Axes (#103)

CAREamics · Mar 5, 2024 · 922a829 · 922a829
2 parents 14ff842 + 3280564
commit 922a829
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diff --git a/examples/2D/n2v/example_BSD68_lightning.ipynb b/examples/2D/n2v/example_BSD68_lightning.ipynb
@@ -8,20 +8,18 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "import tifffile\n",
     "import matplotlib.pyplot as plt\n",
+    "import tifffile\n",
+    "from careamics_portfolio import PortfolioManager\n",
     "from pytorch_lightning import Trainer\n",
-    "import albumentations as Aug\n",
     "\n",
-    "from careamics_portfolio import PortfolioManager\n",
-    "from careamics.lightning_module import (\n",
-    "    CAREamicsModule,\n",
-    "    CAREamicsTrainDataModule,\n",
+    "from careamics import CAREamicsModule\n",
+    "from careamics.lightning_prediction import CAREamicsFiring\n",
+    "from careamics.ligthning_datamodule import (\n",
     "    CAREamicsPredictDataModule,\n",
-    "    CAREamicsFiring,\n",
+    "    CAREamicsTrainDataModule,\n",
     ")\n",
-    "from careamics.utils.metrics import psnr\n",
-    "from careamics.transforms import ManipulateN2V"
+    "from careamics.utils.metrics import psnr"
    ]
   },
   {
@@ -38,8 +36,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Download and unzip the files\n",
+    "# Explore portfolio\n",
     "portfolio = PortfolioManager()\n",
+    "print(portfolio.denoising)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download and unzip the files\n",
     "root_path = Path(\"data\")\n",
     "files = portfolio.denoising.N2V_BSD68.download(root_path)\n",
     "print(f\"List of downloaded files: {files}\")"
@@ -55,7 +63,12 @@
     "train_path = data_path / \"train\"\n",
     "val_path = data_path / \"val\"\n",
     "test_path = data_path / \"test\" / \"images\"\n",
-    "gt_path = data_path / \"test\" / \"gt\""
+    "gt_path = data_path / \"test\" / \"gt\"\n",
+    "\n",
+    "train_path.mkdir(parents=True, exist_ok=True)\n",
+    "val_path.mkdir(parents=True, exist_ok=True)\n",
+    "test_path.mkdir(parents=True, exist_ok=True)\n",
+    "gt_path.mkdir(parents=True, exist_ok=True)"
    ]
   },
   {
@@ -118,7 +131,9 @@
     "    algorithm=\"n2v\",\n",
     "    loss=\"n2v\",\n",
     "    architecture=\"UNet\",\n",
-    ")\n"
+    "    optimizer_parameters={\"lr\": 1e-4},\n",
+    "    lr_scheduler_parameters={\"factor\": 0.5, \"patience\": 10},\n",
+    ")"
    ]
   },
   {
@@ -129,17 +144,6 @@
     "### Define the Transforms"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transforms = Aug.Compose(\n",
-    "    [Aug.Flip(), Aug.RandomRotate90(), Aug.Normalize(), ManipulateN2V()],\n",
-    ")"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -161,7 +165,6 @@
     "    patch_size=(64, 64),\n",
     "    axes=\"SYX\",\n",
     "    batch_size=128,\n",
-    "    transforms=transforms,\n",
     "    num_workers=4,\n",
     ")"
    ]
@@ -182,7 +185,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "trainer = Trainer(max_epochs=1)"
+    "trainer = Trainer(max_epochs=50)"
    ]
   },
   {
@@ -202,17 +205,6 @@
     "### Define a prediction datamodule"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transforms_predict = Aug.Compose(\n",
-    "    [Aug.Normalize()],\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -225,8 +217,6 @@
     "    tile_size=(256, 256),\n",
     "    axes=\"YX\",\n",
     "    batch_size=1,\n",
-    "    num_workers=0,\n",
-    "    transforms=transforms_predict,\n",
     ")"
    ]
   },
@@ -324,7 +314,7 @@
     "psnr_total = 0\n",
     "\n",
     "for pred, gt in zip(preds, gts):\n",
-    "    psnr_total += psnr(gt, pred)\n",
+    "    psnr_total += psnr(gt, pred.squeeze())\n",
     "\n",
     "print(f\"PSNR total: {psnr_total / len(preds)}\")"
    ]
@@ -353,7 +343,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.18"
   },
   "vscode": {
    "interpreter": {

diff --git a/examples/careamics_api.py b/examples/careamics_api.py
@@ -1,7 +1,8 @@
 from careamics import CAREamist, Configuration
 
