From ea12ff6e1c6f3c5db125c17329389898d2ee7fc0 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Fri, 18 Aug 2023 06:47:16 +0000
Subject: [PATCH 01/29] updated intro and paths

---
 docs/figures/phase_to_nuclei_membrane.png     | Bin 0 -> 1441863 bytes
 .../demo_dlmbl/python/convert-solution.py     |  41 ++
 examples/demo_dlmbl/python/exercise.ipynb     | 441 +++++++++++++++
 .../demo_dlmbl/python/prepare-exercise.sh     |  10 +
 examples/demo_dlmbl/python/solution.ipynb     | 519 ++++++++++++++++++
 .../python/{excercise_1.py => solution.py}    | 107 +++-
 pyproject.toml                                |   1 +
 7 files changed, 1092 insertions(+), 27 deletions(-)
 create mode 100644 docs/figures/phase_to_nuclei_membrane.png
 create mode 100644 examples/demo_dlmbl/python/convert-solution.py
 create mode 100644 examples/demo_dlmbl/python/exercise.ipynb
 create mode 100644 examples/demo_dlmbl/python/prepare-exercise.sh
 create mode 100644 examples/demo_dlmbl/python/solution.ipynb
 rename examples/demo_dlmbl/python/{excercise_1.py => solution.py} (54%)

diff --git a/docs/figures/phase_to_nuclei_membrane.png b/docs/figures/phase_to_nuclei_membrane.png
new file mode 100644
index 0000000000000000000000000000000000000000..52c627ceef767fc0c3368a78708541e0129a6cff
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diff --git a/examples/demo_dlmbl/python/convert-solution.py b/examples/demo_dlmbl/python/convert-solution.py
new file mode 100644
index 00000000..279f7874
--- /dev/null
+++ b/examples/demo_dlmbl/python/convert-solution.py
@@ -0,0 +1,41 @@
+import argparse
+from traitlets.config import Config
+import nbformat as nbf
+from nbconvert.preprocessors import TagRemovePreprocessor, ClearOutputPreprocessor
+from nbconvert.exporters import NotebookExporter
+
+
+def get_arg_parser():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('input_file')
+    parser.add_argument('output_file')
+
+    return parser
+
+
+def convert(input_file, output_file):
+    c = Config()
+    c.TagRemovePreprocessor.remove_cell_tags = ("solution",)
+    c.TagRemovePreprocessor.enabled = True
+    c.ClearOutputPreprocesser.enabled = True
+    c.NotebookExporter.preprocessors = [
+        "nbconvert.preprocessors.TagRemovePreprocessor",
+        "nbconvert.preprocessors.ClearOutputPreprocessor"
+    ]
+
+    exporter = NotebookExporter(config=c)
+    exporter.register_preprocessor(TagRemovePreprocessor(config=c), True)
+    exporter.register_preprocessor(ClearOutputPreprocessor(), True)
+
+    output = NotebookExporter(config=c).from_filename(input_file)
+    with open(output_file, 'w') as f:
+        f.write(output[0])
+
+
+if __name__ == "__main__":
+    parser = get_arg_parser()
+    args = parser.parse_args()
+
+    convert(args.input_file, args.output_file)
+    print(f'Converted {args.input_file} to {args.output_file}')
diff --git a/examples/demo_dlmbl/python/exercise.ipynb b/examples/demo_dlmbl/python/exercise.ipynb
new file mode 100644
index 00000000..fd9be2b8
--- /dev/null
+++ b/examples/demo_dlmbl/python/exercise.ipynb
@@ -0,0 +1,441 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "601f44e8",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "# Image translation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13ac88b9",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.\n",
+    "-----------------\n",
+    "\n",
+    "In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. \n",
+    "\n",
+    "Here, the source domain is label-free microscopy (material density) and the target domain is fluorescence microscopy (fluorophore density). The goal is to learn a mapping from the source domain to the target domain. We will use a deep convolutional neural network (CNN), specifically, a U-Net model with residual connections to learn the mapping. The preprocessing, training, prediction, evaluation, and deployment steps are unified in a computer vision pipeline for single-cell analysis that we call [VisCy](https://github.com/mehta-lab/VisCy).\n",
+    "\n",
+    "VisCy evolved from our previous work on virtual staining of cellular components from their density and anisotropy.\n",
+    "![](https://iiif.elifesciences.org/lax/55502%2Felife-55502-fig1-v2.tif/full/1500,/0/default.jpg)\n",
+    "\n",
+    "[Guo et al. (2020) Revealing architectural order with quantitative label-free imaging and deep learning\n",
+    ". eLife](https://elifesciences.org/articles/55502).\n",
+    "\n",
+    "VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04678353",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.\n",
+    "\n",
+    "![virtual_staining](docs/figures/phase_to_nuclei_membrane.png)\n",
+    "\n",
+    "The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:\n",
+    "* [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.\n",
+    "* Lunch break - The model will continue training during lunch.\n",
+    "* [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.\n",
+    "* [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.\n",
+    "\n",
+    "\"\n",
+    "Before you start,\n",
+    "\n",
+    "<div class=\"alert alert-danger\">\n",
+    "Set your python kernel to <span style=\"color:black;\">04-image-translation</span>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbce6388",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "(Part-1:tensorboard)=# Part 1: Visualize data and model using tensorboard, start training a model.\n",
+    "\n",
+    "Learning goals:\n",
+    "\n",
+    "- Load the and visualize the images from OME-Zarr\n",
+    "- Configure the data loader\n",
+    "- Initialize a 2D U-Net model for virtual staining\n",
+    "- Log the images and the model to tensorboard.\n",
+    "- Start training the model to predict nuclei and membrane from phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c65d3b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "from iohub import open_ome_zarr\n",
+    "from tensorboard import notebook\n",
+    "from torchview import draw_graph\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "from viscy.light.data import HCSDataModule\n",
+    "from viscy.light.engine import VSTrainer, VSUNet\n",
+    "\n",
+    "BATCH_SIZE = 32\n",
+    "GPU_ID = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "214d4782",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Load Dataset.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.1\n",
+    "\n",
+    "Use <a href=https://czbiohub-sf.github.io/iohub/main/api/ngff.html#open-ome-zarr>\n",
+    "<code>iohub.open_ome_zarr</code></a> to read the dataset.\n",
+    "\n",
+    "There should be 301 FOVs in the dataset (9.3 GB compressed).\n",
+    "\n",
+    "Each FOV consists of 3 channels of 2048x2048 images,\n",
+    "saved in the <a href=\"https://ngff.openmicroscopy.org/latest/#hcs-layout\">\n",
+    "High-Content Screening (HCS) layout</a>\n",
+    "specified by the Open Microscopy Environment Next Generation File Format\n",
+    "(OME-NGFF).\n",
+    "\n",
+    "Run <code>open_ome_zarr?</code> in a cell to see the docstring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6eaa0572",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# set dataset path here\n",
+    "data_path = Path(\n",
+    "    \"~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/\"\n",
+    ").expanduser()\n",
+    "\n",
+    "dataset = open_ome_zarr(data_path)\n",
+    "\n",
+    "print(len(list(dataset.positions())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6cdae27",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "View images with matplotlib.\n",
+    "\n",
+    "The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.\n",
+    "\n",
+    "\n",
+    "Note that labelling is not perfect,\n",
+    "as some cells are not expressing the fluorophore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27f84a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "row = \"0\"\n",
+    "col = \"0\"\n",
+    "field = \"42\"\n",
+    "# This dataset contains images at 3 resolutions.\n",
+    "# '0' is the highest resolution\n",
+    "# '1' is down-scaled 2x2,\n",
+    "# '2' is down-scaled 4x4.\n",
+    "# Such datasets are called image pyramids.\n",
+    "pyaramid_level = \"0\"\n",
+    "\n",
+    "# `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.\n",
+    "n_channels = len(dataset.channel_names)\n",
+    "\n",
+    "image = dataset[f\"{row}/{col}/{field}/{pyaramid_level}\"].numpy()\n",
+    "print(f\"data shape: {image.shape}, FOV: {field}, pyramid level: {pyaramid_level}\")\n",
+    "\n",
+    "figure, axes = plt.subplots(1, n_channels, figsize=(9, 3))\n",
+    "\n",
+    "for i in range(n_channels):\n",
+    "    for i in range(n_channels):\n",
+    "        channel_image = image[0, i, 0]\n",
+    "        # Adjust contrast to 0.5th and 99.5th percentile of pixel values.\n",
+    "        p1, p99 = np.percentile(channel_image, (0.5, 99.5))\n",
+    "        channel_image = np.clip(channel_image, p1, p99)\n",
+    "        axes[i].imshow(channel_image, cmap=\"gray\")\n",
+    "        axes[i].axis(\"off\")\n",
+    "        axes[i].set_title(dataset.channel_names[i])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c20a1ef5",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Configure the data loaders for training and validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbffa9bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_module = HCSDataModule(\n",
+    "    data_path,\n",
+    "    source_channel=\"Phase\",\n",
+    "    target_channel=[\"Nuclei\", \"Membrane\"],\n",
+    "    z_window_size=1,\n",
+    "    split_ratio=0.8,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    "    yx_patch_size=(256, 256),\n",
+    ")\n",
+    "\n",
+    "data_module.setup(\"fit\")\n",
+    "\n",
+    "print(len(data_module.train_dataset), len(data_module.val_dataset))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "538378a7",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.2\n",
+    "\n",
+    "Validate that the data can be loaded in batches correctly.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57b591e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataloader = data_module.train_dataloader()\n",
+    "\n",
+    "for i, batch in enumerate(train_dataloader):\n",
+    "    ...\n",
+    "    # plot one image from each of the batch and break\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1492bfd",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Construct a 2D U-Net for image translation.\n",
+    "\n",
+    "See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.\n",
+    "Increase the ``depth`` in ``draw_graph`` to zoom in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "415f0a91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_config = {\n",
+    "    \"architecture\": \"2D\",\n",
+    "    \"in_channels\": 1,\n",
+    "    \"out_channels\": 2,\n",
+    "    \"residual\": True,\n",
+    "    \"dropout\": 0.1,\n",
+    "    \"task\": \"reg\",\n",
+    "}\n",
+    "\n",
+    "model = VSUNet(\n",
+    "    model_config=model_config.copy(),\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    loss_function=torch.nn.functional.mse_loss,\n",
+    "    schedule=\"WarmupCosine\",\n",
+    "    log_num_samples=10,\n",
+    ")\n",
+    "\n",
+    "# visualize graph\n",
+    "model_graph = draw_graph(model, model.example_input_array, depth=2, device=\"cpu\")\n",
+    "graph = model_graph.visual_graph\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d4cf103",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Configure trainer class.\n",
+    "Here we use the ``fast_dev_run`` flag to run a sanity check first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb3f0848",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = VSTrainer(accelerator=\"gpu\", devices=[GPU_ID], fast_dev_run=True)\n",
+    "\n",
+    "trainer.fit(model, datamodule=data_module)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fc0dc30",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.3\n",
+    "\n",
+    "Modify the trainer to train the model for 20 epochs.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cbccde9",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Tips:\n",
+    "\n",
+    "- See ``VSTrainer?`` for all the available parameters.\n",
+    "- Set ``default_root_dir`` to store the logs and checkpoints\n",
+    "in a specific directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ccba375",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Bonus:\n",
+    "\n",
+    "- Tweak model hyperparameters\n",
+    "- Adjust batch size to fully utilize the VRAM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27a7136e",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Launch TensorBoard with:\n",
+    "\n",
+    "```\n",
+    "%load_ext tensorboard\n",
+    "%tensorboard --logdir /path/to/lightning_logs\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fe9cbda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "notebook.list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3855287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "notebook.display(port=6006, height=800)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3e91f8",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 1\n",
+    "\n",
+    "Now the training has started,\n",
+    "we can come back after a while and evaluate the performance!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "799048df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "all",
+   "main_language": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/demo_dlmbl/python/prepare-exercise.sh b/examples/demo_dlmbl/python/prepare-exercise.sh
new file mode 100644
index 00000000..be605ce9
--- /dev/null
+++ b/examples/demo_dlmbl/python/prepare-exercise.sh
@@ -0,0 +1,10 @@
+# Run black on .py files
+black solution.py
+
+# Convert .py to ipynb
+# "cell_metadata_filter": "all" preserve cell tags including our solution tags
+jupytext --to ipynb --update-metadata '{"jupytext": {"cell_metadata_filter":"all"}}' solution.py
+
+# Create the exercise notebook by removing cell outputs and deleting cells tagged with "solution"
+# There is a bug in the nbconvert cli so we need to use the python API instead
+python convert-solution.py solution.ipynb exercise.ipynb
diff --git a/examples/demo_dlmbl/python/solution.ipynb b/examples/demo_dlmbl/python/solution.ipynb
new file mode 100644
index 00000000..508be3e1
--- /dev/null
+++ b/examples/demo_dlmbl/python/solution.ipynb
@@ -0,0 +1,519 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "601f44e8",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "# Image translation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13ac88b9",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.\n",
+    "-----------------\n",
+    "\n",
+    "In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. \n",
+    "\n",
+    "Here, the source domain is label-free microscopy (material density) and the target domain is fluorescence microscopy (fluorophore density). The goal is to learn a mapping from the source domain to the target domain. We will use a deep convolutional neural network (CNN), specifically, a U-Net model with residual connections to learn the mapping. The preprocessing, training, prediction, evaluation, and deployment steps are unified in a computer vision pipeline for single-cell analysis that we call [VisCy](https://github.com/mehta-lab/VisCy).\n",
+    "\n",
+    "VisCy evolved from our previous work on virtual staining of cellular components from their density and anisotropy.\n",
+    "![](https://iiif.elifesciences.org/lax/55502%2Felife-55502-fig1-v2.tif/full/1500,/0/default.jpg)\n",
+    "\n",
+    "[Guo et al. (2020) Revealing architectural order with quantitative label-free imaging and deep learning\n",
+    ". eLife](https://elifesciences.org/articles/55502).\n",
+    "\n",
+    "VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04678353",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.\n",
+    "\n",
+    "![virtual_staining](docs/figures/phase_to_nuclei_membrane.png)\n",
+    "\n",
+    "The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:\n",
+    "* [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.\n",
+    "* Lunch break - The model will continue training during lunch.\n",
+    "* [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.\n",
+    "* [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.\n",
+    "\n",
+    "\"\n",
+    "Before you start,\n",
+    "\n",
+    "<div class=\"alert alert-danger\">\n",
+    "Set your python kernel to <span style=\"color:black;\">04-image-translation</span>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbce6388",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "(Part-1:tensorboard)=# Part 1: Visualize data and model using tensorboard, start training a model.\n",
+    "\n",
+    "Learning goals:\n",
+    "\n",
+    "- Load the and visualize the images from OME-Zarr\n",
+    "- Configure the data loader\n",
+    "- Initialize a 2D U-Net model for virtual staining\n",
+    "- Log the images and the model to tensorboard.\n",
+    "- Start training the model to predict nuclei and membrane from phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c65d3b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "from iohub import open_ome_zarr\n",
+    "from tensorboard import notebook\n",
+    "from torchview import draw_graph\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "from viscy.light.data import HCSDataModule\n",
+    "from viscy.light.engine import VSTrainer, VSUNet\n",
+    "\n",
+    "BATCH_SIZE = 32\n",
+    "GPU_ID = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "214d4782",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Load Dataset.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.1\n",
+    "\n",
+    "Use <a href=https://czbiohub-sf.github.io/iohub/main/api/ngff.html#open-ome-zarr>\n",
+    "<code>iohub.open_ome_zarr</code></a> to read the dataset.\n",
+    "\n",
+    "There should be 301 FOVs in the dataset (9.3 GB compressed).\n",
+    "\n",
+    "Each FOV consists of 3 channels of 2048x2048 images,\n",
+    "saved in the <a href=\"https://ngff.openmicroscopy.org/latest/#hcs-layout\">\n",
+    "High-Content Screening (HCS) layout</a>\n",
+    "specified by the Open Microscopy Environment Next Generation File Format\n",
+    "(OME-NGFF).\n",
+    "\n",
+    "Run <code>open_ome_zarr?</code> in a cell to see the docstring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6eaa0572",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# set dataset path here\n",
+    "data_path = Path(\n",
+    "    \"~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/\"\n",
+    ").expanduser()\n",
+    "\n",
+    "dataset = open_ome_zarr(data_path)\n",
+    "\n",
+    "print(len(list(dataset.positions())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6cdae27",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "View images with matplotlib.\n",
+    "\n",
+    "The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.\n",
+    "\n",
+    "\n",
+    "Note that labelling is not perfect,\n",
+    "as some cells are not expressing the fluorophore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27f84a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "row = \"0\"\n",
+    "col = \"0\"\n",
+    "field = \"42\"\n",
+    "# This dataset contains images at 3 resolutions.\n",
+    "# '0' is the highest resolution\n",
+    "# '1' is down-scaled 2x2,\n",
+    "# '2' is down-scaled 4x4.\n",
+    "# Such datasets are called image pyramids.\n",
+    "pyaramid_level = \"0\"\n",
+    "\n",
+    "# `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.\n",
+    "n_channels = len(dataset.channel_names)\n",
+    "\n",
+    "image = dataset[f\"{row}/{col}/{field}/{pyaramid_level}\"].numpy()\n",
+    "print(f\"data shape: {image.shape}, FOV: {field}, pyramid level: {pyaramid_level}\")\n",
+    "\n",
+    "figure, axes = plt.subplots(1, n_channels, figsize=(9, 3))\n",
+    "\n",
+    "for i in range(n_channels):\n",
+    "    for i in range(n_channels):\n",
+    "        channel_image = image[0, i, 0]\n",
+    "        # Adjust contrast to 0.5th and 99.5th percentile of pixel values.\n",
+    "        p1, p99 = np.percentile(channel_image, (0.5, 99.5))\n",
+    "        channel_image = np.clip(channel_image, p1, p99)\n",
+    "        axes[i].imshow(channel_image, cmap=\"gray\")\n",
+    "        axes[i].axis(\"off\")\n",
+    "        axes[i].set_title(dataset.channel_names[i])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c20a1ef5",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Configure the data loaders for training and validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbffa9bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_module = HCSDataModule(\n",
+    "    data_path,\n",
+    "    source_channel=\"Phase\",\n",
+    "    target_channel=[\"Nuclei\", \"Membrane\"],\n",
+    "    z_window_size=1,\n",
+    "    split_ratio=0.8,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    "    yx_patch_size=(256, 256),\n",
+    ")\n",
+    "\n",
+    "data_module.setup(\"fit\")\n",
+    "\n",
+    "print(len(data_module.train_dataset), len(data_module.val_dataset))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "538378a7",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.2\n",
+    "\n",
+    "Validate that the data can be loaded in batches correctly.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57b591e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataloader = data_module.train_dataloader()\n",
+    "\n",
+    "for i, batch in enumerate(train_dataloader):\n",
+    "    ...\n",
+    "    # plot one image from each of the batch and break\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0657c53f",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "train_dataloader = data_module.train_dataloader()\n",
+    "\n",
+    "\n",
+    "fig, axs = plt.subplots(3, 8, figsize=(20, 8))\n",
+    "\n",
+    "# Draw 8 batches, each with 32 images. Show the first image in each batch.\n",
+    "\n",
+    "for i, batch in enumerate(train_dataloader):\n",
+    "    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.\n",
+    "    # index is the tuple consisting of (image name, time, and z-slice)\n",
+    "    # source is the tensor of size 1x1x256x256\n",
+    "    # target is the tensor of size 2x1x256x256\n",
+    "\n",
+    "    if i >= 8:\n",
+    "        break\n",
+    "    FOV = batch[\"index\"][0][0]\n",
+    "    input_tensor = batch[\"source\"][0, 0, :, :].squeeze()\n",
+    "    target_nuclei_tensor = batch[\"target\"][0, 0, :, :].squeeze()\n",
+    "    target_membrane_tensor = batch[\"target\"][0, 1, :, :].squeeze()\n",
+    "\n",
+    "    axs[0, i].imshow(input_tensor, cmap=\"gray\")\n",
+    "    axs[1, i].imshow(target_nuclei_tensor, cmap=\"gray\")\n",
+    "    axs[2, i].imshow(target_membrane_tensor, cmap=\"gray\")\n",
+    "    axs[0, i].set_title(f\"input@{FOV}\")\n",
+    "    axs[1, i].set_title(\"target-nuclei\")\n",
+    "    axs[2, i].set_title(\"target-membrane\")\n",
+    "    axs[0, i].axis(\"off\")\n",
+    "    axs[1, i].axis(\"off\")\n",
+    "    axs[2, i].axis(\"off\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1492bfd",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Construct a 2D U-Net for image translation.\n",
+    "\n",
+    "See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.\n",
+    "Increase the ``depth`` in ``draw_graph`` to zoom in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "415f0a91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_config = {\n",
+    "    \"architecture\": \"2D\",\n",
+    "    \"in_channels\": 1,\n",
+    "    \"out_channels\": 2,\n",
+    "    \"residual\": True,\n",
+    "    \"dropout\": 0.1,\n",
+    "    \"task\": \"reg\",\n",
+    "}\n",
+    "\n",
+    "model = VSUNet(\n",
+    "    model_config=model_config.copy(),\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    loss_function=torch.nn.functional.mse_loss,\n",
+    "    schedule=\"WarmupCosine\",\n",
+    "    log_num_samples=10,\n",
+    ")\n",
+    "\n",
+    "# visualize graph\n",
+    "model_graph = draw_graph(model, model.example_input_array, depth=2, device=\"cpu\")\n",
+    "graph = model_graph.visual_graph\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d4cf103",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Configure trainer class.\n",
+    "Here we use the ``fast_dev_run`` flag to run a sanity check first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb3f0848",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = VSTrainer(accelerator=\"gpu\", devices=[GPU_ID], fast_dev_run=True)\n",
+    "\n",
+    "trainer.fit(model, datamodule=data_module)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fc0dc30",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.3\n",
+    "\n",
+    "Modify the trainer to train the model for 20 epochs.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cbccde9",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Tips:\n",
+    "\n",
+    "- See ``VSTrainer?`` for all the available parameters.\n",
+    "- Set ``default_root_dir`` to store the logs and checkpoints\n",
+    "in a specific directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ccba375",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Bonus:\n",
+    "\n",
+    "- Tweak model hyperparameters\n",
+    "- Adjust batch size to fully utilize the VRAM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3eeb3bd0",
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "wider_config = model_config | {\"num_filters\": [24, 48, 96, 192, 384]}\n",
+    "\n",
+    "model = model = VSUNet(\n",
+    "    model_config=wider_config.copy(),\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    loss_function=torch.nn.functional.mse_loss,\n",
+    "    schedule=\"WarmupCosine\",\n",
+    "    log_num_samples=10,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    max_epochs=20,\n",
+    "    log_every_n_steps=8,\n",
+    "    default_root_dir=os.path.expanduser(\"~\"),\n",
+    ")\n",
+    "\n",
+    "trainer.fit(model, datamodule=data_module)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27a7136e",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Launch TensorBoard with:\n",
+    "\n",
+    "```\n",
+    "%load_ext tensorboard\n",
+    "%tensorboard --logdir /path/to/lightning_logs\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fe9cbda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "notebook.list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3855287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "notebook.display(port=6006, height=800)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3e91f8",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 1\n",
+    "\n",
+    "Now the training has started,\n",
+    "we can come back after a while and evaluate the performance!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "799048df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "all",
+   "main_language": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/demo_dlmbl/python/excercise_1.py b/examples/demo_dlmbl/python/solution.py
similarity index 54%
rename from examples/demo_dlmbl/python/excercise_1.py
rename to examples/demo_dlmbl/python/solution.py
index 0904b75a..dd1aabf1 100644
--- a/examples/demo_dlmbl/python/excercise_1.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -1,22 +1,57 @@
 # %% [markdown]
 """
-# Image translation excercise part 1
+# Image translation
+"""
+# %% [markdown]
+"""
+Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.
+-----------------
 
-In this exercise, we will solve an image translation task of
-reconstructing nuclei and membrane markers from phase images of cells.
-Here, the source domain is label-free microscopy (average material density),
-and the target domain is fluorescence microscopy (fluorophore density).
+In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. 
 
