🚀 Add PreProcessor to AnomalyModule

configs/data/avenue.yaml

@@ -8,9 +8,6 @@ init_args:
   train_batch_size: 32
   eval_batch_size: 32
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   val_split_mode: from_test
   val_split_ratio: 0.5
   seed: null

configs/data/btech.yaml

@@ -5,9 +5,6 @@ init_args:
   train_batch_size: 32
   eval_batch_size: 32
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   test_split_mode: from_dir
   test_split_ratio: 0.2
   val_split_mode: same_as_test

configs/data/folder.yaml

@@ -12,9 +12,6 @@ init_args:
   eval_batch_size: 32
   num_workers: 8
   task: segmentation
-  transform: null
-  train_transform: null
-  eval_transform: null
   test_split_mode: from_dir
   test_split_ratio: 0.2
   val_split_mode: same_as_test

configs/data/kolektor.yaml

@@ -4,9 +4,6 @@ init_args:
   train_batch_size: 32
   eval_batch_size: 32
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   test_split_mode: from_dir
   test_split_ratio: 0.2
   val_split_mode: same_as_test

configs/data/mvtec.yaml

@@ -6,9 +6,6 @@ init_args:
   eval_batch_size: 32
   num_workers: 8
   task: segmentation
-  transform: null
-  train_transform: null
-  eval_transform: null
   test_split_mode: from_dir
   test_split_ratio: 0.2
   val_split_mode: same_as_test

configs/data/mvtec_3d.yaml

@@ -5,9 +5,6 @@ init_args:
   train_batch_size: 32
   eval_batch_size: 32
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   test_split_mode: from_dir
   test_split_ratio: 0.2
   val_split_mode: same_as_test

configs/data/shanghaitech.yaml

@@ -8,9 +8,6 @@ init_args:
   train_batch_size: 32
   eval_batch_size: 32
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   val_split_mode: FROM_TEST
   val_split_ratio: 0.5
   seed: null

configs/data/ucsd_ped.yaml

@@ -8,9 +8,6 @@ init_args:
   train_batch_size: 8
   eval_batch_size: 1
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   val_split_mode: FROM_TEST
   val_split_ratio: 0.5
   seed: null

configs/data/visa.yaml

@@ -5,9 +5,6 @@ init_args:
   train_batch_size: 32
   eval_batch_size: 32
   num_workers: 8
-  transform: null
-  train_transform: null
-  eval_transform: null
   test_split_mode: from_dir
   test_split_ratio: 0.2
   val_split_mode: same_as_test

notebooks/100_datamodules/101_btech.ipynb

@@ -48,7 +48,7 @@
     "# NOTE: Provide the path to the dataset root directory.\n",
     "#   If the datasets is not downloaded, it will be downloaded\n",
     "#   to this directory.\n",
-    "dataset_root = Path.cwd().parent / \"datasets\" / \"BTech\""
+    "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"BTech\""
    ]
   },
   {
@@ -106,7 +106,6 @@
     "btech_datamodule = BTech(\n",
     "    root=dataset_root,\n",
     "    category=\"01\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=0,\n",
@@ -378,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.8"
   },
   "orig_nbformat": 4,
   "vscode": {

notebooks/100_datamodules/102_mvtec.ipynb

@@ -58,7 +58,7 @@
     "# NOTE: Provide the path to the dataset root directory.\n",
     "#   If the datasets is not downloaded, it will be downloaded\n",
     "#   to this directory.\n",
-    "dataset_root = Path.cwd().parent / \"datasets\" / \"MVTec\""
+    "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"MVTec\""
    ]
   },
   {
@@ -84,7 +84,6 @@
     "mvtec_datamodule = MVTec(\n",
     "    root=dataset_root,\n",
     "    category=\"bottle\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=0,\n",
@@ -345,7 +344,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.8"
   },
   "orig_nbformat": 4,
   "vscode": {