+
 def main():
-    config_dict ={
+    config_dict = {
         "experiment_name": "ConfigTest",
         "working_directory": ".",
         "algorithm": {
@@ -14,9 +15,7 @@ def main():
             "optimizer": {
                 "name": "Adam",
             },
-            "lr_scheduler": {
-                "name": "ReduceLROnPlateau"
-            },
+            "lr_scheduler": {"name": "ReduceLROnPlateau"},
         },
         "training": {
             "num_epochs": 1,
@@ -42,5 +41,5 @@ def main():
     # print(pred.shape)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/examples/careamics_lightning_api.ipynb b/examples/careamics_lightning_api.ipynb
@@ -9,12 +9,11 @@
     "import albumentations as Aug\n",
     "from pytorch_lightning import Trainer\n",
     "\n",
-    "\n",
     "from careamics import (\n",
     "    CAREamicsModule,\n",
     "    CAREamicsTrainDataModule,\n",
     ")\n",
-    "from careamics.transforms import ManipulateN2V\n"
+    "from careamics.transforms import ManipulateN2V"
    ]
   },
   {
@@ -26,20 +25,20 @@
     "# Instantiate ligthning module\n",
     "model = CAREamicsModule(\n",
     "    algorithm=\"n2v\",\n",
-    "    loss=\"n2v\", \n",
+    "    loss=\"n2v\",\n",
     "    architecture=\"UNet\",\n",
     "    model_parameters={\n",
     "        # parameters such as depth, n2v2, etc. See UNet definition.\n",
     "    },\n",
-    "    optimizer=\"Adam\", # see SupportedOptimizer\n",
+    "    optimizer=\"Adam\",  # see SupportedOptimizer\n",
     "    optimizer_parameters={\n",
     "        \"lr\": 1e-4,\n",
     "        # parameters from torch.optim\n",
     "    },\n",
-    "    lr_scheduler=\"ReduceLROnPlateau\", # see SupportedLRScheduler\n",
+    "    lr_scheduler=\"ReduceLROnPlateau\",  # see SupportedLRScheduler\n",
     "    lr_scheduler_parameters={\n",
     "        # parameters from torch.optim.lr_scheduler\n",
-    "    }\n",
+    "    },\n",
     ")"
    ]
   },
@@ -68,9 +67,12 @@
    "outputs": [],
    "source": [
     "# define function to read data\n",
+    "\n",
+    "\n",
     "def read_my_data_type(file):\n",
     "    pass\n",
     "\n",
+    "\n",
     "# Create your transforms using albumentations\n",
     "transforms = Aug.Compose(\n",
     "    [Aug.Flip(), Aug.RandomRotate90(), Aug.Normalize(), ManipulateN2V()],\n",
@@ -83,13 +85,13 @@
     "train_data_module = CAREamicsTrainDataModule(\n",
     "    train_path=train_path,\n",
     "    val_path=val_path,\n",
-    "    data_type=\"custom\", # this forces read_source_func to be specified\n",
+    "    data_type=\"custom\",  # this forces read_source_func to be specified\n",
     "    patch_size=(64, 64),\n",
     "    axes=\"SYX\",\n",
     "    batch_size=128,\n",
     "    transforms=transforms,\n",
     "    num_workers=4,\n",
-    "    read_source_func = read_my_data_type # function to read data\n",
+    "    read_source_func=read_my_data_type,  # function to read data\n",
     ")"
    ]
   },