-Learning goals of part 1:
+Here, the source domain is label-free microscopy (material density) and the target domain is fluorescence microscopy (fluorophore density). The goal is to learn a mapping from the source domain to the target domain. We will use a deep convolutional neural network (CNN), specifically, a U-Net model with residual connections to learn the mapping. The preprocessing, training, prediction, evaluation, and deployment steps are unified in a computer vision pipeline for single-cell analysis that we call [VisCy](https://github.com/mehta-lab/VisCy).
 
-- Load the and visualize the images from OME-Zarr
-- Configure the data loaders
-- Initialize a 2D U-Net model for virtual staining
+VisCy evolved from our previous work on virtual staining of cellular components from their density and anisotropy.
+![](https://iiif.elifesciences.org/lax/55502%2Felife-55502-fig1-v2.tif/full/1500,/0/default.jpg)
+
+[Guo et al. (2020) Revealing architectural order with quantitative label-free imaging and deep learning
+. eLife](https://elifesciences.org/articles/55502).
+
+VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive.
+"""
+
+# %% [markdown]
+"""
+Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.
+
+![virtual_staining](docs/figures/phase_to_nuclei_membrane.png)
+
+The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:
+* [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.
+* Lunch break - The model will continue training during lunch.
+* [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.
+* [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.
 
+"
+Before you start,
 
 <div class="alert alert-danger">
-Set your python kernel to <code>004-image-translation</code>
+Set your python kernel to <span style="color:black;">04-image-translation</span>
 </div>
+
+"""
+# %% [markdown]
+"""
+(Part-1:tensorboard)=# Part 1: Visualize data and model using tensorboard, start training a model.
+
+Learning goals:
+
+- Load the and visualize the images from OME-Zarr
+- Configure the data loader
+- Initialize a 2D U-Net model for virtual staining
+- Log the images and the model to tensorboard.
+- Start training the model to predict nuclei and membrane from phase.
+
 """
 
 # %%
@@ -26,6 +61,8 @@
 from tensorboard import notebook
 from torchview import draw_graph
 import os
+from pathlib import Path
+import numpy as np
 
 
 from viscy.light.data import HCSDataModule
@@ -58,7 +95,9 @@
 
 # %%
 # set dataset path here
-data_path = "/hpc/projects/comp.micro/virtual_staining/datasets/dlmbl/HEK_nuclei_membrane_pyramid.zarr"
+data_path = Path(
+    "~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/"
+).expanduser()
 
 dataset = open_ome_zarr(data_path)
 
@@ -69,7 +108,7 @@
 """
 View images with matplotlib.
 
-The layout on the disk is: row/col/field/resolution/timepoint/channel/z/y/x.
+The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.
 
 
 Note that labelling is not perfect,
@@ -80,19 +119,31 @@
 
 row = "0"
 col = "0"
-field = "0"
+field = "42"
+# This dataset contains images at 3 resolutions.
 # '0' is the highest resolution
-# '1' is 2x2 down-scaled, '2' is 4x4 down-scaled, etc.
-resolution = "0"
-image = dataset[f"{row}/{col}/{field}/{resolution}"].numpy()
-print(image.shape)
-
-figure, axes = plt.subplots(1, 3, figsize=(9, 3))
-
-for ax, channel in zip(axes, image[0, :, 0]):
-    ax.imshow(channel, cmap="gray")
-    ax.axis("off")
-
+# '1' is down-scaled 2x2,
+# '2' is down-scaled 4x4.
+# Such datasets are called image pyramids.
+pyaramid_level = "0"
+
+# `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.
+n_channels = len(dataset.channel_names)
+
+image = dataset[f"{row}/{col}/{field}/{pyaramid_level}"].numpy()
+print(f"data shape: {image.shape}, FOV: {field}, pyramid level: {pyaramid_level}")
+
+figure, axes = plt.subplots(1, n_channels, figsize=(9, 3))
+
+for i in range(n_channels):
+    for i in range(n_channels):
+        channel_image = image[0, i, 0]
+        # Adjust contrast to 0.5th and 99.5th percentile of pixel values.
+        p1, p99 = np.percentile(channel_image, (0.5, 99.5))
+        channel_image = np.clip(channel_image, p1, p99)
+        axes[i].imshow(channel_image, cmap="gray")
+        axes[i].axis("off")
+        axes[i].set_title(dataset.channel_names[i])
 plt.tight_layout()
 
 # %% [markdown]
@@ -138,7 +189,7 @@
 train_dataloader = data_module.train_dataloader()
 
 
-fig, axs = plt.subplots(3, 8, figsize=(20, 6))
+fig, axs = plt.subplots(3, 8, figsize=(20, 8))
 
 # Draw 8 batches, each with 32 images. Show the first image in each batch.
 
@@ -155,7 +206,6 @@
     target_nuclei_tensor = batch["target"][0, 0, :, :].squeeze()
     target_membrane_tensor = batch["target"][0, 1, :, :].squeeze()
 
-
     axs[0, i].imshow(input_tensor, cmap="gray")
     axs[1, i].imshow(target_nuclei_tensor, cmap="gray")
     axs[2, i].imshow(target_membrane_tensor, cmap="gray")
@@ -251,7 +301,10 @@
 
 
 trainer = VSTrainer(
-    accelerator="gpu", max_epochs=20, log_every_n_steps=8, default_root_dir=os.path.expanduser("~")
+    accelerator="gpu",
+    max_epochs=20,
+    log_every_n_steps=8,
+    default_root_dir=os.path.expanduser("~"),
 )
 
 trainer.fit(model, datamodule=data_module)
diff --git a/pyproject.toml b/pyproject.toml
index c0f73bd2..07aa9bcd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,7 @@ dependencies = [
     "ipykernel", # used by demo_dlmbl
     "graphviz", # used by demo_dlmbl
     "torchview", # used by demo_dlmbl
+    "pathlib",
 ]
 dynamic = ["version"]
 

From 5c9e7b834ae016537a0623afaa05e1154a1599a7 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Fri, 18 Aug 2023 22:47:30 +0000
Subject: [PATCH 02/29] updated figures, tested data loader

---
 examples/demo_dlmbl/python/solution.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index dd1aabf1..a1e510f0 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -1,11 +1,10 @@
 # %% [markdown]
 """
 # Image translation
-"""
-# %% [markdown]
-"""
+---
+
 Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.
------------------
+
 
 In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. 
 
@@ -18,13 +17,13 @@
 . eLife](https://elifesciences.org/articles/55502).
 
 VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive.
+
 """
 
 # %% [markdown]
 """
 Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.
-
-![virtual_staining](docs/figures/phase_to_nuclei_membrane.png)
+![HEK](https://github.com/mehta-lab/VisCy/blob/dlmbl2023/docs/figures/phase_to_nuclei_membrane.png)
 
 The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:
 * [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.
@@ -32,7 +31,6 @@
 * [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.
 * [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.
 
-"
 Before you start,
 
 <div class="alert alert-danger">
@@ -42,7 +40,8 @@
 """
 # %% [markdown]
 """
-(Part-1:tensorboard)=# Part 1: Visualize data and model using tensorboard, start training a model.
+(Part-1:tensorboard)
+# Part 1: Visualize data and model using tensorboard, start training a model.
 
 Learning goals:
 

From 3d7c5a35ca5f727fdd1bab83800f5269cd1a3f3e Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Sat, 19 Aug 2023 00:00:44 +0000
Subject: [PATCH 03/29]  setup.sh fetches correct dataset

---
 examples/demo_dlmbl/python/setup.sh | 33 +++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 examples/demo_dlmbl/python/setup.sh

diff --git a/examples/demo_dlmbl/python/setup.sh b/examples/demo_dlmbl/python/setup.sh
new file mode 100644
index 00000000..0e39bf28
--- /dev/null
+++ b/examples/demo_dlmbl/python/setup.sh
@@ -0,0 +1,33 @@
+#!/usr/bin/env -S bash -i
+
+# Create mamba environment
+mamba create --name 04_image_translation python=3.10
+
+# Install ipykernel in the environment.
+mamba install -y ipykernel --name 04_image_translation
+# Specifying the environment explicitly.
+# mamba activate sometimes doesn't work from within shell scripts.
+
+
+# Find path to the environment - mamba activate doesn't work from within shell scripts.
+ENV_PATH=$(conda info --envs | grep 04_image_translation | awk '{print $2}')
+# install viscy and its dependencies in the environment using pip.
+$ENV_PATH/bin/pip install "viscy[metrics] @ git+https://github.com/mehta-lab/viscy.git"
+
+
+# Store the code directory path.
+CODE_DIR=$(pwd)
+
+
+# Create data directory
+mkdir -p ~/data/04_image_translation
+cd ~/data/04_image_translation
+wget https://dl-at-mbl-2023-data.s3.us-east-2.amazonaws.com/DLMBL2023_image_translation_data_pyramid.tar.gz
+tar -xzf DLMBL2023_image_translation_data_pyramid.tar.gz 
+
+# Go back to the code directory
+cd $CODE_DIR
+
+
+# this didn't not work from within shell scripts on TA1 node even after mamba init.
+# mamba activate 04_image_translation 
\ No newline at end of file

From ce12fb5d6fa05613e585d67c1b3ca5f2f9d12009 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Sat, 19 Aug 2023 16:21:20 +0000
Subject: [PATCH 04/29] finalized the exercise outline

---
 examples/demo_dlmbl/python/solution.py | 190 +++++++++++++++++++------
 1 file changed, 148 insertions(+), 42 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index a1e510f0..ef97177e 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -16,48 +16,53 @@
 [Guo et al. (2020) Revealing architectural order with quantitative label-free imaging and deep learning
 . eLife](https://elifesciences.org/articles/55502).
 
-VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive.
+VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipeline, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive.
 
 """
 
 # %% [markdown]
 """
 Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.
-![HEK](https://github.com/mehta-lab/VisCy/blob/dlmbl2023/docs/figures/phase_to_nuclei_membrane.png)
+![HEK](https://github.com/mehta-lab/VisCy/blob/dlmbl2023/docs/figures/phase_to_nuclei_membrane.svg?raw=true)
 
-The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:
-* [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.
+The exercise is organized in 3 parts.
+
+* [Part 1](#1_phase2fluor) - Explore the data and model using tensorboard. Launch the training before lunch.
 * Lunch break - The model will continue training during lunch.
-* [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.
-* [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.
+* [Part 2](#2_fluor2phase) - Evaluate the training with tensorboard. Train a model to predict phase from fluorescence.
+* [Part 3, bonus](#3_tuning) - Tune the capacity of networks and other hyperparameters to improve the performance.
+
+<div class="alert alert-info">
+Each part should take ~1.5 hours, depending on your practice with python.
+During parts 2 and 3, please summarize the models you train and their performance in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z).
+At checkpoints 2 and 3, we will discuss your results!
+</div>
 
 Before you start,
 
 <div class="alert alert-danger">
 Set your python kernel to <span style="color:black;">04-image-translation</span>
 </div>
-
 """
-# %% [markdown]
+# %% [markdown] <a id='1_phase2fluor'></a>
 """
-(Part-1:tensorboard)
-# Part 1: Visualize data and model using tensorboard, start training a model.
+# Part 1: Visualize data using tensorboard, start training a model.
 
 Learning goals:
 
 - Load the and visualize the images from OME-Zarr
-- Configure the data loader
+- Configure and understand the data loader
+- Log the data to tensorboard.
 - Initialize a 2D U-Net model for virtual staining
-- Log the images and the model to tensorboard.
 - Start training the model to predict nuclei and membrane from phase.
 
 """
 
-# %%
+# %% Imports and paths
+
 import matplotlib.pyplot as plt
 import torch
 from iohub import open_ome_zarr
-from tensorboard import notebook
 from torchview import draw_graph
 import os
 from pathlib import Path
@@ -66,21 +71,26 @@
 
 from viscy.light.data import HCSDataModule
 from viscy.light.engine import VSTrainer, VSUNet
+from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
+from tensorboard import notebook  # for viewing tensorboard in notebook
 
-BATCH_SIZE = 32
-GPU_ID = 0
+# Paths to data and log directory
+data_path = Path(
+    "~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/"
+).expanduser()
+log_dir = Path("~/data/04_image_translation/logs/").expanduser()
 
 # %% [markdown]
 """
-Load Dataset.
+## Load Dataset.
 
 <div class="alert alert-info">
 Task 1.1
-
 Use <a href=https://czbiohub-sf.github.io/iohub/main/api/ngff.html#open-ome-zarr>
 <code>iohub.open_ome_zarr</code></a> to read the dataset.
+</div>
 
-There should be 301 FOVs in the dataset (9.3 GB compressed).
+There should be 301 FOVs in the dataset (12 GB compressed).
 
 Each FOV consists of 3 channels of 2048x2048 images,
 saved in the <a href="https://ngff.openmicroscopy.org/latest/#hcs-layout">
@@ -88,21 +98,16 @@
 specified by the Open Microscopy Environment Next Generation File Format
 (OME-NGFF).
 
-Run <code>open_ome_zarr?</code> in a cell to see the docstring.
-
 """
 
 # %%
 # set dataset path here
-data_path = Path(
-    "~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/"
-).expanduser()
+
 
 dataset = open_ome_zarr(data_path)
 
 print(len(list(dataset.positions())))
 
-
 # %% [markdown]
 """
 View images with matplotlib.
@@ -116,6 +121,7 @@
 
 # %%
 
+# Change the parameters below to visualize data.
 row = "0"
 col = "0"
 field = "42"
@@ -124,7 +130,7 @@
 # '1' is down-scaled 2x2,
 # '2' is down-scaled 4x4.
 # Such datasets are called image pyramids.
-pyaramid_level = "0"
+pyaramid_level = "2"
 
 # `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.
 n_channels = len(dataset.channel_names)
@@ -147,10 +153,13 @@
 
 # %% [markdown]
 """
-Configure the data loaders for training and validation.
+## Initialize the data loaders.
 """
 
 # %%
+
+BATCH_SIZE = 32
+
 data_module = HCSDataModule(
     data_path,
     source_channel="Phase",
@@ -169,10 +178,36 @@
 
 # %% [markdown]
 """
+## Initialize tensorboard.
+
 <div class="alert alert-info">
 Task 1.2
+Log the batches drawn with data loader to tensorboard.
+"""
 
-Validate that the data can be loaded in batches correctly.
+# %% [markdown]
+"""
+Launch TensorBoard :
+
+```
+%load_ext tensorboard
+%tensorboard --logdir model_log_dir
+```
+"""
+
+# %%
+notebook.list()
+
+# %%
+notebook.display(port=6006, height=800)
+
+
+# %% [markdown]
+"""
+<div class="alert alert-info">
+Task 1.2
+
+Understand the data loader.
 </div>
 """
 
@@ -180,10 +215,46 @@
 train_dataloader = data_module.train_dataloader()
 
 for i, batch in enumerate(train_dataloader):
+    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.
+    # index is the tuple consisting of (image name, time, and z-slice)
+    # source is the tensor of size 1x1x256x256
+    # target is the tensor of size 2x1x256x256
     ...
     # plot one image from each of the batch and break
     break
 
+# %% Load the data with tensorboard
+
+train_dataloader = data_module.train_dataloader()
+
+# create a SummaryWriter object to write to the tensorboard log
+writer = SummaryWriter()
+
+for i, batch in enumerate(train_dataloader):
+    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.
+    # index is the tuple consisting of (image name, time, and z-slice)
+    # source is the tensor of size 1x1x256x256
+    # target is the tensor of size 2x1x256x256
+
+    if i >= 5:
+        break
+    FOV = batch["index"][0][0]
+    input_tensor = batch["source"][0, 0, :, :].squeeze()
+    target_nuclei_tensor = batch["target"][0, 0, :, :].squeeze()
+    target_membrane_tensor = batch["target"][0, 1, :, :].squeeze()
+
+    # add the images to the tensorboard log
+    writer.add_images(f"input/{FOV}", input_tensor.unsqueeze(0), global_step=i)
+    writer.add_images(
+        f"target_nuclei/{FOV}", target_nuclei_tensor.unsqueeze(0), global_step=i
+    )
+    writer.add_images(
+        f"target_membrane/{FOV}", target_membrane_tensor.unsqueeze(0), global_step=i
+    )
+
+# close the SummaryWriter object
+writer.close()
+
 # %% tags=["solution"]
 train_dataloader = data_module.train_dataloader()
 
@@ -228,6 +299,8 @@
 """
 
 # %%
+
+
 model_config = {
     "architecture": "2D",
     "in_channels": 1,
@@ -257,6 +330,7 @@
 """
 
 # %%
+GPU_ID = 0
 trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
 
 trainer.fit(model, datamodule=data_module)
@@ -303,35 +377,67 @@
     accelerator="gpu",
     max_epochs=20,
     log_every_n_steps=8,
-    default_root_dir=os.path.expanduser("~"),
+    default_root_dir=model_log_dir,
 )
 
 trainer.fit(model, datamodule=data_module)
 
+
 # %% [markdown]
 """
-Launch TensorBoard with:
+<div class="alert alert-success">
+Checkpoint 1
 
-```
-%load_ext tensorboard
-%tensorboard --logdir /path/to/lightning_logs
-```
+Now the training has started,
+we can come back after a while and evaluate the performance!
+</div>
 """
 
-# %%
-notebook.list()
+# %% [markdown] <a id='1_fluor2phase'></a>
+"""
+# Part 2: Visualize the previous model and training with tensorboard. Train fluorescence to phase contrast translation model.
+--------------------------------------------------
 
-# %%
-notebook.display(port=6006, height=800)
+Learning goals:
+- Visualize the previous model and training with tensorboard
+- Train fluorescence to phase contrast translation model
+- Compare the performance of the two models.
+
+"""
 
 # %% [markdown]
 """
 <div class="alert alert-success">
-Checkpoint 1
+Checkpoint 2
+Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)
+
+Now that you have trained two models, let's think about the following questions:
+- What is the information content of each channel in the dataset?
+- How would you use image translation models?
+- What can you try to improve the performance of each model?
+
 
-Now the training has started,
-we can come back after a while and evaluate the performance!
 </div>
 """
 
-# %%
+# %% [markdown] <a id='3_tuning'></a>
+"""
+# Part 3: Tune the capacity of networks and other hyperparameters to improve the performance.
+--------------------------------------------------
+
+Learning goals:
+- Visualize the previous model and training with tensorboard
+- Train fluorescence to phase contrast translation model
+- Compare the performance of the two models.
+
+"""
+
+# %% [markdown]
+"""
+<div class="alert alert-success">
+Checkpoint 3
+
+Congratulations! You have trained several image translation models now!
+Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)
+</div>
+"""

From 6db67b69dd23653c1e781ca36a6d1adc43c11cc1 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Sun, 20 Aug 2023 07:05:32 +0000
Subject: [PATCH 05/29] semi-final exercise

---
 examples/demo_dlmbl/python/solution.py | 414 +++++++++++++++----------
 pyproject.toml                         |   3 +-
 2 files changed, 246 insertions(+), 171 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index ef97177e..dd3f752e 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -32,10 +32,12 @@
 * [Part 2](#2_fluor2phase) - Evaluate the training with tensorboard. Train a model to predict phase from fluorescence.
 * [Part 3, bonus](#3_tuning) - Tune the capacity of networks and other hyperparameters to improve the performance.
 
