCE = 0.5\nWEIGHT_DICE = 0.3\nWEIGHT_FOCAL = 0.5\nALPHA_FOCAL = 0.85\nGAMMA_FOCAL = 1.5\n\n# -------------------------\n# Instantiate model, opt, scheduler\n# -------------------------\nmodel = UNet(in_ch=3, out_ch=1, base_ch=32).to(DEVICE)\n\noptim = AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, T_max=EPOCHS, eta_min=1e-6)\n\n# -------------------------\n# Loss functions\n# -------------------------\nbce_loss = nn.BCEWithLogitsLoss(reduction=\"mean\")\n\ndef dice_loss_logits(pred_logits, target, eps=1e-7):\n    pred = torch.sigmoid(pred_logits)\n    p = pred.view(pred.size(0), -1)\n    t = target.view(target.size(0), -1)\n    inter = (p * t).sum(dim=1)\n    dice = (2. * inter + eps) / (p.sum(dim=1) + t.sum(dim=1) + eps)\n    return 1.0 - dice.mean()\n\ndef focal_loss_logits(pred_logits, target, alpha=ALPHA_FOCAL, gamma=GAMMA_FOCAL):\n    bce = F.binary_cross_entropy_with_logits(pred_logits, target, reduction=\"none\")\n    prob = torch.sigmoid(pred_logits)\n    p_t = prob * target + (1 - prob) * (1 - target)\n    alpha_factor = alpha * target + (1 - alpha) * (1 - target)\n    modulating_factor = (1.0 - p_t).pow(gamma)\n    loss = alpha_factor * modulating_factor * bce\n    return loss.mean()\n\ndef total_loss_fn(pred_logits, target):\n    return (\n        WEIGHT_BCE * bce_loss(pred_logits, target)\n        + WEIGHT_DICE * dice_loss_logits(pred_logits, target)\n        + WEIGHT_FOCAL * focal_loss_logits(pred_logits, target)\n    )\n\n# -------------------------\n# Training / validation loops\n# -------------------------\ndef train_one_epoch(model, loader):\n    model.train()\n    running = 0.0\n    n = 0\n    pbar = tqdm(loader, desc=\"Train\")\n    for batch in pbar:\n        imgs = batch[\"images\"].to(DEVICE)\n        masks = batch[\"masks\"].to(DEVICE)   # (B,1,H,W)\n\n        optim.zero_grad()\n        logits = model(imgs)                # UNet forward\n        loss = total_loss_fn(logits, masks)\n        loss.backward()\n        optim.step()\n\n        running += loss.item() * imgs.size(0)\n        n += imgs.size(0)\n        pbar.set_postfix(train_loss=f\"{running / n:.4f}\")\n    return running / max(1, n)\n\ndef validate_loss_only(model, loader):\n    model.eval()\n    running = 0.0\n    n = 0\n    with torch.no_grad():\n        for batch in tqdm(loader, desc=\"Val\"):\n            imgs = batch[\"images\"].to(DEVICE)\n            masks = batch[\"masks\"].to(DEVICE)\n            logits = model(imgs)\n            loss = total_loss_fn(logits, masks)\n            running += loss.item() * imgs.size(0)\n            n += imgs.size(0)\n    return running / max(1, n)\n\n# -------------------------\n# Main loop\n# -------------------------\nbest_val = float(\"inf\")\nstart = time.time()\nfor epoch in range(1, EPOCHS+1):\n    print(f\"\\n=== Epoch {epoch}/{EPOCHS} ===\")\n    train_loss = train_one_epoch(model, train_loader)\n    val_loss = validate_loss_only(model, val_loader)\n    print(f\"Epoch {epoch} Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}\")\n\n    # save epoch and best-by-val\n    epoch_ckpt = CHECKPOINT_DIR / f\"epoch{epoch}_train{train_loss:.4f}_val{val_loss:.4f}.pth\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"model_state\": model.state_dict(),\n            \"optim_state\": optim.state_dict(),\n            \"train_loss\": train_loss,\n            \"val_loss\": val_loss,\n        },\n        epoch_ckpt,\n    )\n    print(\"Saved:\", epoch_ckpt)\n\n    if val_loss < best_val:\n        best_val = val_loss\n        best_ckpt = CHECKPOINT_DIR / f\"best_val_epoch{epoch}_val{val_loss:.4f}.pth\"\n        torch.save(\n            {\n                \"epoch\": epoch,\n                \"model_state\": model.state_dict(),\n                \"optim_state\": optim.state_dict(),\n                \"train_loss\": train_loss,\n                \"val_loss\": val_loss,\n            },\n            best_ckpt,\n        )\n        print(\"Saved best-val:\", best_ckpt)\n\n    scheduler.step()\n    gc.collect()\n    torch.cuda.empty_cache()\n\nprint(\"Training finished. Time (min):\", (time.time() - start) / 60)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T09:27:02.521701Z","iopub.execute_input":"2025-12-10T09:27:02.521928Z","iopub.status.idle":"2025-12-10T09:37:29.063044Z","shell.execute_reply.started":"2025-12-10T09:27:02.521912Z","shell.execute_reply":"2025-12-10T09:37:29.062422Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# CELL 2: oF1 SWEEP — load best checkpoint, sweep thresholds & min sizes (UNet)\nimport numpy as np\nimport cv2\nfrom tqdm.auto import tqdm\nfrom pathlib import Path\nfrom scipy.optimize import linear_sum_assignment\nimport torch, json\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nCHECKPOINT_DIR = Path(\"/kaggle/working/checkpoints\")\nckpts = sorted(CHECKPOINT_DIR.glob(\"best_val_epoch*.pth\"))\nif len(ckpts) == 0:\n    ckpts = sorted(CHECKPOINT_DIR.glob(\"epoch*.pth\"))\nif len(ckpts) == 0:\n    raise FileNotFoundError(\"No checkpoints found.\")\nBEST_CKPT = ckpts[-1]\nprint(\"Using checkpoint:\", BEST_CKPT)\n\n# reconstruct model identical to training\nmodel = UNet(in_ch=3, out_ch=1, base_ch=32).to(DEVICE)\nck = torch.load(BEST_CKPT, map_location=\"cpu\")\nstate = ck.get(\"model_state\", ck.get(\"state_dict\", ck))\nnew_state = {k.replace(\"module.\", \"\"): v for k,v in state.items()}\nmissing, unexpected = model.load_state_dict(new_state, strict=False)\nprint(\"Missing keys:\", len(missing), \"Unexpected:\", len(unexpected))\nmodel.eval()\n\n# helper metrics\ndef f1_pair(pred_mask, gt_mask):\n    pred = pred_mask.flatten().astype(bool)\n    gt = gt_mask.flatten().astype(bool)\n    tp = np.logical_and(pred, gt).sum()\n    fp = np.logical_and(pred, ~gt).sum()\n    fn = np.logical_and(~pred, gt).sum()\n    if (2*tp + fp + fn) == 0: return 0.0\n    return 2*tp / (2*tp + fp + fn)\n\ndef oF1(pred_masks, gt_masks):\n    if len(gt_masks)==0 and len(pred_masks)==0: return 1.0\n    if len(gt_masks)==0: return 0.0\n    if len(pred_masks)==0: return 0.0\n    P = len(pred_masks); G = len(gt_masks)\n    M = np.zeros((P,G))\n    for i in range(P):\n        for j in range(G):\n            M[i,j] = f1_pair(pred_masks[i], gt_masks[j])\n    if P < G:\n        M = np.vstack([M, np.zeros((G-P,G))])\n        P = G\n    r,c = linear_sum_assignment(-M)\n    matched = M[r,c].sum() / max(1, G)\n    penalty = G / max(P,G)\n    return matched * penalty\n\n# instance extraction\ndef prob_to_instances(prob_map, thr, min_size):\n    bw = (prob_map > thr).astype(np.uint8)\n    num, labels = cv2.connectedComponents(bw, connectivity=8)\n    insts = []\n    for lab in range(1, num):\n        m = (labels==lab).astype(np.uint8)\n        if m.sum() >= min_size:\n            insts.append(m)\n    return insts\n\ndef evaluate_of1(model, loader, thr, min_size):\n    model.eval()\n    scores = []\n    with torch.no_grad():\n        for batch in tqdm(loader, desc=f\"Eval thr={thr} min={min_size}\"):\n            imgs = batch[\"images\"].to(DEVICE)\n            gt_insts = batch[\"insts\"]  # list of lists of numpy arrays\n            logits = model(imgs)\n            probs = torch.sigmoid(logits).cpu().numpy().squeeze(1)  # (B,H,W)\n            for i in range(probs.shape[0]):\n                pred_insts = prob_to_instances(probs[i], thr, min_size)\n                gt_insts_i = [(g>0).astype(np.uint8) for g in gt_insts[i]]\n                scores.append(oF1(pred_insts, gt_insts_i))\n    return float(np.mean(scores)) if len(scores)>0 else 0.0\n\nthresholds = [0.2,0.3,0.4,0.5]\nmin_sizes  = [16,64]\n\nbest_score = -1.0\nbest_thr = None\nbest_ms = None\nresults = []\n\nfor thr in thresholds:\n    for ms in min_sizes:\n        score = evaluate_of1(model, val_loader, thr, ms)\n        print(f\"THR={thr:.2f} MIN={ms} => oF1={score:.4f}\")\n        results.append({\"thr\": thr, \"min_size\": ms, \"of1\": score})\n        if score > best_score:\n            best_score = score\n            best_thr = thr\n            best_ms = ms\n\nprint(\"BEST oF1:\", best_score, \"THR:\", best_thr, \"MIN:\", best_ms)\n\nout = {\n    \"best_threshold\": float(best_thr),\n    \"best_min_size\": int(best_ms),\n    \"best_of1\": float(best_score),\n    \"results\": results,\n}\nwith open(\"/kaggle/working/best_params.json\", \"w\") as f:\n    json.dump(out, f, indent=2)\nprint(\"Saved /kaggle/working/best_params.json\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T10:29:22.643915Z","iopub.execute_input":"2025-12-10T10:29:22.644251Z","iopub.status.idle":"2025-12-10T10:52:24.489555Z","shell.execute_reply.started":"2025-12-10T10:29:22.644227Z","shell.execute_reply":"2025-12-10T10:52:24.488797Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Predictions on Test dataset","metadata":{}},{"cell_type":"markdown","source":"<!-- if forged it predicted as \"[1234,8,122,12]\" -->","metadata":{}},{"cell_type":"code","source":"# CELL 3: INFERENCE + RLE SUBMISSION (uses best_params.json, UNet)\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nimport cv2\nimport torch\nfrom tqdm.auto import tqdm\nfrom IPython.display import FileLink\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nBASE_DIR = Path(\"/kaggle/input/recodai-luc-scientific-image-forgery-detection\")\nTEST_DIR = BASE_DIR / \"test_images\"\nSAMPLE_SUB = BASE_DIR / \"sample_submission.csv\"\n\nOUT_CSV = Path(\"/kaggle/working/submission.csv\")\nCKPT_DIR = Path(\"/kaggle/working/checkpoints\")\nBP = Path(\"/kaggle/working/best_params.json\")\n\nIMG_SIZE = (512, 512)\nMEAN = np.array([0.485, 0.456, 0.406])\nSTD  = np.array([0.229, 0.224, 0.225])\n\n# -------------------------\n# load best params\n# -------------------------\nif BP.exists():\n    bp = json.load(open(BP))\n    THRESH = float(bp.get(\"best_threshold\", 0.2))\n    MIN_COMPONENT_SIZE = int(bp.get(\"best_min_size\",1))\nelse:\n    THRESH = 0.2\n    MIN_COMPONENT_SIZE = 1\n\n# -------------------------\n# load best checkpoint\n# -------------------------\nckpts = sorted(CKPT_DIR.glob(\"best_val_epoch*.pth\"))\nif len(ckpts) == 0:\n    ckpts = sorted(CKPT_DIR.glob(\"epoch*.pth\"))\nif len(ckpts) == 0:\n    raise FileNotFoundError(\"No checkpoints found in /kaggle/working/checkpoints\")\n\nBEST_CKPT = ckpts[-1]\nprint(\"Using checkpoint:\", BEST_CKPT)\nck = torch.load(BEST_CKPT, map_location=\"cpu\")\nstate = ck.get(\"model_state\", ck.get(\"state_dict\", ck))\nstate = {k.replace(\"module.\", \"\"): v for k, v in state.items()}\n\n# -------------------------\n# rebuild UNet (same as training)\n# -------------------------\nmodel = UNet(in_ch=3, out_ch=1, base_ch=32).to(DEVICE)\nmissing, unexpected = model.load_state_dict(state, strict=False)\nprint(\"Loaded checkpoint -> missing:\", len(missing), \"unexpected:\", len(unexpected))\nmodel.eval()\n\n# -------------------------\n# RLE encoder: ONE mask -> ONE JSON array string\n# -------------------------\ndef rle_encode_numpy(mask: np.ndarray) -> str:\n    \"\"\"\n    Encode binary mask (H,W) into JSON-style RLE string.\n    Returns e.g. \"[123, 4, 567, 8]\".\n    \"\"\"\n    flat = mask.T.flatten().astype(np.uint8)  # Fortran order: transpose first\n    dots = np.where(flat == 1)[0]\n    if len(dots) == 0:\n        return \"authentic\"\n\n    run = []\n    prev = -2\n    for b in dots:\n        if b > prev + 1:\n            run.extend((int(b + 1), 0))\n        run[-1] += 1\n        prev = b\n\n    return json.dumps([int(x) for x in run])\n\n# -------------------------\n# Convert prob map -> connected components -> union mask\n# -------------------------\ndef prob_to_union_mask(prob_map: np.ndarray, thr: float, min_size: int) -> np.ndarray:\n    \"\"\"\n    Threshold prob_map and keep union of all components >= min_size.