notebooks/100_datamodules/103_folder.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
     "# NOTE: Provide the path to the dataset root directory.\n",
     "#   If the datasets is not downloaded, it will be downloaded\n",
     "#   to this directory.\n",
-    "dataset_root = Path.cwd().parent / \"datasets\" / \"hazelnut_toy\""
+    "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"hazelnut_toy\""
    ]
   },
   {
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,6 @@
     "    abnormal_dir=\"crack\",\n",
     "    task=TaskType.SEGMENTATION,\n",
     "    mask_dir=dataset_root / \"mask\" / \"crack\",\n",
-    "    image_size=(256, 256),\n",
     ")\n",
     "folder_datamodule.setup()"
    ]
@@ -114,7 +113,7 @@
    "outputs": [],
    "source": [
     "# Train images\n",
-    "i, data = next(enumerate(folder_datamodule.train_dataloader()))\n",
+    "data = next(iter(folder_datamodule.train_data))\n",
     "print(data.image.shape)"
    ]
   },
@@ -125,7 +124,7 @@
    "outputs": [],
    "source": [
     "# Test images\n",
-    "i, data = next(enumerate(folder_datamodule.test_dataloader()))\n",
+    "data = next(iter(folder_datamodule.test_data))\n",
     "print(data.image.shape, data.gt_mask.shape)"
    ]
   },
@@ -143,8 +142,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img = to_pil_image(data.image[0].clone())\n",
-    "msk = to_pil_image(data.gt_mask[0].int() * 255).convert(\"RGB\")\n",
+    "img = to_pil_image(data.image.clone())\n",
+    "msk = to_pil_image(data.gt_mask.int() * 255).convert(\"RGB\")\n",
     "\n",
     "Image.fromarray(np.hstack((np.array(img), np.array(msk))))"
    ]
@@ -187,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -369,7 +368,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.8"
   },
   "orig_nbformat": 4,
   "vscode": {

notebooks/100_datamodules/104_tiling.ipynb

@@ -44,7 +44,7 @@
     "# NOTE: Provide the path to the dataset root directory.\n",
     "#   If the datasets is not downloaded, it will be downloaded\n",
     "#   to this directory.\n",
-    "dataset_root = Path.cwd().parent / \"datasets\" / \"MVTec\" / \"transistor\""
+    "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"MVTec\" / \"transistor\""
    ]
   },
   {

notebooks/200_models/201_fastflow.ipynb

@@ -44,7 +44,7 @@
     "# NOTE: Provide the path to the dataset root directory.\n",
     "#   If the datasets is not downloaded, it will be downloaded\n",
     "#   to this directory.\n",
-    "dataset_root = Path.cwd().parent / \"datasets\" / \"MVTec\""
+    "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"MVTec\""
    ]
   },
   {
@@ -120,7 +120,6 @@
     "datamodule = MVTec(\n",
     "    root=dataset_root,\n",
     "    category=\"bottle\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=0,\n",
@@ -319,7 +318,9 @@
    },
    "outputs": [],
    "source": [
-    "inference_dataset = PredictDataset(path=dataset_root / \"bottle/test/broken_large/000.png\")\n",
+    "pre_processor = Fastflow.configure_pre_processor()\n",
+    "transform = pre_processor.predict_transform\n",
+    "inference_dataset = PredictDataset(path=dataset_root / \"bottle/test/broken_large/000.png\", transform=transform)\n",
     "inference_dataloader = DataLoader(dataset=inference_dataset, collate_fn=inference_dataset.collate_fn)"
    ]
   },
@@ -554,7 +555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.8"
   },
   "orig_nbformat": 4,
   "vscode": {

notebooks/600_loggers/601_mlflow_logging.ipynb

@@ -135,7 +135,7 @@
     "# NOTE: Provide the path to the dataset root directory.\n",
     "#   If the datasets is not downloaded, it will be downloaded\n",
     "#   to this directory.\n",
-    "dataset_root = Path.cwd().parent / \"datasets\" / \"MVTec\""
+    "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"MVTec\""
    ]
   },
   {
@@ -197,7 +197,6 @@
     "datamodule = MVTec(\n",
     "    root=dataset_root,\n",
     "    category=\"bottle\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=24,\n",
@@ -420,7 +419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/700_metrics/701a_aupimo.ipynb

@@ -140,7 +140,6 @@
     "datamodule = MVTec(\n",
     "    root=dataset_root,\n",
     "    category=\"leather\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=8,\n",
@@ -405,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.8"
   },
   "orig_nbformat": 4
  },

notebooks/700_metrics/701b_aupimo_advanced_i.ipynb

@@ -164,7 +164,6 @@
     "datamodule = MVTec(\n",
     "    root=dataset_root,\n",
     "    category=\"leather\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=8,\n",

notebooks/700_metrics/701c_aupimo_advanced_ii.ipynb

@@ -158,7 +158,6 @@
     "datamodule = MVTec(\n",
     "    root=dataset_root,\n",
     "    category=\"leather\",\n",
-    "    image_size=256,\n",
     "    train_batch_size=32,\n",
     "    eval_batch_size=32,\n",
     "    num_workers=8,\n",