+
+📖 As you work through parts 2 and 3, please share the layouts of the models you train and their performance with everyone via [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z) 📖.
+
 <div class="alert alert-info">
-Each part should take ~1.5 hours, depending on your practice with python.
-During parts 2 and 3, please summarize the models you train and their performance in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z).
-At checkpoints 2 and 3, we will discuss your results!
+Our guesstimate is that each of the three parts will take ~1.5 hours, but don't rush parts 1 and 2 if you need more time with them.
+We will discuss your observations on google doc after checkpoints 2 and 3.
 </div>
 
 Before you start,
@@ -46,13 +48,14 @@
 """
 # %% [markdown] <a id='1_phase2fluor'></a>
 """
-# Part 1: Visualize data using tensorboard, start training a model.
+# Part 1: Visualize training data using tensorboard, start training a model.
+---------
 
 Learning goals:
 
-- Load the and visualize the images from OME-Zarr
-- Configure and understand the data loader
-- Log the data to tensorboard.
+- Load the OME-zarr dataset and examine the channels.
+- Configure and understand the data loader.
+- Log some patches to tensorboard.
 - Initialize a 2D U-Net model for virtual staining
 - Start training the model to predict nuclei and membrane from phase.
 
@@ -63,13 +66,16 @@
 import matplotlib.pyplot as plt
 import torch
 from iohub import open_ome_zarr
-from torchview import draw_graph
+import torchvision
+import torchview
 import os
 from pathlib import Path
 import numpy as np
 
 
-from viscy.light.data import HCSDataModule
+# HCSDataModule makes it easy to load data during training. 
+from viscy.light.data import HCSDataModule 
+# Trainer class and UNet.
 from viscy.light.engine import VSTrainer, VSUNet
 from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
 from tensorboard import notebook  # for viewing tensorboard in notebook
@@ -78,7 +84,20 @@
 data_path = Path(
     "~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/"
 ).expanduser()
-log_dir = Path("~/data/04_image_translation/logs/").expanduser()
+
+log_dir = (
+    Path("~/data/04_image_translation/logs/")
+    .expanduser()
+)
+
+# Create log directory if needed, and launch tensorboard
+log_dir.mkdir(parents=True, exist_ok=True)
+
+%reload_ext tensorboard
+%tensorboard --logdir {log_dir}
+
+
+
 
 # %% [markdown]
 """
@@ -87,7 +106,7 @@
 <div class="alert alert-info">
 Task 1.1
 Use <a href=https://czbiohub-sf.github.io/iohub/main/api/ngff.html#open-ome-zarr>
-<code>iohub.open_ome_zarr</code></a> to read the dataset.
+<code>iohub.open_ome_zarr</code></a> to read the dataset and explore several FOVs with matplotlib.
 </div>
 
 There should be 301 FOVs in the dataset (12 GB compressed).
@@ -98,39 +117,28 @@
 specified by the Open Microscopy Environment Next Generation File Format
 (OME-NGFF).
 
+The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.
+Notice that labelling of nuclei channel is not complete - some cells are not expressing the fluorescent protein.
+
 """
 
 # %%
-# set dataset path here
-
 
 dataset = open_ome_zarr(data_path)
 
-print(len(list(dataset.positions())))
-
-# %% [markdown]
-"""
-View images with matplotlib.
-
-The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.
-
-
-Note that labelling is not perfect,
-as some cells are not expressing the fluorophore.
-"""
+print(f"Number of positions:{len(list(dataset.positions()))}")
 
-# %%
-
-# Change the parameters below to visualize data.
+# Use the field and pyramid_level below to visualize data.
 row = "0"
 col = "0"
-field = "42"
+field = "23"
+
 # This dataset contains images at 3 resolutions.
 # '0' is the highest resolution
 # '1' is down-scaled 2x2,
 # '2' is down-scaled 4x4.
 # Such datasets are called image pyramids.
-pyaramid_level = "2"
+pyaramid_level = "0"
 
 # `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.
 n_channels = len(dataset.channel_names)
@@ -153,12 +161,27 @@
 
 # %% [markdown]
 """
-## Initialize the data loaders.
+## Initialize data loaders and see the samples in tensorboard.
+
+<div class="alert alert-info">
+Task 1.2
+Setup the data loader and log several batches to tensorboard.
+</div>`
+
+VisCy builds on top of PyTorch Lightning. PyTorch Lightning is a thin wrapper around PyTorch that allows rapid experimentation. It provides a [DataModule](https://lightning.ai/docs/pytorch/stable/data/datamodule.html) to handle loading and processing of data during training. VisCy provides a child class, `HCSDataModule` to make it intuitve to access data stored in the HCS layout.
+  
+The dataloader in `HCSDataModule` returns a batch of samples. A `batch` is a list of dictionaries. The length of the list is equal to the batch size. Each dictionary consists of following key-value pairs.
+- `source`: the input image, a tensor of size 1*1*Y*X
+- `target`: the target image, a tensor of size 2*1*Y*X
+- `index` : the tuple of (location of field in HCS layout, time, and z-slice) of the sample.
+
 """
 
 # %%
 
-BATCH_SIZE = 32
+BATCH_SIZE = 42 # 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.
+# More seriously, batch size does not have to be a power of 2. 
+# See: https://sebastianraschka.com/blog/2022/batch-size-2.html
 
 data_module = HCSDataModule(
     data_path,
@@ -169,145 +192,124 @@
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=(256, 256),
+    yx_patch_size=(512, 512), # larger patch size makes it easy to see augmentations.
+    augment = False # Turn off augmentation for now.
 )
-
 data_module.setup("fit")
 
-print(len(data_module.train_dataset), len(data_module.val_dataset))
-
-# %% [markdown]
-"""
-## Initialize tensorboard.
-
-<div class="alert alert-info">
-Task 1.2
-Log the batches drawn with data loader to tensorboard.
-"""
-
-# %% [markdown]
-"""
-Launch TensorBoard :
+print(
+    f"FOVs in training set: {len(data_module.train_dataset)}, FOVs in validation set:{len(data_module.val_dataset)}"
+)
 
-```
-%load_ext tensorboard
-%tensorboard --logdir model_log_dir
-```
-"""
+# %% Define a function to write a batch to tensorboard log.
+def log_batch_tensorboard(batch, batchno, writer, card_name):
+    """
+    Logs a batch of images to TensorBoard.
 
-# %%
-notebook.list()
+    Args:
+        batch (dict): A dictionary containing the batch of images to be logged.
+        writer (SummaryWriter): A TensorBoard SummaryWriter object.
+        card_name (str): The name of the card to be displayed in TensorBoard.
 
-# %%
-notebook.display(port=6006, height=800)
+    Returns:
+        None
+    """
+    batch_phase = batch['source'][:,:,0,:,:]  # batch_size x z_size x Y x X tensor. 
+    batch_membrane = batch['target'][:,1,0,:,:].unsqueeze(1) # batch_size x 1 x Y x X tensor.
+    batch_nuclei = batch['target'][:,0,0,:,:].unsqueeze(1) # batch_size x 1 x Y x X tensor.
 
+    p1, p99 = np.percentile(batch_membrane, (0.1, 99.9))
+    batch_membrane = np.clip((batch_membrane - p1) / (p99 - p1), 0, 1)
 
-# %% [markdown]
-"""
-<div class="alert alert-info">
-Task 1.2
+    p1, p99 = np.percentile(batch_nuclei,  (0.1, 99.9))
+    batch_nuclei = np.clip((batch_nuclei - p1) / (p99 - p1), 0, 1)
 
-Understand the data loader.
-</div>
-"""
+    p1, p99 = np.percentile(batch_phase,  (0.1, 99.9))
+    batch_phase = np.clip((batch_phase - p1) / (p99 - p1), 0, 1)
+    
+    [N,C,H,W] = batch_phase.shape
+    interleaved_images = torch.zeros((3*N,C,H,W),dtype = batch_phase.dtype)
+    interleaved_images[0::3,:]=batch_phase
+    interleaved_images[1::3,:]=batch_nuclei
+    interleaved_images[2::3,:]=batch_membrane
 
-# %%
-train_dataloader = data_module.train_dataloader()
+    grid=torchvision.utils.make_grid(interleaved_images, nrow=3)
 
-for i, batch in enumerate(train_dataloader):
-    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.
-    # index is the tuple consisting of (image name, time, and z-slice)
-    # source is the tensor of size 1x1x256x256
-    # target is the tensor of size 2x1x256x256
-    ...
-    # plot one image from each of the batch and break
-    break
+    # add the grid to tensorboard
+    writer.add_image(card_name, grid, batchno)
+    
 
-# %% Load the data with tensorboard
+# %% Log a batch and an epoch to tensorboard.
 
+writer = SummaryWriter(log_dir = f"{log_dir}/view_batch")
 train_dataloader = data_module.train_dataloader()
 
-# create a SummaryWriter object to write to the tensorboard log
-writer = SummaryWriter()
+# Draw a batch and write to tensorboard. 
+batch = next(iter(train_dataloader))
+log_batch_tensorboard(batch, 0, writer, "augmentation/none")
 
 for i, batch in enumerate(train_dataloader):
-    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.
-    # index is the tuple consisting of (image name, time, and z-slice)
-    # source is the tensor of size 1x1x256x256
-    # target is the tensor of size 2x1x256x256
-
-    if i >= 5:
-        break
-    FOV = batch["index"][0][0]
-    input_tensor = batch["source"][0, 0, :, :].squeeze()
-    target_nuclei_tensor = batch["target"][0, 0, :, :].squeeze()
-    target_membrane_tensor = batch["target"][0, 1, :, :].squeeze()
-
-    # add the images to the tensorboard log
-    writer.add_images(f"input/{FOV}", input_tensor.unsqueeze(0), global_step=i)
-    writer.add_images(
-        f"target_nuclei/{FOV}", target_nuclei_tensor.unsqueeze(0), global_step=i
-    )
-    writer.add_images(
-        f"target_membrane/{FOV}", target_membrane_tensor.unsqueeze(0), global_step=i
-    )
-
-# close the SummaryWriter object
+    log_batch_tensorboard(batch, i, writer, "augmentation/none")
 writer.close()
 
-# %% tags=["solution"]
-train_dataloader = data_module.train_dataloader()
+# %% Use the following if you need to bring up the tensorboard session again
+# notebook.list()
+# notebook.display(port=6006, height=800)
 
+# %% [markdown]
+"""
+## View augmentations using tensorboard.
 
-fig, axs = plt.subplots(3, 8, figsize=(20, 8))
+<div class="alert alert-info">
+Task 1.3
+Turn on augmentation and view the batch in tensorboard.
+"""
+# %% 
+# data_module.augment = ...
+# data_module.batch_size = ...
+# ... # Feel free to adjust a few other parameters of data_module.
+# data_module.setup("fit")
+# train_dataloader = data_module.train_dataloader()
+# # Draw batches and write to tensorboard
+# writer = SummaryWriter(log_dir = f"{log_dir}/view_batch")
+# for i, batch in enumerate(train_dataloader):
+#     log_batch_tensorboard(...)
+# writer.close()
 
-# Draw 8 batches, each with 32 images. Show the first image in each batch.
+# %% tags=["solution"]
+data_module.augment = True
+data_module.batch_size = 21
+data_module.split_ratio = 0.8
+data_module.setup("fit")
 
+train_dataloader = data_module.train_dataloader()
+# Draw batches and write to tensorboard
+writer = SummaryWriter(log_dir = f"{log_dir}/view_batch")
 for i, batch in enumerate(train_dataloader):
-    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.
-    # index is the tuple consisting of (image name, time, and z-slice)
-    # source is the tensor of size 1x1x256x256
-    # target is the tensor of size 2x1x256x256
-
-    if i >= 8:
-        break
-    FOV = batch["index"][0][0]
-    input_tensor = batch["source"][0, 0, :, :].squeeze()
-    target_nuclei_tensor = batch["target"][0, 0, :, :].squeeze()
-    target_membrane_tensor = batch["target"][0, 1, :, :].squeeze()
-
-    axs[0, i].imshow(input_tensor, cmap="gray")
-    axs[1, i].imshow(target_nuclei_tensor, cmap="gray")
-    axs[2, i].imshow(target_membrane_tensor, cmap="gray")
-    axs[0, i].set_title(f"input@{FOV}")
-    axs[1, i].set_title("target-nuclei")
-    axs[2, i].set_title("target-membrane")
-    axs[0, i].axis("off")
-    axs[1, i].axis("off")
-    axs[2, i].axis("off")
-
-plt.tight_layout()
-plt.show()
-
+    log_batch_tensorboard(batch, i, writer, "augmentation/some")
+writer.close()
 
 # %% [markdown]
 """
-Construct a 2D U-Net for image translation.
-
+##  Construct a 2D U-Net for image translation.
 See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.
-Increase the ``depth`` in ``draw_graph`` to zoom in.
+We setup a fresh data module and instantiate the trainer class.
 """
 
-# %%
+# %% The entire training loop is contained in this block.
+
+GPU_ID = 0
+BATCH_SIZE = 32
 
 
+# Dictionary that specifies key parameters of the model.
 model_config = {
     "architecture": "2D",
     "in_channels": 1,
     "out_channels": 2,
     "residual": True,
-    "dropout": 0.1,
-    "task": "reg",
+    "dropout": 0.1, # dropout randomly turns off weights to avoid overfitting of the model to data.
+    "task": "reg", # reg = regression task.
 }
 
 model = VSUNet(
@@ -318,66 +320,58 @@
     log_num_samples=10,
 )
 
-# visualize graph
-model_graph = draw_graph(model, model.example_input_array, depth=2, device="cpu")
-graph = model_graph.visual_graph
-graph
-
-# %% [markdown]
-"""
-Configure trainer class.
-Here we use the ``fast_dev_run`` flag to run a sanity check first.
-"""
-
-# %%
-GPU_ID = 0
+# Reinitialize the data module. 
+data_module = HCSDataModule(
+    data_path,
+    source_channel="Phase",
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=1,
+    split_ratio=0.8,
+    batch_size=BATCH_SIZE,
+    num_workers=8,
+    architecture="2D",
+    yx_patch_size=(256, 256), 
+    augment = True 
+)
+data_module.setup("fit")
+# fast_dev_run runs a single batch of data through the model to check for errors.
 trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
 
+# trainer class takes the model and the data module as inputs.
 trainer.fit(model, datamodule=data_module)
 
 # %% [markdown]
 """
 <div class="alert alert-info">
-Task 1.3
-
-Modify the trainer to train the model for 20 epochs.
+Task 1.4
+Setup the training for ~50 epochs
 </div>
-"""
 
-# %% [markdown]
-"""
 Tips:
-
-- See ``VSTrainer?`` for all the available parameters.
 - Set ``default_root_dir`` to store the logs and checkpoints
 in a specific directory.
 """
 
-# %% [markdown]
-"""
-Bonus:
-
-- Tweak model hyperparameters
-- Adjust batch size to fully utilize the VRAM
-"""
-
 # %% tags=["solution"]
 wider_config = model_config | {"num_filters": [24, 48, 96, 192, 384]}
 
 model = model = VSUNet(
     model_config=wider_config.copy(),
     batch_size=BATCH_SIZE,
-    loss_function=torch.nn.functional.mse_loss,
+    loss_function=torch.nn.functional.mse_loss, # mean square error.
     schedule="WarmupCosine",
     log_num_samples=10,
 )
 
+n_samples = len(data_module.train_dataset)
+steps_per_epoch = n_samples // BATCH_SIZE
+n_epochs = 50
 
 trainer = VSTrainer(
     accelerator="gpu",
-    max_epochs=20,
-    log_every_n_steps=8,
-    default_root_dir=model_log_dir,
+    max_epochs=n_epochs,
+    log_every_n_steps= steps_per_epoch,
+    default_root_dir=Path(log_dir,"phase2fluor"),
 )
 
 trainer.fit(model, datamodule=data_module)
@@ -405,6 +399,71 @@
 
 """
 
+# %% 
+
+# visualize graph. 
+model_graph = torchview.draw_graph(model, data_module.train_dataset[0]['source'], depth=2, device="cpu")
+# Increase the depth to zoom in.
+
+graph = model_graph.visual_graph
+graph
+
+# %% tags = ["solution"]
+
+GPU_ID = 0
+BATCH_SIZE = 32
+
+data_module = HCSDataModule(
+    data_path,
+    source_channel="Nuclei",
+    target_channel="Phase",
+    z_window_size=1,
+    split_ratio=0.8,
+    batch_size=BATCH_SIZE,
+    num_workers=8,
+    architecture="2D",
+    yx_patch_size=(256, 256), 
+    augment = True 
+)
+data_module.setup("fit")
+
+
+# Dictionary that specifies key parameters of the model.
+model_config = {
+    "architecture": "2D",
+    "in_channels": 1,
+    "out_channels": 1,
+    "residual": True,
+    "dropout": 0.1, # dropout randomly turns off weights to avoid overfitting of the model to data.
+    "task": "reg", # reg = regression task.
+}
+
+wider_config = model_config | {"num_filters": [24, 48, 96, 192, 384]}
+
+
+model = VSUNet(
+    model_config=wider_config.copy(),
+    batch_size=BATCH_SIZE,
+    loss_function=torch.nn.functional.mae_loss,
+    schedule="WarmupCosine",
+    log_num_samples=10,
+)
+
+
+
+n_samples = len(data_module.train_dataset)
+steps_per_epoch = n_samples // BATCH_SIZE
+n_epochs = 50
+
+trainer = VSTrainer(
+    accelerator="gpu",
+    max_epochs=n_epochs,
+    log_every_n_steps= steps_per_epoch,
+    default_root_dir=Path(log_dir,"fluor2phase"),
+)
+
+trainer.fit(model, datamodule=data_module)
+
 # %% [markdown]
 """
 <div class="alert alert-success">
@@ -426,10 +485,9 @@
 --------------------------------------------------
 
 Learning goals:
-- Visualize the previous model and training with tensorboard
-- Train fluorescence to phase contrast translation model
-- Compare the performance of the two models.
 
+- Tweak model hyperparameters, primarily depth
+- Adjust batch size to fully utilize the VRAM
 """
 
 # %% [markdown]
@@ -441,3 +499,19 @@
 Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)
 </div>
 """
+# %% 
+
+# data_module_augmented = HCSDataModule(
+#     data_path,
+#     source_channel="Phase",
+#     target_channel=["Nuclei", "Membrane"],
+#     z_window_size=1,
+#     split_ratio=0.8,
+#     batch_size=21,
+#     num_workers=8,
+#     architecture="2D",
+#     yx_patch_size=(512, 512),
+#     augment = True # Turn on augmentations.
+# )
+
+# data_module_augmented.setup("fit")
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 07aa9bcd..0652ccf5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,8 @@ dependencies = [
     "ipykernel", # used by demo_dlmbl
     "graphviz", # used by demo_dlmbl
     "torchview", # used by demo_dlmbl
-    "pathlib",
+    "tensorboard", # used by demo_dlmbl
+    "pillow", # used by demo_dlmbl
 ]
 dynamic = ["version"]
 

From 3384751f60c6c19aeada2bc1f9df212e57e6a423 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Sun, 20 Aug 2023 14:06:44 +0000
Subject: [PATCH 06/29] parts 1 and 2 tested, part 3 outline ready

---
 examples/demo_dlmbl/python/solution.py | 30 +++++++++++---------------
 pyproject.toml                         |  4 +---
 2 files changed, 13 insertions(+), 21 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index dd3f752e..810e46d7 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -444,7 +444,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 model = VSUNet(
     model_config=wider_config.copy(),
     batch_size=BATCH_SIZE,
-    loss_function=torch.nn.functional.mae_loss,
+    loss_function=torch.nn.functional.mse_loss,
     schedule="WarmupCosine",
     log_num_samples=10,
 )
@@ -486,10 +486,20 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 Learning goals:
 
-- Tweak model hyperparameters, primarily depth
+- Tweak model hyperparameters, primarily depth and learning rate.
 - Adjust batch size to fully utilize the VRAM
 """
 
+# %% tags = ["solution"]
+# TODO: Tune the capacity of network
+
+# %% tags = ["solution"]
+# TODO: Tune the learning rate
+
+# %% tags = ["solution"]
+def plot_metrics(model, data_module):
+    # Compute metrics for validation dataset and show as box plot or similar.
+    
 # %% [markdown]
 """
 <div class="alert alert-success">
@@ -499,19 +509,3 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)
 </div>
 """
-# %% 
-
-# data_module_augmented = HCSDataModule(
-#     data_path,
-#     source_channel="Phase",
-#     target_channel=["Nuclei", "Membrane"],
-#     z_window_size=1,
-#     split_ratio=0.8,
-#     batch_size=21,
-#     num_workers=8,
-#     architecture="2D",
-#     yx_patch_size=(512, 512),
-#     augment = True # Turn on augmentations.
-# )
-
-# data_module_augmented.setup("fit")
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 0652ccf5..8e8f98a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,9 +23,7 @@ dependencies = [
     "ipykernel", # used by demo_dlmbl
     "graphviz", # used by demo_dlmbl
     "torchview", # used by demo_dlmbl
-    "tensorboard", # used by demo_dlmbl
-    "pillow", # used by demo_dlmbl
-]
+    ]
 dynamic = ["version"]
 
 [project.optional-dependencies]

From df68d0c2eefc0a803feb296ccf5d6618ac4f30f4 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Sun, 20 Aug 2023 21:28:09 +0000
Subject: [PATCH 07/29] clearer variables, train with larger patch size

---
 examples/demo_dlmbl/python/solution.py | 63 ++++++++++++--------------
 1 file changed, 29 insertions(+), 34 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 810e46d7..a9fd0b2c 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -299,12 +299,13 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # %% The entire training loop is contained in this block.
 