\n    Returns a single binary mask (H,W) with 0/1.\n    \"\"\"\n    bw = (prob_map > thr).astype(np.uint8)\n    if bw.sum() == 0:\n        return np.zeros_like(bw, dtype=np.uint8)\n\n    num, labels = cv2.connectedComponents(bw, connectivity=8)\n    union = np.zeros_like(bw, dtype=np.uint8)\n    for lab in range(1, num):\n        m = (labels == lab).astype(np.uint8)\n        if m.sum() >= min_size:\n            union[m == 1] = 1\n    return union\n\n# -------------------------\n# Inference for one image: returns \"authentic\" OR RLE string\n# -------------------------\ndef infer_image(img_path: Path) -> str:\n    img = np.array(Image.open(img_path).convert(\"RGB\"))\n    h0, w0 = img.shape[:2]\n\n    # resize + normalize\n    img_resized = np.array(\n        Image.fromarray(img).resize((IMG_SIZE[1], IMG_SIZE[0]))\n    )\n    img_norm = (img_resized / 255.0 - MEAN) / STD\n    t = torch.tensor(\n        img_norm.transpose(2, 0, 1),\n        dtype=torch.float32\n    ).unsqueeze(0).to(DEVICE)\n\n    with torch.no_grad():\n        logits = model(t)\n        prob = torch.sigmoid(logits)[0, 0].cpu().numpy() # (H, W) at IMG_SIZE\n    # with torch.no_grad():\n    # # original\n    #     logits = model(t)\n    #     prob0 = torch.sigmoid(logits)[0, 0]\n\n    # # horizontal flip\n    #     t_h = torch.flip(t, dims=[3])  # flip width\n    #     logits_h = model(t_h)\n    #     prob_h = torch.flip(torch.sigmoid(logits_h)[0, 0], dims=[1])\n\n    # # vertical flip\n    #     t_v = torch.flip(t, dims=[2])  # flip height\n    #     logits_v = model(t_v)\n    #     prob_v = torch.flip(torch.sigmoid(logits_v)[0, 0], dims=[0])\n\n    # # average\n    #     prob = (prob0 + prob_h + prob_v) / 3.0\n    #     prob = prob.cpu().numpy()\n        \n    # union mask in model resolution\n    mask_small = prob_to_union_mask(prob, thr=THRESH, min_size=MIN_COMPONENT_SIZE)\n\n    if mask_small.sum() == 0:\n        return \"authentic\"\n\n    # resize mask back to original size\n    pil_m = Image.fromarray((mask_small * 255).astype(np.uint8))\n    m_big = pil_m.resize((w0, h0), resample=Image.NEAREST)\n    mask_big = (np.array(m_big) > 127).astype(np.uint8)\n\n    # final single RLE string\n    return rle_encode_numpy(mask_big)\n\n# -------------------------\n# Run inference over TEST_DIR\n# -------------------------\nfiles = sorted(\n    [p for p in TEST_DIR.iterdir()\n     if p.suffix.lower() in (\".png\", \".jpg\", \".jpeg\", \".tif\", \".tiff\")]\n)\nprint(\"Num test images found:\", len(files))\n\nrows = []\nfor p in tqdm(files, desc=\"Predict\"):\n    ann = infer_image(p)  # \"authentic\" or RLE string\n    rows.append({\n        \"case_id\": str(p.stem),   # keep as string for merge\n        \"annotation\": ann\n    })\n\ndf = pd.DataFrame(rows)\ndf[\"case_id\"] = df[\"case_id\"].astype(str)\n\n# -------------------------\n# Align with sample_submission to guarantee correct IDs & row count\n# -------------------------\nsample_sub = pd.read_csv(SAMPLE_SUB)\nsample_sub[\"case_id\"] = sample_sub[\"case_id\"].astype(str)\n\nsubmission = sample_sub[[\"case_id\"]].merge(df, on=\"case_id\", how=\"left\")\nsubmission[\"annotation\"] = submission[\"annotation\"].fillna(\"authentic\")\n\nsubmission.to_csv(OUT_CSV, index=False)\nprint(\"Saved submission:\", OUT_CSV)\ndisplay(FileLink(str(OUT_CSV)))\nprint(submission.head())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-10T10:01:38.694654Z","iopub.execute_input":"2025-12-10T10:01:38.695138Z","iopub.status.idle":"2025-12-10T10:01:38.964197Z","shell.execute_reply.started":"2025-12-10T10:01:38.695111Z","shell.execute_reply":"2025-12-10T10:01:38.963485Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}