Refactoring Lightning API

.pre-commit-config.yaml

@@ -32,6 +32,7 @@ repos:
         additional_dependencies:
           - numpy
           - types-PyYAML
+          - types-setuptools
 
   # check docstrings
   - repo: https://github.com/numpy/numpydoc

examples/2D/n2v/example_BSD68_lightning.ipynb

@@ -8,20 +8,17 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "import torch\n",
-    "import albumentations as Aug\n",
     "import matplotlib.pyplot as plt\n",
     "import tifffile\n",
     "from careamics_portfolio import PortfolioManager\n",
     "from pytorch_lightning import Trainer\n",
     "\n",
     "from careamics import CAREamicsModule\n",
-    "from careamics.lightning_prediction import CAREamicsFiring\n",
-    "from careamics.ligthning_datamodule import (\n",
+    "from careamics.lightning_datamodule import (\n",
     "    CAREamicsPredictDataModule,\n",
     "    CAREamicsTrainDataModule,\n",
     ")\n",
-    "from careamics.transforms import N2VManipulate\n",
+    "from careamics.lightning_prediction import CAREamicsPredictionLoop\n",
     "from careamics.utils.metrics import psnr"
    ]
   },
@@ -124,6 +121,16 @@
     "Please take as look at the [documentation](https://careamics.github.io) to see the full list of parameters and configuration options"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# N2V2 requires changes to the UNet model and to the Dataset (augmentations)\n",
+    "use_n2v2 = False  # change to True to use N2V2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -134,18 +141,22 @@
     "    algorithm=\"n2v\",\n",
     "    loss=\"n2v\",\n",
     "    architecture=\"UNet\",\n",
-    "    model_parameters={\"n2v2\": True},\n",
+    "    model_parameters={\"n2v2\": use_n2v2},\n",
     "    optimizer_parameters={\"lr\": 1e-3},\n",
     "    lr_scheduler_parameters={\"factor\": 0.5, \"patience\": 10},\n",
-    ")\n"
+    ")"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialize the datamodule"
+    "### Initialize the datamodule\n",
+    "\n",
+    "The data module can take a `Path` or `str` to a folder or file, or a `np.ndarray`.\n",
+    "\n",
+    "For custom types, you need to pass a read function and an extension_filter."
    ]
   },
   {
@@ -155,13 +166,16 @@
    "outputs": [],
    "source": [
     "train_data_module = CAREamicsTrainDataModule(\n",
-    "    train_path=train_path,\n",
-    "    val_path=val_path,\n",
-    "    data_type=\"tiff\",\n",
+    "    train_data=train_path,\n",
+    "    val_data=val_path,\n",
+    "    data_type=\"tiff\",  # to use np.ndarray, set data_type to \"array\"\n",
     "    patch_size=(64, 64),\n",
     "    axes=\"SYX\",\n",
     "    batch_size=128,\n",
     "    dataloader_params={\"num_workers\": 4},\n",
+    "    use_n2v2=use_n2v2,\n",
+    "    struct_n2v_axis=\"none\",  # choice between \"horizontal\", \"vertical\", or \"none\" (no structN2V)\n",
+    "    struct_n2v_span=7,\n",
     ")"
    ]
   },
@@ -208,9 +222,10 @@
    "outputs": [],
    "source": [
     "pred_data_module = CAREamicsPredictDataModule(\n",
-    "    pred_path=test_path,\n",
+    "    pred_data=test_path,\n",
     "    data_type=\"tiff\",\n",
     "    tile_size=(256, 256),\n",
+    "    tile_overlap=(48, 48),\n",
     "    axes=\"YX\",\n",
     "    batch_size=1,\n",
     "    tta_transforms=True,\n",
@@ -224,7 +239,7 @@
    "source": [
     "### Run prediction\n",
     "\n",
-    "We need to specify the path to the data we want to denoise"
+    "First, we want to use CAREamics prediction loop, which allows tiling:"
    ]
   },
   {
@@ -233,16 +248,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tiled_loop = CAREamicsFiring(trainer)"
+    "tiled_loop = CAREamicsPredictionLoop(trainer)\n",
+    "trainer.predict_loop = tiled_loop"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "trainer.predict_loop = tiled_loop"
+    "Then, we predict using the datamodule."
    ]
   },
   {
@@ -270,7 +284,7 @@
    "source": [
     "# Create a list of ground truth images\n",
     "\n",
-    "gts = [tifffile.imread(f) for f in sorted(gt_path.glob(\"*.tiff\"))]\n"
+    "gts = [tifffile.imread(f) for f in sorted(gt_path.glob(\"*.tiff\"))]"
    ]
   },
   {
@@ -362,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.8.17"
   },
   "vscode": {
    "interpreter": {

examples/DL4MIA_N2V.ipynb

@@ -68,7 +68,9 @@
     "from careamics_portfolio import PortfolioManager\n",
     "\n",
     "from careamics import CAREamist\n",
-    "from careamics.config import create_n2v_training_configuration, create_n2v_inference_configuration"
+    "from careamics.config import (\n",
+    "    create_n2v_training_configuration,\n",
+    ")"
    ]
   },
   {
@@ -202,7 +204,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "engine = CAREamist(source=training_config)\n"
+    "engine = CAREamist(source=training_config)"
    ]
   },
   {
@@ -273,8 +275,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.18"
-  },
-  "orig_nbformat": 4
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2