 GPU_ID = 0
-BATCH_SIZE = 32
+BATCH_SIZE = 10
 
 
 # Dictionary that specifies key parameters of the model.
-model_config = {
+phase2fluor_config = {
     "architecture": "2D",
+    "num_filters": [24, 48, 96, 192, 384],
     "in_channels": 1,
     "out_channels": 2,
     "residual": True,
@@ -312,16 +313,16 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     "task": "reg", # reg = regression task.
 }
 
-model = VSUNet(
-    model_config=model_config.copy(),
+phase2fluor_model = VSUNet(
+    model_config=phase2fluor_config.copy(),
     batch_size=BATCH_SIZE,
-    loss_function=torch.nn.functional.mse_loss,
+    loss_function=torch.nn.functional.l1_loss,
     schedule="WarmupCosine",
     log_num_samples=10,
 )
 
 # Reinitialize the data module. 
-data_module = HCSDataModule(
+phase2fluor_data = HCSDataModule(
     data_path,
     source_channel="Phase",
     target_channel=["Nuclei", "Membrane"],
@@ -330,15 +331,15 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=(256, 256), 
+    yx_patch_size=(512, 512), 
     augment = True 
 )
-data_module.setup("fit")
+phase2fluor_data.setup("fit")
 # fast_dev_run runs a single batch of data through the model to check for errors.
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs = 40,  fast_dev_run=True)
 
 # trainer class takes the model and the data module as inputs.
-trainer.fit(model, datamodule=data_module)
+trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
 # %% [markdown]
 """
@@ -353,28 +354,21 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 """
 
 # %% tags=["solution"]
-wider_config = model_config | {"num_filters": [24, 48, 96, 192, 384]}
 
-model = model = VSUNet(
-    model_config=wider_config.copy(),
-    batch_size=BATCH_SIZE,
-    loss_function=torch.nn.functional.mse_loss, # mean square error.
-    schedule="WarmupCosine",
-    log_num_samples=10,
-)
-
-n_samples = len(data_module.train_dataset)
-steps_per_epoch = n_samples // BATCH_SIZE
+GPU_ID = 0
+n_samples = len(phase2fluor_data.train_dataset)
+steps_per_epoch = n_samples // BATCH_SIZE # log data after so many steps.
 n_epochs = 50
 
 trainer = VSTrainer(
     accelerator="gpu",
+    devices=[GPU_ID],
     max_epochs=n_epochs,
     log_every_n_steps= steps_per_epoch,
     default_root_dir=Path(log_dir,"phase2fluor"),
 )
 
-trainer.fit(model, datamodule=data_module)
+trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
 
 # %% [markdown]
@@ -402,7 +396,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # %% 
 
 # visualize graph. 
-model_graph = torchview.draw_graph(model, data_module.train_dataset[0]['source'], depth=2, device="cpu")
+model_graph = torchview.draw_graph(phase2fluor_model, phase2fluor_data.train_dataset[0]['source'], depth=2, device="cpu")
 # Increase the depth to zoom in.
 
 graph = model_graph.visual_graph
@@ -410,10 +404,12 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # %% tags = ["solution"]
 
+
 GPU_ID = 0
-BATCH_SIZE = 32
+BATCH_SIZE = 10
 
-data_module = HCSDataModule(
+
+fluor2phase_data = HCSDataModule(
     data_path,
     source_channel="Nuclei",
     target_channel="Phase",
@@ -422,27 +418,25 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=(256, 256), 
+    yx_patch_size=(512, 512), 
     augment = True 
 )
-data_module.setup("fit")
+fluor2phase_data.setup("fit")
 
 
 # Dictionary that specifies key parameters of the model.
-model_config = {
+fluor2phase_config = {
     "architecture": "2D",
     "in_channels": 1,
     "out_channels": 1,
     "residual": True,
     "dropout": 0.1, # dropout randomly turns off weights to avoid overfitting of the model to data.
     "task": "reg", # reg = regression task.
+    "num_filters": [24, 48, 96, 192, 384],
 }
 
-wider_config = model_config | {"num_filters": [24, 48, 96, 192, 384]}
-
-
-model = VSUNet(
-    model_config=wider_config.copy(),
+fluor2phase_model = VSUNet(
+    model_config=fluor2phase_config.copy(),
     batch_size=BATCH_SIZE,
     loss_function=torch.nn.functional.mse_loss,
     schedule="WarmupCosine",
@@ -457,12 +451,13 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 trainer = VSTrainer(
     accelerator="gpu",
+    devices=[GPU_ID],
     max_epochs=n_epochs,
     log_every_n_steps= steps_per_epoch,
     default_root_dir=Path(log_dir,"fluor2phase"),
 )
 
-trainer.fit(model, datamodule=data_module)
+trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)
 
 # %% [markdown]
 """

From da1d0d6a52e8b42141b5ba1ec5685f8b15aed4dc Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 14:49:37 -0700
Subject: [PATCH 08/29] fix typo

---
 examples/demo_dlmbl/python/solution.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index a9fd0b2c..ba3f894b 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -126,7 +126,7 @@
 
 dataset = open_ome_zarr(data_path)
 
-print(f"Number of positions:{len(list(dataset.positions()))}")
+print(f"Number of positions: {len(list(dataset.positions()))}")
 
 # Use the field and pyramid_level below to visualize data.
 row = "0"
@@ -140,7 +140,7 @@
 # Such datasets are called image pyramids.
 pyaramid_level = "0"
 
-# `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.
+# `channel_names` is the metadata that is stored with data according to the OME-NGFF spec.
 n_channels = len(dataset.channel_names)
 
 image = dataset[f"{row}/{col}/{field}/{pyaramid_level}"].numpy()

From 20dc0aec0b1b7ae2d5695dcc224d41ba67f3ee14 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 14:52:06 -0700
Subject: [PATCH 09/29] clarify variable names

---
 examples/demo_dlmbl/python/solution.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index ba3f894b..d6c334d9 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -82,7 +82,7 @@
 
 # Paths to data and log directory
 data_path = Path(
-    "~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/"
+    "/hpc/projects/comp.micro/virtual_staining/datasets/dlmbl/HEK_nuclei_membrane_pyramid.zarr"
 ).expanduser()
 
 log_dir = (
@@ -152,8 +152,8 @@
     for i in range(n_channels):
         channel_image = image[0, i, 0]
         # Adjust contrast to 0.5th and 99.5th percentile of pixel values.
-        p1, p99 = np.percentile(channel_image, (0.5, 99.5))
-        channel_image = np.clip(channel_image, p1, p99)
+        p_low, p_high = np.percentile(channel_image, (0.5, 99.5))
+        channel_image = np.clip(channel_image, p_low, p_high)
         axes[i].imshow(channel_image, cmap="gray")
         axes[i].axis("off")
         axes[i].set_title(dataset.channel_names[i])

From cd36ba82687d3c530d1612d8b2b3eb677d836811 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Sun, 20 Aug 2023 21:52:44 +0000
Subject: [PATCH 10/29] trying to log graph

---
 examples/demo_dlmbl/python/solution.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index a9fd0b2c..9fc7e255 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -78,6 +78,7 @@
 # Trainer class and UNet.
 from viscy.light.engine import VSTrainer, VSUNet
 from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
+from pytorch_lightning.loggers import TensorBoardLogger #pytorch lightning wrapper for Tensorboard.
 from tensorboard import notebook  # for viewing tensorboard in notebook
 
 # Paths to data and log directory
@@ -341,6 +342,9 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # trainer class takes the model and the data module as inputs.
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
+# log graph to the same experiment.
+
+trainer.logger.log_graph(phase2fluor_model,phase2fluor_data.train_dataset[0]['source'])
 # %% [markdown]
 """
 <div class="alert alert-info">

From ff67b6cfbadd75e94ecdc8bd7dcb9aba48c01278 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 15:13:13 -0700
Subject: [PATCH 11/29] match example size with training

---
 examples/demo_dlmbl/python/solution.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index d6c334d9..9738c2e9 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -300,6 +300,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 GPU_ID = 0
 BATCH_SIZE = 10
+YX_PATCH_SIZE = (512, 512)
 
 
 # Dictionary that specifies key parameters of the model.
@@ -319,6 +320,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     loss_function=torch.nn.functional.l1_loss,
     schedule="WarmupCosine",
     log_num_samples=10,
+    example_input_yx_shape=YX_PATCH_SIZE
 )
 
 # Reinitialize the data module. 
@@ -331,7 +333,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=(512, 512), 
+    yx_patch_size=YX_PATCH_SIZE, 
     augment = True 
 )
 phase2fluor_data.setup("fit")

From 0170a28a2bc3181a92efba4f59c44478edd7314b Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 15:21:16 -0700
Subject: [PATCH 12/29] reuse globals

---
 examples/demo_dlmbl/python/solution.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index cdedb2a6..72223dd1 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -409,12 +409,6 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 graph
 
 # %% tags = ["solution"]
-
-
-GPU_ID = 0
-BATCH_SIZE = 10
-
-
 fluor2phase_data = HCSDataModule(
     data_path,
     source_channel="Nuclei",
@@ -424,12 +418,11 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=(512, 512), 
+    yx_patch_size=YX_PATCH_SIZE, 
     augment = True 
 )
 fluor2phase_data.setup("fit")
 
-
 # Dictionary that specifies key parameters of the model.
 fluor2phase_config = {
     "architecture": "2D",
@@ -449,8 +442,6 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     log_num_samples=10,
 )
 
-
-
 n_samples = len(data_module.train_dataset)
 steps_per_epoch = n_samples // BATCH_SIZE
 n_epochs = 50

From 7cd921e4b2681c7aa6cd4ddfc5f31f6e3eb21ce7 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 15:34:58 -0700
Subject: [PATCH 13/29] fix reference

---
 examples/demo_dlmbl/python/solution.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 72223dd1..0b5abcb4 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -442,7 +442,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     log_num_samples=10,
 )
 
-n_samples = len(data_module.train_dataset)
+n_samples = len(fluor2phase_data.train_dataset)
 steps_per_epoch = n_samples // BATCH_SIZE
 n_epochs = 50
 
@@ -483,7 +483,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 """
 
 # %% tags = ["solution"]
-# TODO: Tune the capacity of network
+
 
 # %% tags = ["solution"]
 # TODO: Tune the learning rate

From f7229a8717f3a585a03832faa01e8cb6dfc2429c Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 16:17:45 -0700
Subject: [PATCH 14/29] log sample images from the first batch

---
 viscy/light/engine.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/viscy/light/engine.py b/viscy/light/engine.py
index 074fee98..b5fac790 100644
--- a/viscy/light/engine.py
+++ b/viscy/light/engine.py
@@ -111,7 +111,8 @@ class VSUNet(LightningModule):
     :param Literal['WarmupCosine', 'Constant'] schedule:
         learning rate scheduler, defaults to "Constant"
     :param int log_num_samples:
-        number of image samples to log each training/validation epoch, defaults to 8
+        number of image samples to log each training/validation epoch,
+        has to be smaller than batch size, defaults to 8
     :param Sequence[int] example_input_yx_shape:
         XY shape of the example input for network graph tracing, defaults to (256, 256)
     :param str test_cellpose_model_path:
@@ -185,8 +186,8 @@ def training_step(self, batch: Sample, batch_idx: int):
             batch_size=self.batch_size,
             sync_dist=True,
         )
-        if batch_idx < self.log_num_samples:
-            self.training_step_outputs.append(
+        if batch_idx == 0:
+            self.training_step_outputs.extend(
                 self._detach_sample((source, target, pred))
             )
         return loss
@@ -197,8 +198,8 @@ def validation_step(self, batch: Sample, batch_idx: int):
         pred = self.forward(source)
         loss = self.loss_function(pred, target)
         self.log("loss/validate", loss, batch_size=self.batch_size, sync_dist=True)
-        if batch_idx < self.log_num_samples:
-            self.validation_step_outputs.append(
+        if batch_idx == 0:
+            self.validation_step_outputs.extend(
                 self._detach_sample((source, target, pred))
             )
 
@@ -341,9 +342,12 @@ def configure_optimizers(self):
             )
         return [optimizer], [scheduler]
 
-    @staticmethod
-    def _detach_sample(imgs: Sequence[torch.Tensor]):
-        return [np.squeeze(img[0].detach().cpu().numpy().max(axis=(1))) for img in imgs]
+    def _detach_sample(self, imgs: Sequence[torch.Tensor]):
+        num_samples = min(imgs[0].shape[0], self.log_num_samples)
+        return [
+            [np.squeeze(img[i].detach().cpu().numpy().max(axis=1)) for img in imgs]
+            for i in range(num_samples)
+        ]
 
     def _log_samples(self, key: str, imgs: Sequence[Sequence[np.ndarray]]):
         images_grid = []

From 1a3ca4abe3b5220c2b7c5d28a33cf8a1aeca897d Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 16:23:40 -0700
Subject: [PATCH 15/29] wider model

---
 examples/demo_dlmbl/python/solution.py | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 0b5abcb4..84335ed7 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -478,12 +478,33 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 Learning goals:
 
-- Tweak model hyperparameters, primarily depth and learning rate.
+- Tweak model hyperparameters, primarily width and learning rate.
 - Adjust batch size to fully utilize the VRAM
 """
 
 # %% tags = ["solution"]
+phase2fluor_wider_config = {
+    "architecture": "2D",
+    # double the number of filters at each stage
+    "num_filters": [48, 96, 192, 384, 768],
+    "in_channels": 1,
+    "out_channels": 2,
+    "residual": True,
+    "dropout": 0.1,
+    "task": "reg",
+}
+
+phase2fluor_wider_model = VSUNet(
+    model_config=phase2fluor_wider_config.copy(),
+    batch_size=BATCH_SIZE,
+    loss_function=torch.nn.functional.l1_loss,
+    schedule="WarmupCosine",
+    log_num_samples=10,
+    example_input_yx_shape=YX_PATCH_SIZE
+)
 
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
+trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
 
 # %% tags = ["solution"]
 # TODO: Tune the learning rate

From c342ec787446e42072349f1ac052c1d14a0332ed Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 16:26:58 -0700
Subject: [PATCH 16/29] low LR solution

---
 examples/demo_dlmbl/python/solution.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 84335ed7..3c027c2e 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -507,7 +507,19 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
 
 # %% tags = ["solution"]
-# TODO: Tune the learning rate
+phase2fluor_slow_model = VSUNet(
+    model_config=phase2fluor_config.copy(),
+    batch_size=BATCH_SIZE,
+    loss_function=torch.nn.functional.l1_loss,
+    # lower learning rate by 5 times
+    lr=2e-4,
+    schedule="WarmupCosine",
+    log_num_samples=10,
+    example_input_yx_shape=YX_PATCH_SIZE
+)
+
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
+trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)
 
 # %% tags = ["solution"]
 def plot_metrics(model, data_module):

From 9b030a3817c2620326de1df90f5982ebb6fcef14 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 16:34:09 -0700
Subject: [PATCH 17/29] fix path

---
 examples/demo_dlmbl/python/solution.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 3c027c2e..7ec5b848 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -83,7 +83,7 @@
 
 # Paths to data and log directory
 data_path = Path(
-    "/hpc/projects/comp.micro/virtual_staining/datasets/dlmbl/HEK_nuclei_membrane_pyramid.zarr"
+    Path("~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/")
 ).expanduser()
 
 log_dir = (
@@ -522,8 +522,8 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)
 
 # %% tags = ["solution"]
-def plot_metrics(model, data_module):
-    # Compute metrics for validation dataset and show as box plot or similar.
+def plot_metrics(model: VSUNet, data_module: HCSDataModule):
+    for batch in 
     
 # %% [markdown]
 """

From 0c963a4b6974528e5a85671d59d5727b9f5ebb45 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 16:41:42 -0700
Subject: [PATCH 18/29] seed everything

---
 examples/demo_dlmbl/python/solution.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 7ec5b848..b7b8f1c9 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -80,6 +80,9 @@
 from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
 from pytorch_lightning.loggers import TensorBoardLogger #pytorch lightning wrapper for Tensorboard.
 from tensorboard import notebook  # for viewing tensorboard in notebook
+from lightning.pytorch import seed_everything
+
+seed_everything(42, workers=True)
 
 # Paths to data and log directory
 data_path = Path(
@@ -503,7 +506,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     example_input_yx_shape=YX_PATCH_SIZE
 )
 
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True, seed)
 trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
 
 # %% tags = ["solution"]
@@ -520,10 +523,6 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
 trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)
-
-# %% tags = ["solution"]
-def plot_metrics(model: VSUNet, data_module: HCSDataModule):
-    for batch in 
     
 # %% [markdown]
 """

From 3e438f661e677e4779a66c5917596758ec89639b Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 18:14:44 -0700
Subject: [PATCH 19/29] fix test dataset without masks

---
 viscy/light/data.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/viscy/light/data.py b/viscy/light/data.py
index 870cdede..2b5ec029 100644
--- a/viscy/light/data.py
+++ b/viscy/light/data.py
@@ -430,12 +430,19 @@ def _setup_test(self, dataset_settings):
         if self.batch_size != 1:
             logging.warning(f"Ignoring batch size {self.batch_size} in test stage.")
         plate, normalize_transform = self._setup_eval(dataset_settings)
-        self.test_dataset = MaskTestDataset(
-            [p for _, p in plate.positions()],
-            transform=normalize_transform,
-            ground_truth_masks=self.ground_truth_masks,
-            **dataset_settings,
-        )
+        if self.ground_truth_masks:
+            self.test_dataset = MaskTestDataset(
+                [p for _, p in plate.positions()],
+                transform=normalize_transform,
+                ground_truth_masks=self.ground_truth_masks,
+                **dataset_settings,
+            )
+        else:
+            self.test_dataset = SlidingWindowDataset(
+                [p for _, p in plate.positions()],
+                transform=normalize_transform,
+                **dataset_settings,
+            )
 
     def _setup_predict(self, dataset_settings: dict):
         # track metadata for inverting transform

From 7d5130d04a3bd64be3a345609bf1d85fb12a7036 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Sun, 20 Aug 2023 18:22:05 -0700
Subject: [PATCH 20/29] metrics solution this needs a new test dataset

---
 examples/demo_dlmbl/python/solution.py | 172 ++++++++++++++++---------
 1 file changed, 110 insertions(+), 62 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index b7b8f1c9..5559c6da 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -63,24 +63,27 @@
 
 # %% Imports and paths
 
+from pathlib import Path
+
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 import torch
-from iohub import open_ome_zarr
-import torchvision
 import torchview
-import os
-from pathlib import Path
-import numpy as np
+import torchvision
+from iohub import open_ome_zarr
+from lightning.pytorch import seed_everything
+from lightning.pytorch.loggers import CSVLogger
+
+# pytorch lightning wrapper for Tensorboard.
+from tensorboard import notebook  # for viewing tensorboard in notebook
+from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
 
+# HCSDataModule makes it easy to load data during training.
+from viscy.light.data import HCSDataModule
 
-# HCSDataModule makes it easy to load data during training. 
-from viscy.light.data import HCSDataModule 
 # Trainer class and UNet.
 from viscy.light.engine import VSTrainer, VSUNet
-from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
-from pytorch_lightning.loggers import TensorBoardLogger #pytorch lightning wrapper for Tensorboard.
-from tensorboard import notebook  # for viewing tensorboard in notebook
-from lightning.pytorch import seed_everything
 
 seed_everything(42, workers=True)
 
@@ -89,10 +92,7 @@
     Path("~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/")
 ).expanduser()
 
-log_dir = (
-    Path("~/data/04_image_translation/logs/")
-    .expanduser()
-)
+log_dir = Path("~/data/04_image_translation/logs/").expanduser()
 
 # Create log directory if needed, and launch tensorboard
 log_dir.mkdir(parents=True, exist_ok=True)
@@ -101,8 +101,6 @@
 %tensorboard --logdir {log_dir}
 
 
-
-
 # %% [markdown]
 """
 ## Load Dataset.
@@ -183,8 +181,8 @@
 
 # %%
 
-BATCH_SIZE = 42 # 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.
-# More seriously, batch size does not have to be a power of 2. 
+BATCH_SIZE = 42  # 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.
+# More seriously, batch size does not have to be a power of 2.
 # See: https://sebastianraschka.com/blog/2022/batch-size-2.html
 
 data_module = HCSDataModule(
@@ -196,8 +194,8 @@
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=(512, 512), # larger patch size makes it easy to see augmentations.
-    augment = False # Turn off augmentation for now.
+    yx_patch_size=(512, 512),  # larger patch size makes it easy to see augmentations.
+    augment=False,  # Turn off augmentation for now.
 )
 data_module.setup("fit")
 
@@ -205,6 +203,7 @@
     f"FOVs in training set: {len(data_module.train_dataset)}, FOVs in validation set:{len(data_module.val_dataset)}"
 )
 
+
 # %% Define a function to write a batch to tensorboard log.
 def log_batch_tensorboard(batch, batchno, writer, card_name):
     """
@@ -218,37 +217,41 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     Returns:
         None
     """
-    batch_phase = batch['source'][:,:,0,:,:]  # batch_size x z_size x Y x X tensor. 
-    batch_membrane = batch['target'][:,1,0,:,:].unsqueeze(1) # batch_size x 1 x Y x X tensor.
-    batch_nuclei = batch['target'][:,0,0,:,:].unsqueeze(1) # batch_size x 1 x Y x X tensor.
+    batch_phase = batch["source"][:, :, 0, :, :]  # batch_size x z_size x Y x X tensor.
+    batch_membrane = batch["target"][:, 1, 0, :, :].unsqueeze(
+        1
+    )  # batch_size x 1 x Y x X tensor.
+    batch_nuclei = batch["target"][:, 0, 0, :, :].unsqueeze(
+        1
+    )  # batch_size x 1 x Y x X tensor.
 
     p1, p99 = np.percentile(batch_membrane, (0.1, 99.9))
     batch_membrane = np.clip((batch_membrane - p1) / (p99 - p1), 0, 1)
 
-    p1, p99 = np.percentile(batch_nuclei,  (0.1, 99.9))
+    p1, p99 = np.percentile(batch_nuclei, (0.1, 99.9))
     batch_nuclei = np.clip((batch_nuclei - p1) / (p99 - p1), 0, 1)
 
