Port virtual_staining examples into applications/cytoland

applications/cytoland/README.md

@@ -37,6 +37,22 @@ data:
   class_path: viscy_data.hcs.HCSDataModule
 ```
 
+## Tutorials and demos
+
+Scripts and tutorials live under [`examples/`](./examples/):
+
+| Folder | What it demonstrates |
+|--------|----------------------|
+| [`examples/VS_model_inference/`](./examples/VS_model_inference/) | Python API inference demos for VSCyto2D, VSCyto3D, VSNeuromast, and TTA-augmented sliding-window prediction |
+| [`examples/vcp_tutorials/`](./examples/vcp_tutorials/) | Virtual Cell Platform quick-start and organism-specific walkthroughs (HEK293T, neuromast) |
+| [`examples/dl-course-exercise/`](./examples/dl-course-exercise/) | Image-translation course exercise (training from scratch + evaluation) — used at DL@MBL and DL@Janelia |
+| [`examples/phase_contrast/`](./examples/phase_contrast/) | Phase-contrast tutorial and demo workflows |
+| [`examples/configs/`](./examples/configs/) | YAML configs for `viscy fit` / `viscy predict` across models (VSCyto2D/3D, VSNeuromast, FNet3D, dynacell) |
+
+All demo scripts are written as jupytext-style percent-cell `.py` files.
+Regenerate paired `.ipynb` notebooks with `jupytext --to ipynb solution.py`
+if you prefer the notebook UI.
+
 ## Models
 
 | Model | Input | Output | Architecture |

applications/cytoland/examples/VS_model_inference/demo_vscyto2d.py

@@ -0,0 +1,146 @@
+# %% [markdown]
+"""
+# 2D Virtual Staining of A549 Cells
+---
+## Prediction using the VSCyto2D to predict nuclei and plasma membrane from phase.
+This example shows how to virtually stain A549 cells using the _VSCyto2D_ model.
+The model is trained to predict the membrane and nuclei channels from the phase channel.
+"""
+
+# %% Imports and paths
+from pathlib import Path
+
+from iohub import open_ome_zarr
+from plot import plot_vs_n_fluor
+
+# Cytoland and VisCy modular classes for the trainer and model
+from cytoland.engine import FcmaeUNet
+from viscy_data.hcs import HCSDataModule
+from viscy_transforms import NormalizeSampled
+from viscy_utils.callbacks import HCSPredictionWriter
+from viscy_utils.trainer import VisCyTrainer
+
+# %% [markdown] tags=[]
+#
+# <div class="alert alert-block alert-info">
+#
+# # Download the dataset and checkpoints for the VSCyto2D model
+#
+# - Download the VSCyto2D test dataset and model checkpoint from here: <br>
+# https://public.czbiohub.org/comp.micro/viscy
+# - Update the `input_data_path` and `model_ckpt_path` variables with the path to the downloaded files.
+# - Select a FOV (i.e 0/0/0).
+# - Set an output path for the predictions.
+#
+# </div>
+
+# %%
+# TODO: Set download paths
+root_dir = Path("")
+# TODO: modify the path to the downloaded dataset
+input_data_path = root_dir / "VSCyto2D/test/a549_hoechst_cellmask_test.zarr"
+# TODO: modify the path to the downloaded checkpoint
+model_ckpt_path = "/epoch=399-step=23200.ckpt"
+# TODO: modify the path
+# Zarr store to save the predictions
+output_path = root_dir / "./a549_prediction.zarr"
+# TODO: Choose an FOV
+fov = "0/0/0"
+
+input_data_path = input_data_path / fov
+
+# %%
+# Create the VSCyto2D network
+
+# Reduce the batch size if encountering out-of-memory errors
+BATCH_SIZE = 8
+# NOTE: Set the number of workers to 0 for Windows and macOS
+# since multiprocessing only works with a
+# `if __name__ == '__main__':` guard.
+# On Linux, set it to the number of CPU cores to maximize performance.
+NUM_WORKERS = 0
+phase_channel_name = "Phase3D"
+
+# %%[markdown]
+"""
+For this example we will use the following parameters:
+For more information on the VSCyto2D model,
+see ``viscy.unet.networks.fcmae``
+([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/unet/networks/fcmae.py))
+for configuration details.