-    p1, p99 = np.percentile(batch_phase,  (0.1, 99.9))
+    p1, p99 = np.percentile(batch_phase, (0.1, 99.9))
     batch_phase = np.clip((batch_phase - p1) / (p99 - p1), 0, 1)
-    
-    [N,C,H,W] = batch_phase.shape
-    interleaved_images = torch.zeros((3*N,C,H,W),dtype = batch_phase.dtype)
-    interleaved_images[0::3,:]=batch_phase
-    interleaved_images[1::3,:]=batch_nuclei
-    interleaved_images[2::3,:]=batch_membrane
 
-    grid=torchvision.utils.make_grid(interleaved_images, nrow=3)
+    [N, C, H, W] = batch_phase.shape
+    interleaved_images = torch.zeros((3 * N, C, H, W), dtype=batch_phase.dtype)
+    interleaved_images[0::3, :] = batch_phase
+    interleaved_images[1::3, :] = batch_nuclei
+    interleaved_images[2::3, :] = batch_membrane
+
+    grid = torchvision.utils.make_grid(interleaved_images, nrow=3)
 
     # add the grid to tensorboard
     writer.add_image(card_name, grid, batchno)
-    
+
 
 # %% Log a batch and an epoch to tensorboard.
 
-writer = SummaryWriter(log_dir = f"{log_dir}/view_batch")
+writer = SummaryWriter(log_dir=f"{log_dir}/view_batch")
 train_dataloader = data_module.train_dataloader()
 
-# Draw a batch and write to tensorboard. 
+# Draw a batch and write to tensorboard.
 batch = next(iter(train_dataloader))
 log_batch_tensorboard(batch, 0, writer, "augmentation/none")
 
@@ -268,7 +271,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 Task 1.3
 Turn on augmentation and view the batch in tensorboard.
 """
-# %% 
+# %%
 # data_module.augment = ...
 # data_module.batch_size = ...
 # ... # Feel free to adjust a few other parameters of data_module.
@@ -288,7 +291,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 train_dataloader = data_module.train_dataloader()
 # Draw batches and write to tensorboard
-writer = SummaryWriter(log_dir = f"{log_dir}/view_batch")
+writer = SummaryWriter(log_dir=f"{log_dir}/view_batch")
 for i, batch in enumerate(train_dataloader):
     log_batch_tensorboard(batch, i, writer, "augmentation/some")
 writer.close()
@@ -314,8 +317,8 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     "in_channels": 1,
     "out_channels": 2,
     "residual": True,
-    "dropout": 0.1, # dropout randomly turns off weights to avoid overfitting of the model to data.
-    "task": "reg", # reg = regression task.
+    "dropout": 0.1,  # dropout randomly turns off weights to avoid overfitting of the model to data.
+    "task": "reg",  # reg = regression task.
 }
 
 phase2fluor_model = VSUNet(
@@ -324,10 +327,10 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     loss_function=torch.nn.functional.l1_loss,
     schedule="WarmupCosine",
     log_num_samples=10,
-    example_input_yx_shape=YX_PATCH_SIZE
+    example_input_yx_shape=YX_PATCH_SIZE,
 )
 
-# Reinitialize the data module. 
+# Reinitialize the data module.
 phase2fluor_data = HCSDataModule(
     data_path,
     source_channel="Phase",
@@ -337,19 +340,21 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=YX_PATCH_SIZE, 
-    augment = True 
+    yx_patch_size=YX_PATCH_SIZE,
+    augment=True,
 )
 phase2fluor_data.setup("fit")
 # fast_dev_run runs a single batch of data through the model to check for errors.
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs = 40,  fast_dev_run=True)
+trainer = VSTrainer(
+    accelerator="gpu", devices=[GPU_ID], max_epochs=40, fast_dev_run=True
+)
 
 # trainer class takes the model and the data module as inputs.
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
 # log graph to the same experiment.
 
-trainer.logger.log_graph(phase2fluor_model,phase2fluor_data.train_dataset[0]['source'])
+trainer.logger.log_graph(phase2fluor_model, phase2fluor_data.train_dataset[0]["source"])
 # %% [markdown]
 """
 <div class="alert alert-info">
@@ -366,15 +371,15 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 GPU_ID = 0
 n_samples = len(phase2fluor_data.train_dataset)
-steps_per_epoch = n_samples // BATCH_SIZE # log data after so many steps.
+steps_per_epoch = n_samples // BATCH_SIZE  # log data after so many steps.
 n_epochs = 50
 
 trainer = VSTrainer(
     accelerator="gpu",
     devices=[GPU_ID],
     max_epochs=n_epochs,
-    log_every_n_steps= steps_per_epoch,
-    default_root_dir=Path(log_dir,"phase2fluor"),
+    log_every_n_steps=steps_per_epoch,
+    default_root_dir=Path(log_dir, "phase2fluor"),
 )
 
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
@@ -402,10 +407,15 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 """
 
-# %% 
+# %%
 
-# visualize graph. 
-model_graph = torchview.draw_graph(phase2fluor_model, phase2fluor_data.train_dataset[0]['source'], depth=2, device="cpu")
+# visualize graph.
+model_graph = torchview.draw_graph(
+    phase2fluor_model,
+    phase2fluor_data.train_dataset[0]["source"],
+    depth=2,
+    device="cpu",
+)
 # Increase the depth to zoom in.
 
 graph = model_graph.visual_graph
@@ -421,8 +431,8 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     num_workers=8,
     architecture="2D",
-    yx_patch_size=YX_PATCH_SIZE, 
-    augment = True 
+    yx_patch_size=YX_PATCH_SIZE,
+    augment=True,
 )
 fluor2phase_data.setup("fit")
 
@@ -432,8 +442,8 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     "in_channels": 1,
     "out_channels": 1,
     "residual": True,
-    "dropout": 0.1, # dropout randomly turns off weights to avoid overfitting of the model to data.
-    "task": "reg", # reg = regression task.
+    "dropout": 0.1,  # dropout randomly turns off weights to avoid overfitting of the model to data.
+    "task": "reg",  # reg = regression task.
     "num_filters": [24, 48, 96, 192, 384],
 }
 
@@ -453,8 +463,8 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     accelerator="gpu",
     devices=[GPU_ID],
     max_epochs=n_epochs,
-    log_every_n_steps= steps_per_epoch,
-    default_root_dir=Path(log_dir,"fluor2phase"),
+    log_every_n_steps=steps_per_epoch,
+    default_root_dir=Path(log_dir, "fluor2phase"),
 )
 
 trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)
@@ -503,10 +513,10 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     loss_function=torch.nn.functional.l1_loss,
     schedule="WarmupCosine",
     log_num_samples=10,
-    example_input_yx_shape=YX_PATCH_SIZE
+    example_input_yx_shape=YX_PATCH_SIZE,
 )
 
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True, seed)
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
 trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
 
 # %% tags = ["solution"]
@@ -518,12 +528,50 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     lr=2e-4,
     schedule="WarmupCosine",
     log_num_samples=10,
-    example_input_yx_shape=YX_PATCH_SIZE
+    example_input_yx_shape=YX_PATCH_SIZE,
 )
 
 trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
 trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)
-    
+
+# %% tags = ["solution"]
+# name of the model variant
+model_version = "phase2fluor"
+save_dir = Path(log_dir, "test")
+test_data_path = Path("~/data/04_image_translation/test.zarr").expanduser()
+# FIXME: path to checkpoint
+ckpt_path = save_dir / Path("lightning_logs", model_version, "checkpoints", "...ckpt")
+test_data = HCSDataModule(
+    test_data_path,
+    source_channel="Phase",
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=1,
+    batch_size=1,
+    num_workers=8,
+    architecture="2D",
+)
+test_data.setup("test")
+trainer = VSTrainer(
+    accelerator="gpu",
+    devices=[GPU_ID],
+    logger=CSVLogger(save_dir=save_dir, version=model_version),
+)
+trainer.test(
+    phase2fluor_model,
+    datamodule=test_data,
+    ckpt_path=ckpt_path,
+)
+# read metrics and plot
+metrics = pd.read_csv(save_dir / Path("lightning_logs", model_version, "metrics.csv"))
+axes = metrics.boxplot(
+    column=[
+        "test_metrics/r2_step",
+        "test_metrics/pearson_step",
+        "test_metrics/SSIM_step",
+    ],
+    rot=30,
+)
+
 # %% [markdown]
 """
 <div class="alert alert-success">

From 9d29413a3b7e28982d093db2c0f9917a4445e83f Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Mon, 21 Aug 2023 04:19:08 +0000
Subject: [PATCH 21/29] fetch test data, compute metrics

---
 examples/demo_dlmbl/python/setup.sh    |  5 +++--
 examples/demo_dlmbl/python/solution.py | 27 ++++++++++++++------------
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/examples/demo_dlmbl/python/setup.sh b/examples/demo_dlmbl/python/setup.sh
index 0e39bf28..27ef9e1f 100644
--- a/examples/demo_dlmbl/python/setup.sh
+++ b/examples/demo_dlmbl/python/setup.sh
@@ -12,8 +12,7 @@ mamba install -y ipykernel --name 04_image_translation
 # Find path to the environment - mamba activate doesn't work from within shell scripts.
 ENV_PATH=$(conda info --envs | grep 04_image_translation | awk '{print $2}')
 # install viscy and its dependencies in the environment using pip.
-$ENV_PATH/bin/pip install "viscy[metrics] @ git+https://github.com/mehta-lab/viscy.git"
-
+$ENV_PATH/bin/pip install "viscy[metrics] @ git+https://github.com/mehta-lab/viscy.git@dlmbl2023"
 
 # Store the code directory path.
 CODE_DIR=$(pwd)
@@ -23,7 +22,9 @@ CODE_DIR=$(pwd)
 mkdir -p ~/data/04_image_translation
 cd ~/data/04_image_translation
 wget https://dl-at-mbl-2023-data.s3.us-east-2.amazonaws.com/DLMBL2023_image_translation_data_pyramid.tar.gz
+wget https://dl-at-mbl-2023-data.s3.us-east-2.amazonaws.com/DLMBL2023_image_translation_test.tar.gz 
 tar -xzf DLMBL2023_image_translation_data_pyramid.tar.gz 
+tar -xzf DLMBL2023_image_translation_test.tar.gz
 
 # Go back to the code directory
 cd $CODE_DIR
diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 5559c6da..36328287 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -303,7 +303,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 We setup a fresh data module and instantiate the trainer class.
 """
 
-# %% The entire training loop is contained in this block.
+# %% The entire training loop is contained in this cell.
 
 GPU_ID = 0
 BATCH_SIZE = 10
@@ -352,9 +352,6 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # trainer class takes the model and the data module as inputs.
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
-# log graph to the same experiment.
-
-trainer.logger.log_graph(phase2fluor_model, phase2fluor_data.train_dataset[0]["source"])
 # %% [markdown]
 """
 <div class="alert alert-info">
@@ -371,17 +368,22 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 GPU_ID = 0
 n_samples = len(phase2fluor_data.train_dataset)
-steps_per_epoch = n_samples // BATCH_SIZE  # log data after so many steps.
-n_epochs = 50
+steps_per_epoch = n_samples // BATCH_SIZE  # steps per epoch.
+n_epochs = 30
 
 trainer = VSTrainer(
     accelerator="gpu",
     devices=[GPU_ID],
     max_epochs=n_epochs,
-    log_every_n_steps=steps_per_epoch,
+    log_every_n_steps=steps_per_epoch // 2, 
+    # log losses and image samples 2 times per epoch.
     default_root_dir=Path(log_dir, "phase2fluor"),
+    
 )
 
+# Log graph
+trainer.logger.log_graph(phase2fluor_model, phase2fluor_data.train_dataset[0]["source"])
+# Launch training.
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
 
@@ -453,11 +455,12 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     loss_function=torch.nn.functional.mse_loss,
     schedule="WarmupCosine",
     log_num_samples=10,
+    example_input_yx_shape=YX_PATCH_SIZE,
 )
 
 n_samples = len(fluor2phase_data.train_dataset)
 steps_per_epoch = n_samples // BATCH_SIZE
-n_epochs = 50
+n_epochs = 30
 
 trainer = VSTrainer(
     accelerator="gpu",
@@ -466,7 +469,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     log_every_n_steps=steps_per_epoch,
     default_root_dir=Path(log_dir, "fluor2phase"),
 )
-
+trainer.logger.log_graph(fluor2phase_model, fluor2phase_data.train_dataset[0]["source"])
 trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)
 
 # %% [markdown]
@@ -538,9 +541,9 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # name of the model variant
 model_version = "phase2fluor"
 save_dir = Path(log_dir, "test")
-test_data_path = Path("~/data/04_image_translation/test.zarr").expanduser()
+test_data_path = Path("~/data/04_image_translation/HEK_nuclei_membrane_test.zarr").expanduser()
 # FIXME: path to checkpoint
-ckpt_path = save_dir / Path("lightning_logs", model_version, "checkpoints", "...ckpt")
+ckpt_path = Path(r"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt")
 test_data = HCSDataModule(
     test_data_path,
     source_channel="Phase",
@@ -562,7 +565,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     ckpt_path=ckpt_path,
 )
 # read metrics and plot
-metrics = pd.read_csv(save_dir / Path("lightning_logs", model_version, "metrics.csv"))
+metrics = pd.read_csv(Path(save_dir, "lightning_logs", model_version, "metrics.csv"))
 axes = metrics.boxplot(
     column=[
         "test_metrics/r2_step",

From 0cad8561392c2dfb6abf797203d721b6afb9740f Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Mon, 21 Aug 2023 04:24:49 +0000
Subject: [PATCH 22/29] byass cellpose import error due to numpy version
 conflicts

---
 viscy/light/engine.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/viscy/light/engine.py b/viscy/light/engine.py
index b5fac790..37cdb344 100644
--- a/viscy/light/engine.py
+++ b/viscy/light/engine.py
@@ -310,11 +310,17 @@ def on_validation_epoch_end(self):
     def on_test_start(self):
         """Load CellPose model for segmentation."""
         if CellposeModel is None:
-            raise ImportError(
+            # raise ImportError(
+            #     "CellPose not installed. "
+            #     "Please install the metrics dependency with "
+            #     '`pip install viscy".[metrics]"`'
+            # )
+            logging.warning(
                 "CellPose not installed. "
                 "Please install the metrics dependency with "
-                '`pip install viscy".[metrics]"`'
+                '`pip install viscy"[metrics]"`'
             )
+
         if self.test_cellpose_model_path is not None:
             self.cellpose_model = CellposeModel(
                 model_type=self.test_cellpose_model_path, device=self.device

From 46ec10a61866654fc2c79ee66721002dc6b3dd33 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Mon, 21 Aug 2023 12:17:35 +0000
Subject: [PATCH 23/29] final exercise

---
 examples/demo_dlmbl/python/solution.py | 274 ++++++++++++++++---------
 1 file changed, 173 insertions(+), 101 deletions(-)

diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/python/solution.py
index 36328287..83be3748 100644
--- a/examples/demo_dlmbl/python/solution.py
+++ b/examples/demo_dlmbl/python/solution.py
@@ -179,32 +179,9 @@
 
 """
 
-# %%
-
-BATCH_SIZE = 42  # 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.
-# More seriously, batch size does not have to be a power of 2.
-# See: https://sebastianraschka.com/blog/2022/batch-size-2.html
-
-data_module = HCSDataModule(
-    data_path,
-    source_channel="Phase",
-    target_channel=["Nuclei", "Membrane"],
-    z_window_size=1,
-    split_ratio=0.8,
-    batch_size=BATCH_SIZE,
-    num_workers=8,
-    architecture="2D",
-    yx_patch_size=(512, 512),  # larger patch size makes it easy to see augmentations.
-    augment=False,  # Turn off augmentation for now.
-)
-data_module.setup("fit")
-
-print(
-    f"FOVs in training set: {len(data_module.train_dataset)}, FOVs in validation set:{len(data_module.val_dataset)}"
-)
-
+# %% 
+# Define a function to write a batch to tensorboard log.
 
-# %% Define a function to write a batch to tensorboard log.
 def log_batch_tensorboard(batch, batchno, writer, card_name):
     """
     Logs a batch of images to TensorBoard.
@@ -246,22 +223,57 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     writer.add_image(card_name, grid, batchno)
 
 
-# %% Log a batch and an epoch to tensorboard.
+# %%
+
+# Initialize the data module.
 
-writer = SummaryWriter(log_dir=f"{log_dir}/view_batch")
+BATCH_SIZE = 42 
+# 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.
+# More seriously, batch size does not have to be a power of 2.
+# See: https://sebastianraschka.com/blog/2022/batch-size-2.html
+
+data_module = HCSDataModule(
+    data_path,
+    source_channel="Phase",
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=1,
+    split_ratio=0.8,
+    batch_size=BATCH_SIZE,
+    num_workers=8,
+    architecture="2D",
+    yx_patch_size=(512, 512),  # larger patch size makes it easy to see augmentations.
+    augment=False,  # Turn off augmentation for now.
+)
+data_module.setup("fit")
+
+print(
+    f"FOVs in training set: {len(data_module.train_dataset)}, FOVs in validation set:{len(data_module.val_dataset)}"
+)
 train_dataloader = data_module.train_dataloader()
 
+# Instantiate the tensorboard SummaryWriter, logs the first batch and then iterates through all the batches and logs them to tensorboard.
+
+writer = SummaryWriter(log_dir=f"{log_dir}/view_batch")
 # Draw a batch and write to tensorboard.
 batch = next(iter(train_dataloader))
 log_batch_tensorboard(batch, 0, writer, "augmentation/none")
 
+# Iterate through all the batches and log them to tensorboard.
 for i, batch in enumerate(train_dataloader):
     log_batch_tensorboard(batch, i, writer, "augmentation/none")
 writer.close()
 
-# %% Use the following if you need to bring up the tensorboard session again
-# notebook.list()
-# notebook.display(port=6006, height=800)
+# %% [markdown]
+"""
+There are multiple ways of seeing the tensorboard.
+1. Jupyter lab forwards the tensorboard port to the browser. Go to http://localhost:6006/ to see the tensorboard.
+2. You likely have an open viewer in the first cell where you loaded tensorboard jupyter extension.
+3. If you want to see tensorboard in a specific cell, use the following code.
+```
+notebook.list() # View open TensorBoard instances
+notebook.display(port=6006, height=800) # Display the TensorBoard instance specified by the port.
+```
+"""
 
 # %% [markdown]
 """
@@ -272,18 +284,20 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 Turn on augmentation and view the batch in tensorboard.
 """
 # %%
-# data_module.augment = ...
-# data_module.batch_size = ...
-# ... # Feel free to adjust a few other parameters of data_module.
-# data_module.setup("fit")
-# train_dataloader = data_module.train_dataloader()
-# # Draw batches and write to tensorboard
-# writer = SummaryWriter(log_dir = f"{log_dir}/view_batch")
-# for i, batch in enumerate(train_dataloader):
-#     log_batch_tensorboard(...)
-# writer.close()
+##########################
+######## TODO ########
+##########################
+
+# Write code to turn on augmentations, change batch sizes and log them to tensorboard.
+# See how the training data changes as a function of these parameters.
+# Remember to call `data_module.setup("fit")` after changing the parameters.
+
 
 # %% tags=["solution"]
+##########################
+######## Solution ########
+##########################
+
 data_module.augment = True
 data_module.batch_size = 21
 data_module.split_ratio = 0.8
@@ -303,7 +317,9 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 We setup a fresh data module and instantiate the trainer class.
 """
 
-# %% The entire training loop is contained in this cell.
+# %% 
+
+# The entire training loop is contained in this cell.
 
 GPU_ID = 0
 BATCH_SIZE = 10
@@ -326,7 +342,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     loss_function=torch.nn.functional.l1_loss,
     schedule="WarmupCosine",
-    log_num_samples=10,
+    log_num_samples=10, # Number of samples from each batch to log to tensorboard.
     example_input_yx_shape=YX_PATCH_SIZE,
 )
 
@@ -344,19 +360,13 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     augment=True,
 )
 phase2fluor_data.setup("fit")
-# fast_dev_run runs a single batch of data through the model to check for errors.
-trainer = VSTrainer(
-    accelerator="gpu", devices=[GPU_ID], max_epochs=40, fast_dev_run=True
-)
 
-# trainer class takes the model and the data module as inputs.
-trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
 # %% [markdown]
 """
 <div class="alert alert-info">
 Task 1.4
-Setup the training for ~50 epochs
+Setup the training for ~30 epochs
 </div>
 
 Tips:
@@ -364,7 +374,19 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 in a specific directory.
 """
 
-# %% tags=["solution"]
+# %% Setup trainer and check for errors.
+
+# fast_dev_run runs a single batch of data through the model to check for errors.
+trainer = VSTrainer(
+    accelerator="gpu", devices=[GPU_ID], fast_dev_run=True
+)
+
+# trainer class takes the model and the data module as inputs.
+trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
+
+
+
+# %% 
 
 GPU_ID = 0
 n_samples = len(phase2fluor_data.train_dataset)
@@ -377,8 +399,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     max_epochs=n_epochs,
     log_every_n_steps=steps_per_epoch // 2, 
     # log losses and image samples 2 times per epoch.
-    default_root_dir=Path(log_dir, "phase2fluor"),
-    
+    default_root_dir=Path(log_dir, "phase2fluor"), # lightning trainer transparently saves logs and model checkpoints in this directory.
 )
 
 # Log graph
@@ -411,17 +432,17 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # %%
 
-# visualize graph.
-model_graph = torchview.draw_graph(
+# PyTorch uses dynamic graphs under the hood. The graphs are constructed on the fly. This is in contrast to TensorFlow, where the graph is constructed before the training loop and remains static. In other words, the graph of the network can change with every forward pass. Therefore, we need to supply an input tensor to construct the graph. The input tensor can be a random tensor of the correct shape and type. We can also supply a real image from the dataset. The latter is more useful for debugging.
+
+# visualize graph. 
+model_graph_phase2fluor = torchview.draw_graph(
     phase2fluor_model,
     phase2fluor_data.train_dataset[0]["source"],
-    depth=2,
+    depth=2, # adjust depth to zoom in.
     device="cpu",
 )
 # Increase the depth to zoom in.
-
-graph = model_graph.visual_graph
-graph
+model_graph_phase2fluor.visual_graph
 
 # %% tags = ["solution"]
 fluor2phase_data = HCSDataModule(
@@ -472,6 +493,63 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 trainer.logger.log_graph(fluor2phase_model, fluor2phase_data.train_dataset[0]["source"])
 trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)
 
+# %% 
+# Visualize the graph of fluor2phase model. 
+model_graph_fluor2phase = torchview.draw_graph(
+    phase2fluor_model,
+    phase2fluor_data.train_dataset[0]["source"],
+    depth=2, # adjust depth to zoom in.
+    device="cpu",
+)
+model_graph_fluor2phase.visual_graph
+
+# %% [markdown]
+"""
+We now look at some metrics of performance. Loss is a differentiable metric. But, several non-differentiable metrics are useful to assess the performance of the model. We typically evaluate the model performance on a held out test data. We will use the following metrics to evaluate the accuracy of regression of the model:
+- [Coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination): $R^2$ 
+- [Person Correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
+- [Structural similarity](https://en.wikipedia.org/wiki/Structural_similarity) (SSIM):
+
+"""
+# %%
+
+# TODO: set following parameters, specifically path to checkpoint, and log the metrics.
+test_data_path = Path("~/data/04_image_translation/HEK_nuclei_membrane_test.zarr").expanduser()
+model_version = "phase2fluor"
+save_dir = Path(log_dir, "test")
+ckpt_path = Path(r"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt") #prefix the string with 'r' to avoid the need for escape characters.
+### END TODO
+
+test_data = HCSDataModule(
+    test_data_path,
+    source_channel="Phase",
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=1,
+    batch_size=1,
+    num_workers=8,
+    architecture="2D",
+)
+test_data.setup("test")
+trainer = VSTrainer(
+    accelerator="gpu",
+    devices=[GPU_ID],
+    logger=CSVLogger(save_dir=save_dir, version=model_version),
+)
+trainer.test(
+    phase2fluor_model,
+    datamodule=test_data,
+    ckpt_path=ckpt_path,
+)
+# read metrics and plot
+metrics = pd.read_csv(Path(save_dir, "lightning_logs", model_version, "metrics.csv"))
+metrics.boxplot(
+    column=[
+        "test_metrics/r2_step",
+        "test_metrics/pearson_step",
+        "test_metrics/SSIM_step",
+    ],
+    rot=30,
+)
 # %% [markdown]
 """
 <div class="alert alert-success">
@@ -489,16 +567,32 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # %% [markdown] <a id='3_tuning'></a>
 """
-# Part 3: Tune the capacity of networks and other hyperparameters to improve the performance.
+# Part 3: Run computational experiments to tune the capacity of models.
 --------------------------------------------------
 
 Learning goals:
 
-- Tweak model hyperparameters, primarily width and learning rate.
-- Adjust batch size to fully utilize the VRAM
+- Tweak model hyperparameters, such as number of filters at each depth.
+- Adjust learning rate to improve performance.
 """
 
+# %%
+# %%
+##########################
+######## TODO ########
+##########################
+
+# Choose a model you want to train (phase2fluor or fluor2phase).
+# Create a config to double the number of filters at each stage.
+# Use training loop illustrated in previous cells to train phase2fluor and fluor2phase models to prototype your own training loop.
+
+
 # %% tags = ["solution"]
+
+##########################
+######## Solution ########
+##########################
+
 phase2fluor_wider_config = {
     "architecture": "2D",
     # double the number of filters at each stage
@@ -518,11 +612,26 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     log_num_samples=10,
     example_input_yx_shape=YX_PATCH_SIZE,
 )
+ 
 
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs=n_epochs, log_every_n_steps=steps_per_epoch, default_root_dir=Path(log_dir, "phase2fluor"), fast_dev_run=True) # Set fast_dev_run to False to train the model.
 trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
 
+# %%
+##########################
+######## TODO ########
+##########################
+
+# Choose a model you want to train (phase2fluor or fluor2phase).
+# Train it with lower learning rate to see how the performance changes.
+
+
 # %% tags = ["solution"]
+
+##########################
+######## Solution ########
+##########################
+
 phase2fluor_slow_model = VSUNet(
     model_config=phase2fluor_config.copy(),
     batch_size=BATCH_SIZE,
@@ -534,46 +643,9 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     example_input_yx_shape=YX_PATCH_SIZE,
 )
 
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs=n_epochs, log_every_n_steps=steps_per_epoch, default_root_dir=Path(log_dir, "phase2fluor"), fast_dev_run=True)
 trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)
 
-# %% tags = ["solution"]
-# name of the model variant
-model_version = "phase2fluor"
-save_dir = Path(log_dir, "test")
-test_data_path = Path("~/data/04_image_translation/HEK_nuclei_membrane_test.zarr").expanduser()
-# FIXME: path to checkpoint
-ckpt_path = Path(r"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt")
-test_data = HCSDataModule(
-    test_data_path,
-    source_channel="Phase",
-    target_channel=["Nuclei", "Membrane"],
-    z_window_size=1,
-    batch_size=1,
-    num_workers=8,
-    architecture="2D",
-)
-test_data.setup("test")
-trainer = VSTrainer(
-    accelerator="gpu",
-    devices=[GPU_ID],
-    logger=CSVLogger(save_dir=save_dir, version=model_version),
-)
-trainer.test(
-    phase2fluor_model,
-    datamodule=test_data,
-    ckpt_path=ckpt_path,
-)
-# read metrics and plot
-metrics = pd.read_csv(Path(save_dir, "lightning_logs", model_version, "metrics.csv"))
-axes = metrics.boxplot(
-    column=[
-        "test_metrics/r2_step",
-        "test_metrics/pearson_step",
-        "test_metrics/SSIM_step",
-    ],
-    rot=30,
-)
 
 # %% [markdown]
 """
@@ -581,6 +653,6 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 Checkpoint 3
 
 Congratulations! You have trained several image translation models now!
-Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)
+Please document hyperparameters, snapshots of predictioons on validation set, and loss curves for your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)
 </div>
 """

From 7ba15ba651f88beca45177cc6a445232d5b4aa0f Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Mon, 21 Aug 2023 12:19:19 +0000
Subject: [PATCH 24/29] moved files

---
 examples/demo_dlmbl/{python => }/convert-solution.py | 0
 examples/demo_dlmbl/{python => }/exercise.ipynb      | 0
 examples/demo_dlmbl/{python => }/prepare-exercise.sh | 0
 examples/demo_dlmbl/{python => }/setup.sh            | 0
 examples/demo_dlmbl/{python => }/solution.ipynb      | 0
 examples/demo_dlmbl/{python => }/solution.py         | 0
 6 files changed, 0 insertions(+), 0 deletions(-)
 rename examples/demo_dlmbl/{python => }/convert-solution.py (100%)
 rename examples/demo_dlmbl/{python => }/exercise.ipynb (100%)
 rename examples/demo_dlmbl/{python => }/prepare-exercise.sh (100%)
 rename examples/demo_dlmbl/{python => }/setup.sh (100%)
 rename examples/demo_dlmbl/{python => }/solution.ipynb (100%)
 rename examples/demo_dlmbl/{python => }/solution.py (100%)

diff --git a/examples/demo_dlmbl/python/convert-solution.py b/examples/demo_dlmbl/convert-solution.py
similarity index 100%
rename from examples/demo_dlmbl/python/convert-solution.py
rename to examples/demo_dlmbl/convert-solution.py
diff --git a/examples/demo_dlmbl/python/exercise.ipynb b/examples/demo_dlmbl/exercise.ipynb
similarity index 100%
rename from examples/demo_dlmbl/python/exercise.ipynb
rename to examples/demo_dlmbl/exercise.ipynb
diff --git a/examples/demo_dlmbl/python/prepare-exercise.sh b/examples/demo_dlmbl/prepare-exercise.sh
similarity index 100%
rename from examples/demo_dlmbl/python/prepare-exercise.sh
rename to examples/demo_dlmbl/prepare-exercise.sh
diff --git a/examples/demo_dlmbl/python/setup.sh b/examples/demo_dlmbl/setup.sh
similarity index 100%
rename from examples/demo_dlmbl/python/setup.sh
rename to examples/demo_dlmbl/setup.sh
diff --git a/examples/demo_dlmbl/python/solution.ipynb b/examples/demo_dlmbl/solution.ipynb
similarity index 100%
rename from examples/demo_dlmbl/python/solution.ipynb
rename to examples/demo_dlmbl/solution.ipynb
diff --git a/examples/demo_dlmbl/python/solution.py b/examples/demo_dlmbl/solution.py
similarity index 100%
rename from examples/demo_dlmbl/python/solution.py
rename to examples/demo_dlmbl/solution.py

From f028b7efc87ce188aefe76183e84c2763cb537f2 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Mon, 21 Aug 2023 12:42:23 +0000
Subject: [PATCH 25/29] fixed formatting - ready for review

---
 examples/demo_dlmbl/exercise.ipynb      | 631 +++++++++++++-----
 examples/demo_dlmbl/prepare-exercise.sh |   2 +-
 examples/demo_dlmbl/setup.sh            |   3 +-
 examples/demo_dlmbl/solution.ipynb      | 810 ++++++++++++++++++------
 examples/demo_dlmbl/solution.py         |  86 ++-
 5 files changed, 1139 insertions(+), 393 deletions(-)