+"""
+# %%
+# Setup the data module.
+data_module = HCSDataModule(
+    data_path=input_data_path,
+    source_channel=phase_channel_name,
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=1,
+    split_ratio=0.8,
+    batch_size=BATCH_SIZE,
+    num_workers=NUM_WORKERS,
+    normalizations=[
+        NormalizeSampled(
+            [phase_channel_name],
+            level="fov_statistics",
+            subtrahend="median",
+            divisor="iqr",
+        )
+    ],
+)
+data_module.prepare_data()
+data_module.setup(stage="predict")
+# %%
+# Setup the model.
+# Dictionary that specifies key parameters of the model.
+config_VSCyto2D = {
+    "in_channels": 1,
+    "out_channels": 2,
+    "encoder_blocks": [3, 3, 9, 3],
+    "dims": [96, 192, 384, 768],
+    "decoder_conv_blocks": 2,
+    "stem_kernel_size": [1, 2, 2],
+    "in_stack_depth": 1,
+    "pretraining": False,
+}
+
+model_VSCyto2D = FcmaeUNet.load_from_checkpoint(model_ckpt_path, model_config=config_VSCyto2D)
+model_VSCyto2D.eval()
+
+# %%
+# Setup the Trainer
+trainer = VisCyTrainer(
+    accelerator="gpu",
+    callbacks=[HCSPredictionWriter(output_path)],
+)
+
+# Start the predictions
+trainer.predict(
+    model=model_VSCyto2D,
+    datamodule=data_module,
+    return_predictions=False,
+)
+
+# %%
+# Open the output_zarr store and inspect the output
+# Show the individual channels and the fused in a 1x3 plot
+output_path = Path(output_path) / fov
+
+# %%
+# Open the predicted data
+vs_store = open_ome_zarr(output_path, mode="r")
+# Get the 2D images
+vs_nucleus = vs_store[0][0, 0, 0]  # (t,c,z,y,x)
+vs_membrane = vs_store[0][0, 1, 0]  # (t,c,z,y,x)
+# Open the experimental fluorescence
+fluor_store = open_ome_zarr(input_data_path, mode="r")
+# Get the 2D images
+# NOTE: Channel indices hardcoded for this dataset
+fluor_nucleus = fluor_store[0][0, 1, 0]  # (t,c,z,y,x)
+fluor_membrane = fluor_store[0][0, 2, 0]  # (t,c,z,y,x)
+
+# Plot
+plot_vs_n_fluor(vs_nucleus, vs_membrane, fluor_nucleus, fluor_membrane)
+
+vs_store.close()
+fluor_store.close()

applications/cytoland/examples/VS_model_inference/demo_vscyto3d.py

@@ -0,0 +1,158 @@
+# %% [markdown]
+"""
+# 3D Virtual Staining of HEK293T Cells
+---
+## Prediction using the VSCyto3D to predict nuclei and membrane from phase.
+This example shows how to virtually stain HEK293T cells using the _VSCyto3D_ model.
+The model is trained to predict the membrane and nuclei channels from the phase channel.
+"""
+
+# %% Imports and paths
+from pathlib import Path
+
+from iohub import open_ome_zarr
+from plot import plot_vs_n_fluor
+
+# Cytoland and VisCy modular classes for the trainer and model
+from cytoland.engine import VSUNet
+from viscy_data.hcs import HCSDataModule
+from viscy_transforms import NormalizeSampled
+from viscy_utils.callbacks import HCSPredictionWriter
+from viscy_utils.trainer import VisCyTrainer
+
+# %% [markdown]
+"""
+## Data and Model Paths
+
+The dataset and model checkpoint files need to be downloaded before running this example.
+"""
+
+# %% [markdown] tags=[]
+#
+# <div class="alert alert-block alert-info">
+#
+# # Download the dataset and checkpoints VSCyto3D
+#
+# - Download the VSCyto3D test dataset and model checkpoint from here: <br>
+# https://public.czbiohub.org/comp.micro/viscy
+# - Update the `input_data_path` and `model_ckpt_path` variables with the path to the downloaded files.