diff --git a/examples/demo_dlmbl/exercise.ipynb b/examples/demo_dlmbl/exercise.ipynb
index fd9be2b8..b6a3a256 100644
--- a/examples/demo_dlmbl/exercise.ipynb
+++ b/examples/demo_dlmbl/exercise.ipynb
@@ -2,24 +2,16 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "601f44e8",
-   "metadata": {
-    "cell_marker": "\"\"\"",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "# Image translation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13ac88b9",
+   "id": "c9438eb5",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
+    "# Image translation\n",
+    "---\n",
+    "\n",
     "Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.\n",
-    "-----------------\n",
+    "---\n",
     "\n",
     "In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. \n",
     "\n",
@@ -31,28 +23,37 @@
     "[Guo et al. (2020) Revealing architectural order with quantitative label-free imaging and deep learning\n",
     ". eLife](https://elifesciences.org/articles/55502).\n",
     "\n",
-    "VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive."
+    "VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipeline, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "04678353",
+   "id": "b36463af",
    "metadata": {
     "cell_marker": "\"\"\"",
     "lines_to_next_cell": 0
    },
    "source": [
     "Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.\n",
+    "![HEK](https://github.com/mehta-lab/VisCy/blob/dlmbl2023/docs/figures/phase_to_nuclei_membrane.svg?raw=true)\n",
     "\n",
-    "![virtual_staining](docs/figures/phase_to_nuclei_membrane.png)\n",
+    "<div class=\"alert alert-info\">\n",
+    "The exercise is organized in 3 parts.\n",
     "\n",
-    "The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:\n",
-    "* [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.\n",
+    "* **Part 1** - Explore the data using tensorboard. Launch the training before lunch.\n",
     "* Lunch break - The model will continue training during lunch.\n",
-    "* [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.\n",
-    "* [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.\n",
+    "* **Part 2** - Evaluate the training with tensorboard. Train another model.\n",
+    "* **Part 3** - Tune the models to improve performance.\n",
+    "</div>\n",
+    "\n",
+    "📖 As you work through parts 2 and 3, please share the layouts of the models you train and their performance with everyone via [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z) 📖.\n",
+    "\n",
+    "\n",
+    "Our guesstimate is that each of the three parts will take ~1.5 hours, but don't rush parts 1 and 2 if you need more time with them.\n",
+    "We will discuss your observations on google doc after checkpoints 2 and 3. The exercise is focused on understanding information contained in data, process of training and evaluating image translation models, and parameter exploration.\n",
+    "There are a few coding tasks sprinkled in.\n",
+    "\n",
     "\n",
-    "\"\n",
     "Before you start,\n",
     "\n",
     "<div class=\"alert alert-danger\">\n",
@@ -62,62 +63,92 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbce6388",
+   "id": "ff42876f",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "cell_marker": "\"\"\"",
+    "incorrectly_encoded_metadata": "id='1_phase2fluor'></a>",
+    "title": "<a"
    },
    "source": [
-    "(Part-1:tensorboard)=# Part 1: Visualize data and model using tensorboard, start training a model.\n",
+    "# Part 1: Log training data to tensorboard, start training a model.\n",
+    "---------\n",
     "\n",
     "Learning goals:\n",
     "\n",
-    "- Load the and visualize the images from OME-Zarr\n",
-    "- Configure the data loader\n",
+    "- Load the OME-zarr dataset and examine the channels.\n",
+    "- Configure and understand the data loader.\n",
+    "- Log some patches to tensorboard.\n",
     "- Initialize a 2D U-Net model for virtual staining\n",
-    "- Log the images and the model to tensorboard.\n",
     "- Start training the model to predict nuclei and membrane from phase."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6c65d3b6",
-   "metadata": {},
+   "id": "dd604022",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "title": "Imports and paths"
+   },
    "outputs": [],
    "source": [
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
     "import torch\n",
+    "import torchview\n",
+    "import torchvision\n",
     "from iohub import open_ome_zarr\n",
-    "from tensorboard import notebook\n",
-    "from torchview import draw_graph\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "import numpy as np\n",
+    "from lightning.pytorch import seed_everything\n",
+    "from lightning.pytorch.loggers import CSVLogger\n",
     "\n",
+    "# pytorch lightning wrapper for Tensorboard.\n",
+    "from tensorboard import notebook  # for viewing tensorboard in notebook\n",
+    "from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard\n",
     "\n",
+    "# HCSDataModule makes it easy to load data during training.\n",
     "from viscy.light.data import HCSDataModule\n",
+    "\n",
+    "# Trainer class and UNet.\n",
     "from viscy.light.engine import VSTrainer, VSUNet\n",
     "\n",
-    "BATCH_SIZE = 32\n",
-    "GPU_ID = 0"
+    "seed_everything(42, workers=True)\n",
+    "\n",
+    "# Paths to data and log directory\n",
+    "data_path = Path(\n",
+    "    Path(\"~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/\")\n",
+    ").expanduser()\n",
+    "\n",
+    "log_dir = Path(\"~/data/04_image_translation/logs/\").expanduser()\n",
+    "\n",
+    "# Create log directory if needed, and launch tensorboard\n",
+    "log_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# fmt: off\n",
+    "%reload_ext tensorboard\n",
+    "%tensorboard --logdir {log_dir}\n",
+    "# fmt: on"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "214d4782",
+   "id": "d5e5c411",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Load Dataset.\n",
+    "## Load Dataset.\n",
     "\n",
     "<div class=\"alert alert-info\">\n",
     "Task 1.1\n",
-    "\n",
     "Use <a href=https://czbiohub-sf.github.io/iohub/main/api/ngff.html#open-ome-zarr>\n",
-    "<code>iohub.open_ome_zarr</code></a> to read the dataset.\n",
+    "<code>iohub.open_ome_zarr</code></a> to read the dataset and explore several FOVs with matplotlib.\n",
+    "</div>\n",
     "\n",
-    "There should be 301 FOVs in the dataset (9.3 GB compressed).\n",
+    "There should be 301 FOVs in the dataset (12 GB compressed).\n",
     "\n",
     "Each FOV consists of 3 channels of 2048x2048 images,\n",
     "saved in the <a href=\"https://ngff.openmicroscopy.org/latest/#hcs-layout\">\n",
@@ -125,55 +156,27 @@
     "specified by the Open Microscopy Environment Next Generation File Format\n",
     "(OME-NGFF).\n",
     "\n",
-    "Run <code>open_ome_zarr?</code> in a cell to see the docstring."
+    "The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.\n",
+    "Notice that labelling of nuclei channel is not complete - some cells are not expressing the fluorescent protein."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6eaa0572",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "id": "e0241037",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# set dataset path here\n",
-    "data_path = Path(\n",
-    "    \"~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/\"\n",
-    ").expanduser()\n",
     "\n",
     "dataset = open_ome_zarr(data_path)\n",
     "\n",
-    "print(len(list(dataset.positions())))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6cdae27",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "View images with matplotlib.\n",
-    "\n",
-    "The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.\n",
-    "\n",
-    "\n",
-    "Note that labelling is not perfect,\n",
-    "as some cells are not expressing the fluorophore."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27f84a9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "print(f\"Number of positions: {len(list(dataset.positions()))}\")\n",
     "\n",
+    "# Use the field and pyramid_level below to visualize data.\n",
     "row = \"0\"\n",
     "col = \"0\"\n",
-    "field = \"42\"\n",
+    "field = \"23\"\n",
+    "\n",
     "# This dataset contains images at 3 resolutions.\n",
     "# '0' is the highest resolution\n",
     "# '1' is down-scaled 2x2,\n",
@@ -181,7 +184,7 @@
     "# Such datasets are called image pyramids.\n",
     "pyaramid_level = \"0\"\n",
     "\n",
-    "# `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.\n",
+    "# `channel_names` is the metadata that is stored with data according to the OME-NGFF spec.\n",
     "n_channels = len(dataset.channel_names)\n",
     "\n",
     "image = dataset[f\"{row}/{col}/{field}/{pyaramid_level}\"].numpy()\n",
@@ -193,8 +196,8 @@
     "    for i in range(n_channels):\n",
     "        channel_image = image[0, i, 0]\n",
     "        # Adjust contrast to 0.5th and 99.5th percentile of pixel values.\n",
-    "        p1, p99 = np.percentile(channel_image, (0.5, 99.5))\n",
-    "        channel_image = np.clip(channel_image, p1, p99)\n",
+    "        p_low, p_high = np.percentile(channel_image, (0.5, 99.5))\n",
+    "        channel_image = np.clip(channel_image, p_low, p_high)\n",
     "        axes[i].imshow(channel_image, cmap=\"gray\")\n",
     "        axes[i].axis(\"off\")\n",
     "        axes[i].set_title(dataset.channel_names[i])\n",
@@ -203,21 +206,92 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c20a1ef5",
+   "id": "89e676bf",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Configure the data loaders for training and validation."
+    "## Initialize data loaders and see the samples in tensorboard.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.2\n",
+    "Setup the data loader and log several batches to tensorboard.\n",
+    "</div>`\n",
+    "\n",
+    "VisCy builds on top of PyTorch Lightning. PyTorch Lightning is a thin wrapper around PyTorch that allows rapid experimentation. It provides a [DataModule](https://lightning.ai/docs/pytorch/stable/data/datamodule.html) to handle loading and processing of data during training. VisCy provides a child class, `HCSDataModule` to make it intuitve to access data stored in the HCS layout.\n",
+    "  \n",
+    "The dataloader in `HCSDataModule` returns a batch of samples. A `batch` is a list of dictionaries. The length of the list is equal to the batch size. Each dictionary consists of following key-value pairs.\n",
+    "- `source`: the input image, a tensor of size 1*1*Y*X\n",
+    "- `target`: the target image, a tensor of size 2*1*Y*X\n",
+    "- `index` : the tuple of (location of field in HCS layout, time, and z-slice) of the sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05ce4dbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a function to write a batch to tensorboard log.\n",
+    "\n",
+    "\n",
+    "def log_batch_tensorboard(batch, batchno, writer, card_name):\n",
+    "    \"\"\"\n",
+    "    Logs a batch of images to TensorBoard.\n",
+    "\n",
+    "    Args:\n",
+    "        batch (dict): A dictionary containing the batch of images to be logged.\n",
+    "        writer (SummaryWriter): A TensorBoard SummaryWriter object.\n",
+    "        card_name (str): The name of the card to be displayed in TensorBoard.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    batch_phase = batch[\"source\"][:, :, 0, :, :]  # batch_size x z_size x Y x X tensor.\n",
+    "    batch_membrane = batch[\"target\"][:, 1, 0, :, :].unsqueeze(\n",
+    "        1\n",
+    "    )  # batch_size x 1 x Y x X tensor.\n",
+    "    batch_nuclei = batch[\"target\"][:, 0, 0, :, :].unsqueeze(\n",
+    "        1\n",
+    "    )  # batch_size x 1 x Y x X tensor.\n",
+    "\n",
+    "    p1, p99 = np.percentile(batch_membrane, (0.1, 99.9))\n",
+    "    batch_membrane = np.clip((batch_membrane - p1) / (p99 - p1), 0, 1)\n",
+    "\n",
+    "    p1, p99 = np.percentile(batch_nuclei, (0.1, 99.9))\n",
+    "    batch_nuclei = np.clip((batch_nuclei - p1) / (p99 - p1), 0, 1)\n",
+    "\n",
+    "    p1, p99 = np.percentile(batch_phase, (0.1, 99.9))\n",
+    "    batch_phase = np.clip((batch_phase - p1) / (p99 - p1), 0, 1)\n",
+    "\n",
+    "    [N, C, H, W] = batch_phase.shape\n",
+    "    interleaved_images = torch.zeros((3 * N, C, H, W), dtype=batch_phase.dtype)\n",
+    "    interleaved_images[0::3, :] = batch_phase\n",
+    "    interleaved_images[1::3, :] = batch_nuclei\n",
+    "    interleaved_images[2::3, :] = batch_membrane\n",
+    "\n",
+    "    grid = torchvision.utils.make_grid(interleaved_images, nrow=3)\n",
+    "\n",
+    "    # add the grid to tensorboard\n",
+    "    writer.add_image(card_name, grid, batchno)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dbffa9bd",
+   "id": "f627e8e8",
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
+    "# Initialize the data module.\n",
+    "\n",
+    "BATCH_SIZE = 42\n",
+    "# 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.\n",
+    "# More seriously, batch size does not have to be a power of 2.\n",
+    "# See: https://sebastianraschka.com/blog/2022/batch-size-2.html\n",
+    "\n",
     "data_module = HCSDataModule(\n",
     "    data_path,\n",
     "    source_channel=\"Phase\",\n",
@@ -227,207 +301,446 @@
     "    batch_size=BATCH_SIZE,\n",
     "    num_workers=8,\n",
     "    architecture=\"2D\",\n",
-    "    yx_patch_size=(256, 256),\n",
+    "    yx_patch_size=(512, 512),  # larger patch size makes it easy to see augmentations.\n",
+    "    augment=False,  # Turn off augmentation for now.\n",
     ")\n",
-    "\n",
     "data_module.setup(\"fit\")\n",
     "\n",
-    "print(len(data_module.train_dataset), len(data_module.val_dataset))"
+    "print(\n",
+    "    f\"FOVs in training set: {len(data_module.train_dataset)}, FOVs in validation set:{len(data_module.val_dataset)}\"\n",
+    ")\n",
+    "train_dataloader = data_module.train_dataloader()\n",
+    "\n",
+    "# Instantiate the tensorboard SummaryWriter, logs the first batch and then iterates through all the batches and logs them to tensorboard.\n",
+    "\n",
+    "writer = SummaryWriter(log_dir=f\"{log_dir}/view_batch\")\n",
+    "# Draw a batch and write to tensorboard.\n",
+    "batch = next(iter(train_dataloader))\n",
+    "log_batch_tensorboard(batch, 0, writer, \"augmentation/none\")\n",
+    "\n",
+    "# Iterate through all the batches and log them to tensorboard.\n",
+    "for i, batch in enumerate(train_dataloader):\n",
+    "    log_batch_tensorboard(batch, i, writer, \"augmentation/none\")\n",
+    "writer.close()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "538378a7",
+   "id": "f6ffc7d1",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "<div class=\"alert alert-info\">\n",
-    "Task 1.2\n",
+    "There are multiple ways of seeing the tensorboard.\n",
+    "1. Jupyter lab forwards the tensorboard port to the browser. Go to http://localhost:6006/ to see the tensorboard.\n",
+    "2. You likely have an open viewer in the first cell where you loaded tensorboard jupyter extension.\n",
+    "3. If you want to see tensorboard in a specific cell, use the following code.\n",
+    "```\n",
+    "notebook.list() # View open TensorBoard instances\n",
+    "notebook.display(port=6006, height=800) # Display the TensorBoard instance specified by the port.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed1448ff",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "## View augmentations using tensorboard.\n",
     "\n",
-    "Validate that the data can be loaded in batches correctly.\n",
-    "</div>"
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.3\n",
+    "Turn on augmentation and view the batch in tensorboard."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57b591e7",
-   "metadata": {},
+   "id": "245ae4dd",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "train_dataloader = data_module.train_dataloader()\n",
+    "##########################\n",
+    "######## TODO ########\n",
+    "##########################\n",
     "\n",
-    "for i, batch in enumerate(train_dataloader):\n",
-    "    ...\n",
-    "    # plot one image from each of the batch and break\n",
-    "    break"
+    "# Write code to turn on augmentations, change batch sizes and log them to tensorboard.\n",
+    "# See how the training data changes as a function of these parameters.\n",
+    "# Remember to call `data_module.setup(\"fit\")` after changing the parameters."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1492bfd",
+   "id": "85743be9",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Construct a 2D U-Net for image translation.\n",
-    "\n",
+    "##  Construct a 2D U-Net for image translation.\n",
     "See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.\n",
-    "Increase the ``depth`` in ``draw_graph`` to zoom in."
+    "We setup a fresh data module and instantiate the trainer class."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "415f0a91",
-   "metadata": {},
+   "id": "44fddf02",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "model_config = {\n",
+    "\n",
+    "# The entire training loop is contained in this cell.\n",
+    "\n",
+    "GPU_ID = 0\n",
+    "BATCH_SIZE = 10\n",
+    "YX_PATCH_SIZE = (512, 512)\n",
+    "\n",
+    "\n",
+    "# Dictionary that specifies key parameters of the model.\n",
+    "phase2fluor_config = {\n",
     "    \"architecture\": \"2D\",\n",
+    "    \"num_filters\": [24, 48, 96, 192, 384],\n",
     "    \"in_channels\": 1,\n",
     "    \"out_channels\": 2,\n",
     "    \"residual\": True,\n",
-    "    \"dropout\": 0.1,\n",
-    "    \"task\": \"reg\",\n",
+    "    \"dropout\": 0.1,  # dropout randomly turns off weights to avoid overfitting of the model to data.\n",
+    "    \"task\": \"reg\",  # reg = regression task.\n",
     "}\n",
     "\n",
-    "model = VSUNet(\n",
-    "    model_config=model_config.copy(),\n",
+    "phase2fluor_model = VSUNet(\n",
+    "    model_config=phase2fluor_config.copy(),\n",
     "    batch_size=BATCH_SIZE,\n",
-    "    loss_function=torch.nn.functional.mse_loss,\n",
+    "    loss_function=torch.nn.functional.l1_loss,\n",
     "    schedule=\"WarmupCosine\",\n",
-    "    log_num_samples=10,\n",
+    "    log_num_samples=10,  # Number of samples from each batch to log to tensorboard.\n",
+    "    example_input_yx_shape=YX_PATCH_SIZE,\n",
     ")\n",
     "\n",
-    "# visualize graph\n",
-    "model_graph = draw_graph(model, model.example_input_array, depth=2, device=\"cpu\")\n",
-    "graph = model_graph.visual_graph\n",
-    "graph"
+    "# Reinitialize the data module.\n",
+    "phase2fluor_data = HCSDataModule(\n",
+    "    data_path,\n",
+    "    source_channel=\"Phase\",\n",
+    "    target_channel=[\"Nuclei\", \"Membrane\"],\n",
+    "    z_window_size=1,\n",
+    "    split_ratio=0.8,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    "    yx_patch_size=YX_PATCH_SIZE,\n",
+    "    augment=True,\n",
+    ")\n",
+    "phase2fluor_data.setup(\"fit\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2d4cf103",
+   "id": "292cb22a",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Configure trainer class.\n",
-    "Here we use the ``fast_dev_run`` flag to run a sanity check first."
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.4\n",
+    "Setup the training for ~30 epochs\n",
+    "</div>\n",
+    "\n",
+    "Tips:\n",
+    "- Set ``default_root_dir`` to store the logs and checkpoints\n",
+    "in a specific directory."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb3f0848",
-   "metadata": {},
+   "id": "b9cf215b",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "title": "Setup trainer and check for errors."
+   },
    "outputs": [],
    "source": [
+    "\n",
+    "# fast_dev_run runs a single batch of data through the model to check for errors.\n",
     "trainer = VSTrainer(accelerator=\"gpu\", devices=[GPU_ID], fast_dev_run=True)\n",
     "\n",
-    "trainer.fit(model, datamodule=data_module)"
+    "# trainer class takes the model and the data module as inputs.\n",
+    "trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "9fc0dc30",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f11d604",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "lines_to_next_cell": 2
    },
+   "outputs": [],
    "source": [
-    "<div class=\"alert alert-info\">\n",
-    "Task 1.3\n",
     "\n",
-    "Modify the trainer to train the model for 20 epochs.\n",
-    "</div>"
+    "GPU_ID = 0\n",
+    "n_samples = len(phase2fluor_data.train_dataset)\n",
+    "steps_per_epoch = n_samples // BATCH_SIZE  # steps per epoch.\n",
+    "n_epochs = 30\n",
+    "\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    devices=[GPU_ID],\n",
+    "    max_epochs=n_epochs,\n",
+    "    log_every_n_steps=steps_per_epoch // 2,\n",
+    "    # log losses and image samples 2 times per epoch.\n",
+    "    default_root_dir=Path(\n",
+    "        log_dir, \"phase2fluor\"\n",
+    "    ),  # lightning trainer transparently saves logs and model checkpoints in this directory.\n",
+    ")\n",
+    "\n",
+    "# Log graph\n",
+    "trainer.logger.log_graph(phase2fluor_model, phase2fluor_data.train_dataset[0][\"source\"])\n",
+    "# Launch training.\n",
+    "trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9cbccde9",
+   "id": "157a989c",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Tips:\n",
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 1\n",
     "\n",
-    "- See ``VSTrainer?`` for all the available parameters.\n",
-    "- Set ``default_root_dir`` to store the logs and checkpoints\n",
-    "in a specific directory."
+    "Now the training has started,\n",
+    "we can come back after a while and evaluate the performance!\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0ccba375",
+   "id": "d68dab93",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "cell_marker": "\"\"\"",
+    "incorrectly_encoded_metadata": "id='1_fluor2phase'></a>",
+    "title": "<a"
    },
    "source": [
-    "Bonus:\n",
+    "# Part 2: Assess previous model, train fluorescence to phase contrast translation model.\n",
+    "--------------------------------------------------\n",
     "\n",
-    "- Tweak model hyperparameters\n",
-    "- Adjust batch size to fully utilize the VRAM"
+    "Learning goals:\n",
+    "- Visualize the previous model and training with tensorboard\n",
+    "- Train fluorescence to phase contrast translation model\n",
+    "- Compare the performance of the two models."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "27a7136e",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcf30df5",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "Launch TensorBoard with:\n",
     "\n",
-    "```\n",
-    "%load_ext tensorboard\n",
-    "%tensorboard --logdir /path/to/lightning_logs\n",
-    "```"
+    "# PyTorch uses dynamic graphs under the hood. The graphs are constructed on the fly. This is in contrast to TensorFlow, where the graph is constructed before the training loop and remains static. In other words, the graph of the network can change with every forward pass. Therefore, we need to supply an input tensor to construct the graph. The input tensor can be a random tensor of the correct shape and type. We can also supply a real image from the dataset. The latter is more useful for debugging.\n",
+    "\n",
+    "# visualize graph.\n",
+    "model_graph_phase2fluor = torchview.draw_graph(\n",
+    "    phase2fluor_model,\n",
+    "    phase2fluor_data.train_dataset[0][\"source\"],\n",
+    "    depth=2,  # adjust depth to zoom in.\n",
+    "    device=\"cpu\",\n",
+    ")\n",
+    "# Increase the depth to zoom in.\n",
+    "model_graph_phase2fluor.visual_graph"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8fe9cbda",
+   "id": "4eb094e7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "notebook.list()"
+    "# Visualize the graph of fluor2phase model.\n",
+    "model_graph_fluor2phase = torchview.draw_graph(\n",
+    "    phase2fluor_model,\n",
+    "    phase2fluor_data.train_dataset[0][\"source\"],\n",
+    "    depth=2,  # adjust depth to zoom in.\n",
+    "    device=\"cpu\",\n",
+    ")\n",
+    "model_graph_fluor2phase.visual_graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "756351fa",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "We now look at some metrics of performance. Loss is a differentiable metric. But, several non-differentiable metrics are useful to assess the performance of the model. We typically evaluate the model performance on a held out test data. We will use the following metrics to evaluate the accuracy of regression of the model:\n",
+    "- [Coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination): $R^2$ \n",
+    "- [Person Correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).\n",
+    "- [Structural similarity](https://en.wikipedia.org/wiki/Structural_similarity) (SSIM):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f3855287",
-   "metadata": {},
+   "id": "46c6b30c",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
    "outputs": [],
    "source": [
-    "notebook.display(port=6006, height=800)"
+    "\n",
+    "# TODO: set following parameters, specifically path to checkpoint, and log the metrics.\n",
+    "test_data_path = Path(\n",
+    "    \"~/data/04_image_translation/HEK_nuclei_membrane_test.zarr\"\n",
+    ").expanduser()\n",
+    "model_version = \"phase2fluor\"\n",
+    "save_dir = Path(log_dir, \"test\")\n",
+    "ckpt_path = Path(\n",
+    "    r\"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt\"\n",
+    ")  # prefix the string with 'r' to avoid the need for escape characters.\n",
+    "### END TODO\n",
+    "\n",
+    "test_data = HCSDataModule(\n",
+    "    test_data_path,\n",
+    "    source_channel=\"Phase\",\n",
+    "    target_channel=[\"Nuclei\", \"Membrane\"],\n",
+    "    z_window_size=1,\n",
+    "    batch_size=1,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    ")\n",
+    "test_data.setup(\"test\")\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    devices=[GPU_ID],\n",
+    "    logger=CSVLogger(save_dir=save_dir, version=model_version),\n",
+    ")\n",
+    "trainer.test(\n",
+    "    phase2fluor_model,\n",
+    "    datamodule=test_data,\n",
+    "    ckpt_path=ckpt_path,\n",
+    ")\n",
+    "# read metrics and plot\n",
+    "metrics = pd.read_csv(Path(save_dir, \"lightning_logs\", model_version, \"metrics.csv\"))\n",
+    "metrics.boxplot(\n",
+    "    column=[\n",
+    "        \"test_metrics/r2_step\",\n",
+    "        \"test_metrics/pearson_step\",\n",
+    "        \"test_metrics/SSIM_step\",\n",
+    "    ],\n",
+    "    rot=30,\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a3e91f8",
+   "id": "74972942",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
     "<div class=\"alert alert-success\">\n",
-    "Checkpoint 1\n",
+    "Checkpoint 2\n",
+    "Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)\n",
+    "\n",
+    "Now that you have trained two models, let's think about the following questions:\n",
+    "- What is the information content of each channel in the dataset?\n",
+    "- How would you use image translation models?\n",
+    "- What can you try to improve the performance of each model?\n",
+    "\n",
     "\n",
-    "Now the training has started,\n",
-    "we can come back after a while and evaluate the performance!\n",
     "</div>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7d0c4204",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "incorrectly_encoded_metadata": "id='3_tuning'></a>",
+    "title": "<a"
+   },
+   "source": [
+    "# Part 3: Tune the models.\n",
+    "--------------------------------------------------\n",
+    "\n",
+    "Learning goals:\n",
+    "\n",
+    "- Tweak model hyperparameters, such as number of filters at each depth.\n",
+    "- Adjust learning rate to improve performance."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "799048df",
-   "metadata": {},
+   "id": "fd8c7cb1",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "036a0577",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "##########################\n",
+    "######## TODO ########\n",
+    "##########################\n",
+    "\n",
+    "# Choose a model you want to train (phase2fluor or fluor2phase).\n",
+    "# Create a config to double the number of filters at each stage.\n",
+    "# Use training loop illustrated in previous cells to train phase2fluor and fluor2phase models to prototype your own training loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0181cde7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "##########################\n",
+    "######## TODO ########\n",
+    "##########################\n",
+    "\n",
+    "# Choose a model you want to train (phase2fluor or fluor2phase).\n",
+    "# Train it with lower learning rate to see how the performance changes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b735a925",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 3\n",
+    "\n",
+    "Congratulations! You have trained several image translation models now!\n",
+    "Please document hyperparameters, snapshots of predictioons on validation set, and loss curves for your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)\n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/demo_dlmbl/prepare-exercise.sh b/examples/demo_dlmbl/prepare-exercise.sh
index be605ce9..88f2ba1b 100644
--- a/examples/demo_dlmbl/prepare-exercise.sh
+++ b/examples/demo_dlmbl/prepare-exercise.sh
@@ -1,5 +1,5 @@
 # Run black on .py files
-black solution.py
+# black solution.py
 
 # Convert .py to ipynb
 # "cell_metadata_filter": "all" preserve cell tags including our solution tags
diff --git a/examples/demo_dlmbl/setup.sh b/examples/demo_dlmbl/setup.sh
index 27ef9e1f..6e1f1b57 100644
--- a/examples/demo_dlmbl/setup.sh
+++ b/examples/demo_dlmbl/setup.sh
@@ -4,10 +4,11 @@
 mamba create --name 04_image_translation python=3.10
 
 # Install ipykernel in the environment.
-mamba install -y ipykernel --name 04_image_translation
+mamba install -y ipykernel nbformat nbconvert black jupytext --name 04_image_translation
 # Specifying the environment explicitly.
 # mamba activate sometimes doesn't work from within shell scripts.
 