+# - Select a FOV (i.e plate/0/0).
+# - Set an output path for the predictions.
+#
+# </div>
+# %%
+# TODO: modify the path to the downloaded dataset
+input_data_path = "/no_pertubation_Phase1e-3_Denconv_Nuc8e-4_Mem8e-4_pad15_bg50.zarr"
+
+# TODO: modify the path to the downloaded checkpoint
+model_ckpt_path = "/epoch=48-step=18130.ckpt"
+
+# TODO: modify the path
+# Zarr store to save the predictions
+output_path = "./hek_prediction_3d.zarr"
+
+# TODO: Choose an FOV
+# FOV of interest
+fov = "plate/0/0"
+
+input_data_path = Path(input_data_path) / fov
+
+# %%
+# Create the VSCyto3D model
+
+# Reduce the batch size if encountering out-of-memory errors
+BATCH_SIZE = 2
+# NOTE: Set the number of workers to 0 for Windows and macOS
+# since multiprocessing only works with a
+# `if __name__ == '__main__':` guard.
+# On Linux, set it to the number of CPU cores to maximize performance.
+NUM_WORKERS = 0
+phase_channel_name = "Phase3D"
+
+# %%[markdown]
+"""
+For this example we will use the following parameters:
+### For more information on the VSCyto3D model:
+See ``viscy.unet.networks.unext2``
+([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/unet/networks/unext2.py))
+for configuration details.
+"""
+# %%
+# Setup the data module.
+data_module = HCSDataModule(
+    data_path=input_data_path,
+    source_channel=phase_channel_name,
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=5,
+    split_ratio=0.8,
+    batch_size=BATCH_SIZE,
+    num_workers=NUM_WORKERS,
+    normalizations=[
+        NormalizeSampled(
+            [phase_channel_name],
+            level="fov_statistics",
+            subtrahend="median",
+            divisor="iqr",
+        )
+    ],
+)
+data_module.prepare_data()
+data_module.setup(stage="predict")
+# %%
+# Setup the model.
+# Dictionary that specifies key parameters of the model.
+config_VSCyto3D = {
+    "in_channels": 1,
+    "out_channels": 2,
+    "in_stack_depth": 5,
+    "backbone": "convnextv2_tiny",
+    "stem_kernel_size": (5, 4, 4),
+    "decoder_mode": "pixelshuffle",
+    "head_expansion_ratio": 4,
+    "head_pool": True,
+}
+
+model_VSCyto3D = VSUNet.load_from_checkpoint(model_ckpt_path, architecture="UNeXt2", model_config=config_VSCyto3D)
+model_VSCyto3D.eval()
+
+# %%
+# Setup the Trainer
+trainer = VisCyTrainer(
+    accelerator="gpu",
+    callbacks=[HCSPredictionWriter(output_path)],
+)
+
+# Start the predictions
+trainer.predict(
+    model=model_VSCyto3D,
+    datamodule=data_module,
+    return_predictions=False,
+)
+
+# %%
+# Open the output_zarr store and inspect the output
+# Show the individual channels and the fused in a 1x3 plot
+output_path = Path(output_path) / fov
+
+# %%
+# Open the predicted data
+vs_store = open_ome_zarr(output_path, mode="r")
+T, C, Z, Y, X = vs_store.data.shape
+
+# Get a z-slice
+z_slice = Z // 2  # NOTE: using the middle slice of the stack. Change as needed.
+vs_nucleus = vs_store[0][0, 0, z_slice]  # (t,c,z,y,x)
+vs_membrane = vs_store[0][0, 1, z_slice]  # (t,c,z,y,x)
+# Open the experimental fluorescence
+fluor_store = open_ome_zarr(input_data_path, mode="r")
+# Get the 2D images
+# NOTE: Channel indices hardcoded for this dataset
+fluor_nucleus = fluor_store[0][0, 2, z_slice]  # (t,c,z,y,x)
+fluor_membrane = fluor_store[0][0, 1, z_slice]  # (t,c,z,y,x)
+
+# Plot
+plot_vs_n_fluor(vs_nucleus, vs_membrane, fluor_nucleus, fluor_membrane)
+
+# Close stores
+vs_store.close()
+fluor_store.close()