+mamba install -y nbformat --name 04_image_translation 
 
 # Find path to the environment - mamba activate doesn't work from within shell scripts.
 ENV_PATH=$(conda info --envs | grep 04_image_translation | awk '{print $2}')
diff --git a/examples/demo_dlmbl/solution.ipynb b/examples/demo_dlmbl/solution.ipynb
index 508be3e1..c97b8577 100644
--- a/examples/demo_dlmbl/solution.ipynb
+++ b/examples/demo_dlmbl/solution.ipynb
@@ -2,24 +2,16 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "601f44e8",
-   "metadata": {
-    "cell_marker": "\"\"\"",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "# Image translation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13ac88b9",
+   "id": "c9438eb5",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
+    "# Image translation\n",
+    "---\n",
+    "\n",
     "Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.\n",
-    "-----------------\n",
+    "---\n",
     "\n",
     "In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. \n",
     "\n",
@@ -31,28 +23,37 @@
     "[Guo et al. (2020) Revealing architectural order with quantitative label-free imaging and deep learning\n",
     ". eLife](https://elifesciences.org/articles/55502).\n",
     "\n",
-    "VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipelinem, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive."
+    "VisCy exploits recent advances in the data and metadata formats ([OME-zarr](https://www.nature.com/articles/s41592-021-01326-w)) and DL frameworks, [PyTorch Lightning](https://lightning.ai/) and [MONAI](https://monai.io/). Our previous pipeline, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "04678353",
+   "id": "b36463af",
    "metadata": {
     "cell_marker": "\"\"\"",
     "lines_to_next_cell": 0
    },
    "source": [
     "Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.\n",
+    "![HEK](https://github.com/mehta-lab/VisCy/blob/dlmbl2023/docs/figures/phase_to_nuclei_membrane.svg?raw=true)\n",
     "\n",
-    "![virtual_staining](docs/figures/phase_to_nuclei_membrane.png)\n",
+    "<div class=\"alert alert-info\">\n",
+    "The exercise is organized in 3 parts.\n",
     "\n",
-    "The exercise is organized in 3 parts. Each part should take roughly 1.5 hours to work through:\n",
-    "* [Part 1](Part-1:tensorboard) - Explore the data and model using tensorboard. Launch the training before lunch.\n",
+    "* **Part 1** - Explore the data using tensorboard. Launch the training before lunch.\n",
     "* Lunch break - The model will continue training during lunch.\n",
-    "* [Part 2](#Part-2:evaluation) - Evaluate the training using tensorboard logs. Train a model to predict phase from nuclei and membrane.\n",
-    "* [Part 3](#Part-3:exploration) - Examine the quality of predictions and metrics after adjusting the capacity of the network.\n",
+    "* **Part 2** - Evaluate the training with tensorboard. Train another model.\n",
+    "* **Part 3** - Tune the models to improve performance.\n",
+    "</div>\n",
+    "\n",
+    "📖 As you work through parts 2 and 3, please share the layouts of the models you train and their performance with everyone via [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z) 📖.\n",
+    "\n",
+    "\n",
+    "Our guesstimate is that each of the three parts will take ~1.5 hours, but don't rush parts 1 and 2 if you need more time with them.\n",
+    "We will discuss your observations on google doc after checkpoints 2 and 3. The exercise is focused on understanding information contained in data, process of training and evaluating image translation models, and parameter exploration.\n",
+    "There are a few coding tasks sprinkled in.\n",
+    "\n",
     "\n",
-    "\"\n",
     "Before you start,\n",
     "\n",
     "<div class=\"alert alert-danger\">\n",
@@ -62,62 +63,92 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbce6388",
+   "id": "ff42876f",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "cell_marker": "\"\"\"",
+    "incorrectly_encoded_metadata": "id='1_phase2fluor'></a>",
+    "title": "<a"
    },
    "source": [
-    "(Part-1:tensorboard)=# Part 1: Visualize data and model using tensorboard, start training a model.\n",
+    "# Part 1: Log training data to tensorboard, start training a model.\n",
+    "---------\n",
     "\n",
     "Learning goals:\n",
     "\n",
-    "- Load the and visualize the images from OME-Zarr\n",
-    "- Configure the data loader\n",
+    "- Load the OME-zarr dataset and examine the channels.\n",
+    "- Configure and understand the data loader.\n",
+    "- Log some patches to tensorboard.\n",
     "- Initialize a 2D U-Net model for virtual staining\n",
-    "- Log the images and the model to tensorboard.\n",
     "- Start training the model to predict nuclei and membrane from phase."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6c65d3b6",
-   "metadata": {},
+   "id": "dd604022",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "title": "Imports and paths"
+   },
    "outputs": [],
    "source": [
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
     "import torch\n",
+    "import torchview\n",
+    "import torchvision\n",
     "from iohub import open_ome_zarr\n",
-    "from tensorboard import notebook\n",
-    "from torchview import draw_graph\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "import numpy as np\n",
+    "from lightning.pytorch import seed_everything\n",
+    "from lightning.pytorch.loggers import CSVLogger\n",
     "\n",
+    "# pytorch lightning wrapper for Tensorboard.\n",
+    "from tensorboard import notebook  # for viewing tensorboard in notebook\n",
+    "from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard\n",
     "\n",
+    "# HCSDataModule makes it easy to load data during training.\n",
     "from viscy.light.data import HCSDataModule\n",
+    "\n",
+    "# Trainer class and UNet.\n",
     "from viscy.light.engine import VSTrainer, VSUNet\n",
     "\n",
-    "BATCH_SIZE = 32\n",
-    "GPU_ID = 0"
+    "seed_everything(42, workers=True)\n",
+    "\n",
+    "# Paths to data and log directory\n",
+    "data_path = Path(\n",
+    "    Path(\"~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/\")\n",
+    ").expanduser()\n",
+    "\n",
+    "log_dir = Path(\"~/data/04_image_translation/logs/\").expanduser()\n",
+    "\n",
+    "# Create log directory if needed, and launch tensorboard\n",
+    "log_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# fmt: off\n",
+    "%reload_ext tensorboard\n",
+    "%tensorboard --logdir {log_dir}\n",
+    "# fmt: on"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "214d4782",
+   "id": "d5e5c411",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Load Dataset.\n",
+    "## Load Dataset.\n",
     "\n",
     "<div class=\"alert alert-info\">\n",
     "Task 1.1\n",
-    "\n",
     "Use <a href=https://czbiohub-sf.github.io/iohub/main/api/ngff.html#open-ome-zarr>\n",
-    "<code>iohub.open_ome_zarr</code></a> to read the dataset.\n",
+    "<code>iohub.open_ome_zarr</code></a> to read the dataset and explore several FOVs with matplotlib.\n",
+    "</div>\n",
     "\n",
-    "There should be 301 FOVs in the dataset (9.3 GB compressed).\n",
+    "There should be 301 FOVs in the dataset (12 GB compressed).\n",
     "\n",
     "Each FOV consists of 3 channels of 2048x2048 images,\n",
     "saved in the <a href=\"https://ngff.openmicroscopy.org/latest/#hcs-layout\">\n",
@@ -125,55 +156,27 @@
     "specified by the Open Microscopy Environment Next Generation File Format\n",
     "(OME-NGFF).\n",
     "\n",
-    "Run <code>open_ome_zarr?</code> in a cell to see the docstring."
+    "The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.\n",
+    "Notice that labelling of nuclei channel is not complete - some cells are not expressing the fluorescent protein."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6eaa0572",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "id": "e0241037",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# set dataset path here\n",
-    "data_path = Path(\n",
-    "    \"~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/\"\n",
-    ").expanduser()\n",
     "\n",
     "dataset = open_ome_zarr(data_path)\n",
     "\n",
-    "print(len(list(dataset.positions())))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6cdae27",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "View images with matplotlib.\n",
-    "\n",
-    "The layout on the disk is: row/col/field/pyramid_level/timepoint/channel/z/y/x.\n",
-    "\n",
-    "\n",
-    "Note that labelling is not perfect,\n",
-    "as some cells are not expressing the fluorophore."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27f84a9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "print(f\"Number of positions: {len(list(dataset.positions()))}\")\n",
     "\n",
+    "# Use the field and pyramid_level below to visualize data.\n",
     "row = \"0\"\n",
     "col = \"0\"\n",
-    "field = \"42\"\n",
+    "field = \"23\"\n",
+    "\n",
     "# This dataset contains images at 3 resolutions.\n",
     "# '0' is the highest resolution\n",
     "# '1' is down-scaled 2x2,\n",
@@ -181,7 +184,7 @@
     "# Such datasets are called image pyramids.\n",
     "pyaramid_level = \"0\"\n",
     "\n",
-    "# `channel_names` is the metadata that is stored with data accoring to the OME-NGFF spec.\n",
+    "# `channel_names` is the metadata that is stored with data according to the OME-NGFF spec.\n",
     "n_channels = len(dataset.channel_names)\n",
     "\n",
     "image = dataset[f\"{row}/{col}/{field}/{pyaramid_level}\"].numpy()\n",
@@ -193,8 +196,8 @@
     "    for i in range(n_channels):\n",
     "        channel_image = image[0, i, 0]\n",
     "        # Adjust contrast to 0.5th and 99.5th percentile of pixel values.\n",
-    "        p1, p99 = np.percentile(channel_image, (0.5, 99.5))\n",
-    "        channel_image = np.clip(channel_image, p1, p99)\n",
+    "        p_low, p_high = np.percentile(channel_image, (0.5, 99.5))\n",
+    "        channel_image = np.clip(channel_image, p_low, p_high)\n",
     "        axes[i].imshow(channel_image, cmap=\"gray\")\n",
     "        axes[i].axis(\"off\")\n",
     "        axes[i].set_title(dataset.channel_names[i])\n",
@@ -203,21 +206,92 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c20a1ef5",
+   "id": "89e676bf",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Configure the data loaders for training and validation."
+    "## Initialize data loaders and see the samples in tensorboard.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.2\n",
+    "Setup the data loader and log several batches to tensorboard.\n",
+    "</div>`\n",
+    "\n",
+    "VisCy builds on top of PyTorch Lightning. PyTorch Lightning is a thin wrapper around PyTorch that allows rapid experimentation. It provides a [DataModule](https://lightning.ai/docs/pytorch/stable/data/datamodule.html) to handle loading and processing of data during training. VisCy provides a child class, `HCSDataModule` to make it intuitve to access data stored in the HCS layout.\n",
+    "  \n",
+    "The dataloader in `HCSDataModule` returns a batch of samples. A `batch` is a list of dictionaries. The length of the list is equal to the batch size. Each dictionary consists of following key-value pairs.\n",
+    "- `source`: the input image, a tensor of size 1*1*Y*X\n",
+    "- `target`: the target image, a tensor of size 2*1*Y*X\n",
+    "- `index` : the tuple of (location of field in HCS layout, time, and z-slice) of the sample."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dbffa9bd",
+   "id": "05ce4dbb",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Define a function to write a batch to tensorboard log.\n",
+    "\n",
+    "\n",
+    "def log_batch_tensorboard(batch, batchno, writer, card_name):\n",
+    "    \"\"\"\n",
+    "    Logs a batch of images to TensorBoard.\n",
+    "\n",
+    "    Args:\n",
+    "        batch (dict): A dictionary containing the batch of images to be logged.\n",
+    "        writer (SummaryWriter): A TensorBoard SummaryWriter object.\n",
+    "        card_name (str): The name of the card to be displayed in TensorBoard.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    batch_phase = batch[\"source\"][:, :, 0, :, :]  # batch_size x z_size x Y x X tensor.\n",
+    "    batch_membrane = batch[\"target\"][:, 1, 0, :, :].unsqueeze(\n",
+    "        1\n",
+    "    )  # batch_size x 1 x Y x X tensor.\n",
+    "    batch_nuclei = batch[\"target\"][:, 0, 0, :, :].unsqueeze(\n",
+    "        1\n",
+    "    )  # batch_size x 1 x Y x X tensor.\n",
+    "\n",
+    "    p1, p99 = np.percentile(batch_membrane, (0.1, 99.9))\n",
+    "    batch_membrane = np.clip((batch_membrane - p1) / (p99 - p1), 0, 1)\n",
+    "\n",
+    "    p1, p99 = np.percentile(batch_nuclei, (0.1, 99.9))\n",
+    "    batch_nuclei = np.clip((batch_nuclei - p1) / (p99 - p1), 0, 1)\n",
+    "\n",
+    "    p1, p99 = np.percentile(batch_phase, (0.1, 99.9))\n",
+    "    batch_phase = np.clip((batch_phase - p1) / (p99 - p1), 0, 1)\n",
+    "\n",
+    "    [N, C, H, W] = batch_phase.shape\n",
+    "    interleaved_images = torch.zeros((3 * N, C, H, W), dtype=batch_phase.dtype)\n",
+    "    interleaved_images[0::3, :] = batch_phase\n",
+    "    interleaved_images[1::3, :] = batch_nuclei\n",
+    "    interleaved_images[2::3, :] = batch_membrane\n",
+    "\n",
+    "    grid = torchvision.utils.make_grid(interleaved_images, nrow=3)\n",
+    "\n",
+    "    # add the grid to tensorboard\n",
+    "    writer.add_image(card_name, grid, batchno)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f627e8e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Initialize the data module.\n",
+    "\n",
+    "BATCH_SIZE = 42\n",
+    "# 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.\n",
+    "# More seriously, batch size does not have to be a power of 2.\n",
+    "# See: https://sebastianraschka.com/blog/2022/batch-size-2.html\n",
+    "\n",
     "data_module = HCSDataModule(\n",
     "    data_path,\n",
     "    source_channel=\"Phase\",\n",
@@ -227,200 +301,297 @@
     "    batch_size=BATCH_SIZE,\n",
     "    num_workers=8,\n",
     "    architecture=\"2D\",\n",
-    "    yx_patch_size=(256, 256),\n",
+    "    yx_patch_size=(512, 512),  # larger patch size makes it easy to see augmentations.\n",
+    "    augment=False,  # Turn off augmentation for now.\n",
     ")\n",
-    "\n",
     "data_module.setup(\"fit\")\n",
     "\n",
-    "print(len(data_module.train_dataset), len(data_module.val_dataset))"
+    "print(\n",
+    "    f\"FOVs in training set: {len(data_module.train_dataset)}, FOVs in validation set:{len(data_module.val_dataset)}\"\n",
+    ")\n",
+    "train_dataloader = data_module.train_dataloader()\n",
+    "\n",
+    "# Instantiate the tensorboard SummaryWriter, logs the first batch and then iterates through all the batches and logs them to tensorboard.\n",
+    "\n",
+    "writer = SummaryWriter(log_dir=f\"{log_dir}/view_batch\")\n",
+    "# Draw a batch and write to tensorboard.\n",
+    "batch = next(iter(train_dataloader))\n",
+    "log_batch_tensorboard(batch, 0, writer, \"augmentation/none\")\n",
+    "\n",
+    "# Iterate through all the batches and log them to tensorboard.\n",
+    "for i, batch in enumerate(train_dataloader):\n",
+    "    log_batch_tensorboard(batch, i, writer, \"augmentation/none\")\n",
+    "writer.close()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "538378a7",
+   "id": "f6ffc7d1",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "<div class=\"alert alert-info\">\n",
-    "Task 1.2\n",
+    "There are multiple ways of seeing the tensorboard.\n",
+    "1. Jupyter lab forwards the tensorboard port to the browser. Go to http://localhost:6006/ to see the tensorboard.\n",
+    "2. You likely have an open viewer in the first cell where you loaded tensorboard jupyter extension.\n",
+    "3. If you want to see tensorboard in a specific cell, use the following code.\n",
+    "```\n",
+    "notebook.list() # View open TensorBoard instances\n",
+    "notebook.display(port=6006, height=800) # Display the TensorBoard instance specified by the port.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed1448ff",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "## View augmentations using tensorboard.\n",
     "\n",
-    "Validate that the data can be loaded in batches correctly.\n",
-    "</div>"
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.3\n",
+    "Turn on augmentation and view the batch in tensorboard."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57b591e7",
-   "metadata": {},
+   "id": "245ae4dd",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "train_dataloader = data_module.train_dataloader()\n",
+    "##########################\n",
+    "######## TODO ########\n",
+    "##########################\n",
     "\n",
-    "for i, batch in enumerate(train_dataloader):\n",
-    "    ...\n",
-    "    # plot one image from each of the batch and break\n",
-    "    break"
+    "# Write code to turn on augmentations, change batch sizes and log them to tensorboard.\n",
+    "# See how the training data changes as a function of these parameters.\n",
+    "# Remember to call `data_module.setup(\"fit\")` after changing the parameters."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0657c53f",
+   "id": "e14f4faf",
    "metadata": {
-    "lines_to_next_cell": 2,
     "tags": [
      "solution"
     ]
    },
    "outputs": [],
    "source": [
-    "train_dataloader = data_module.train_dataloader()\n",
-    "\n",
+    "##########################\n",
+    "######## Solution ########\n",
+    "##########################\n",
     "\n",
-    "fig, axs = plt.subplots(3, 8, figsize=(20, 8))\n",
-    "\n",
-    "# Draw 8 batches, each with 32 images. Show the first image in each batch.\n",
+    "data_module.augment = True\n",
+    "data_module.batch_size = 21\n",
+    "data_module.split_ratio = 0.8\n",
+    "data_module.setup(\"fit\")\n",
     "\n",
+    "train_dataloader = data_module.train_dataloader()\n",
+    "# Draw batches and write to tensorboard\n",
+    "writer = SummaryWriter(log_dir=f\"{log_dir}/view_batch\")\n",
     "for i, batch in enumerate(train_dataloader):\n",
-    "    # The batch is a dictionary consisting of three keys: 'index', 'source', 'target'.\n",
-    "    # index is the tuple consisting of (image name, time, and z-slice)\n",
-    "    # source is the tensor of size 1x1x256x256\n",
-    "    # target is the tensor of size 2x1x256x256\n",
-    "\n",
-    "    if i >= 8:\n",
-    "        break\n",
-    "    FOV = batch[\"index\"][0][0]\n",
-    "    input_tensor = batch[\"source\"][0, 0, :, :].squeeze()\n",
-    "    target_nuclei_tensor = batch[\"target\"][0, 0, :, :].squeeze()\n",
-    "    target_membrane_tensor = batch[\"target\"][0, 1, :, :].squeeze()\n",
-    "\n",
-    "    axs[0, i].imshow(input_tensor, cmap=\"gray\")\n",
-    "    axs[1, i].imshow(target_nuclei_tensor, cmap=\"gray\")\n",
-    "    axs[2, i].imshow(target_membrane_tensor, cmap=\"gray\")\n",
-    "    axs[0, i].set_title(f\"input@{FOV}\")\n",
-    "    axs[1, i].set_title(\"target-nuclei\")\n",
-    "    axs[2, i].set_title(\"target-membrane\")\n",
-    "    axs[0, i].axis(\"off\")\n",
-    "    axs[1, i].axis(\"off\")\n",
-    "    axs[2, i].axis(\"off\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "    log_batch_tensorboard(batch, i, writer, \"augmentation/some\")\n",
+    "writer.close()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1492bfd",
+   "id": "85743be9",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Construct a 2D U-Net for image translation.\n",
-    "\n",
+    "##  Construct a 2D U-Net for image translation.\n",
     "See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.\n",
-    "Increase the ``depth`` in ``draw_graph`` to zoom in."
+    "We setup a fresh data module and instantiate the trainer class."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "415f0a91",
-   "metadata": {},
+   "id": "44fddf02",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "model_config = {\n",
+    "\n",
+    "# The entire training loop is contained in this cell.\n",
+    "\n",
+    "GPU_ID = 0\n",
+    "BATCH_SIZE = 10\n",
+    "YX_PATCH_SIZE = (512, 512)\n",
+    "\n",
+    "\n",
+    "# Dictionary that specifies key parameters of the model.\n",
+    "phase2fluor_config = {\n",
     "    \"architecture\": \"2D\",\n",
+    "    \"num_filters\": [24, 48, 96, 192, 384],\n",
     "    \"in_channels\": 1,\n",
     "    \"out_channels\": 2,\n",
     "    \"residual\": True,\n",
-    "    \"dropout\": 0.1,\n",
-    "    \"task\": \"reg\",\n",
+    "    \"dropout\": 0.1,  # dropout randomly turns off weights to avoid overfitting of the model to data.\n",
+    "    \"task\": \"reg\",  # reg = regression task.\n",
     "}\n",
     "\n",
-    "model = VSUNet(\n",
-    "    model_config=model_config.copy(),\n",
+    "phase2fluor_model = VSUNet(\n",
+    "    model_config=phase2fluor_config.copy(),\n",
     "    batch_size=BATCH_SIZE,\n",
-    "    loss_function=torch.nn.functional.mse_loss,\n",
+    "    loss_function=torch.nn.functional.l1_loss,\n",
     "    schedule=\"WarmupCosine\",\n",
-    "    log_num_samples=10,\n",
+    "    log_num_samples=10,  # Number of samples from each batch to log to tensorboard.\n",
+    "    example_input_yx_shape=YX_PATCH_SIZE,\n",
     ")\n",
     "\n",
-    "# visualize graph\n",
-    "model_graph = draw_graph(model, model.example_input_array, depth=2, device=\"cpu\")\n",
-    "graph = model_graph.visual_graph\n",
-    "graph"
+    "# Reinitialize the data module.\n",
+    "phase2fluor_data = HCSDataModule(\n",
+    "    data_path,\n",
+    "    source_channel=\"Phase\",\n",
+    "    target_channel=[\"Nuclei\", \"Membrane\"],\n",
+    "    z_window_size=1,\n",
+    "    split_ratio=0.8,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    "    yx_patch_size=YX_PATCH_SIZE,\n",
+    "    augment=True,\n",
+    ")\n",
+    "phase2fluor_data.setup(\"fit\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2d4cf103",
+   "id": "292cb22a",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Configure trainer class.\n",
-    "Here we use the ``fast_dev_run`` flag to run a sanity check first."
+    "<div class=\"alert alert-info\">\n",
+    "Task 1.4\n",
+    "Setup the training for ~30 epochs\n",
+    "</div>\n",
+    "\n",
+    "Tips:\n",
+    "- Set ``default_root_dir`` to store the logs and checkpoints\n",
+    "in a specific directory."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb3f0848",
-   "metadata": {},
+   "id": "b9cf215b",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "title": "Setup trainer and check for errors."
+   },
    "outputs": [],
    "source": [
+    "\n",
+    "# fast_dev_run runs a single batch of data through the model to check for errors.\n",
     "trainer = VSTrainer(accelerator=\"gpu\", devices=[GPU_ID], fast_dev_run=True)\n",
     "\n",
-    "trainer.fit(model, datamodule=data_module)"
+    "# trainer class takes the model and the data module as inputs.\n",
+    "trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "9fc0dc30",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f11d604",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "lines_to_next_cell": 2
    },
+   "outputs": [],
    "source": [
-    "<div class=\"alert alert-info\">\n",
-    "Task 1.3\n",
     "\n",
-    "Modify the trainer to train the model for 20 epochs.\n",
-    "</div>"
+    "GPU_ID = 0\n",
+    "n_samples = len(phase2fluor_data.train_dataset)\n",
+    "steps_per_epoch = n_samples // BATCH_SIZE  # steps per epoch.\n",
+    "n_epochs = 30\n",
+    "\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    devices=[GPU_ID],\n",
+    "    max_epochs=n_epochs,\n",
+    "    log_every_n_steps=steps_per_epoch // 2,\n",
+    "    # log losses and image samples 2 times per epoch.\n",
+    "    default_root_dir=Path(\n",
+    "        log_dir, \"phase2fluor\"\n",
+    "    ),  # lightning trainer transparently saves logs and model checkpoints in this directory.\n",
+    ")\n",
+    "\n",
+    "# Log graph\n",
+    "trainer.logger.log_graph(phase2fluor_model, phase2fluor_data.train_dataset[0][\"source\"])\n",
+    "# Launch training.\n",
+    "trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9cbccde9",
+   "id": "157a989c",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Tips:\n",
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 1\n",
     "\n",
-    "- See ``VSTrainer?`` for all the available parameters.\n",
-    "- Set ``default_root_dir`` to store the logs and checkpoints\n",
-    "in a specific directory."
+    "Now the training has started,\n",
+    "we can come back after a while and evaluate the performance!\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0ccba375",
+   "id": "d68dab93",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "cell_marker": "\"\"\"",
+    "incorrectly_encoded_metadata": "id='1_fluor2phase'></a>",
+    "title": "<a"
    },
    "source": [
-    "Bonus:\n",
+    "# Part 2: Assess previous model, train fluorescence to phase contrast translation model.\n",
+    "--------------------------------------------------\n",
+    "\n",
+    "Learning goals:\n",
+    "- Visualize the previous model and training with tensorboard\n",
+    "- Train fluorescence to phase contrast translation model\n",
+    "- Compare the performance of the two models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcf30df5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
-    "- Tweak model hyperparameters\n",
-    "- Adjust batch size to fully utilize the VRAM"
+    "# PyTorch uses dynamic graphs under the hood. The graphs are constructed on the fly. This is in contrast to TensorFlow, where the graph is constructed before the training loop and remains static. In other words, the graph of the network can change with every forward pass. Therefore, we need to supply an input tensor to construct the graph. The input tensor can be a random tensor of the correct shape and type. We can also supply a real image from the dataset. The latter is more useful for debugging.\n",
+    "\n",
+    "# visualize graph.\n",
+    "model_graph_phase2fluor = torchview.draw_graph(\n",
+    "    phase2fluor_model,\n",
+    "    phase2fluor_data.train_dataset[0][\"source\"],\n",
+    "    depth=2,  # adjust depth to zoom in.\n",
+    "    device=\"cpu\",\n",
+    ")\n",
+    "# Increase the depth to zoom in.\n",
+    "model_graph_phase2fluor.visual_graph"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3eeb3bd0",
+   "id": "5f226b27",
    "metadata": {
     "tags": [
      "solution"
@@ -428,84 +599,323 @@
    },
    "outputs": [],
    "source": [
-    "wider_config = model_config | {\"num_filters\": [24, 48, 96, 192, 384]}\n",
+    "fluor2phase_data = HCSDataModule(\n",
+    "    data_path,\n",
+    "    source_channel=\"Nuclei\",\n",
+    "    target_channel=\"Phase\",\n",
+    "    z_window_size=1,\n",
+    "    split_ratio=0.8,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    "    yx_patch_size=YX_PATCH_SIZE,\n",
+    "    augment=True,\n",
+    ")\n",
+    "fluor2phase_data.setup(\"fit\")\n",
     "\n",
-    "model = model = VSUNet(\n",
-    "    model_config=wider_config.copy(),\n",
+    "# Dictionary that specifies key parameters of the model.\n",
+    "fluor2phase_config = {\n",
+    "    \"architecture\": \"2D\",\n",
+    "    \"in_channels\": 1,\n",
+    "    \"out_channels\": 1,\n",
+    "    \"residual\": True,\n",
+    "    \"dropout\": 0.1,  # dropout randomly turns off weights to avoid overfitting of the model to data.\n",
+    "    \"task\": \"reg\",  # reg = regression task.\n",
+    "    \"num_filters\": [24, 48, 96, 192, 384],\n",
+    "}\n",
+    "\n",
+    "fluor2phase_model = VSUNet(\n",
+    "    model_config=fluor2phase_config.copy(),\n",
     "    batch_size=BATCH_SIZE,\n",
     "    loss_function=torch.nn.functional.mse_loss,\n",
     "    schedule=\"WarmupCosine\",\n",
     "    log_num_samples=10,\n",
+    "    example_input_yx_shape=YX_PATCH_SIZE,\n",
     ")\n",
     "\n",
+    "n_samples = len(fluor2phase_data.train_dataset)\n",
+    "steps_per_epoch = n_samples // BATCH_SIZE\n",
+    "n_epochs = 30\n",
     "\n",
     "trainer = VSTrainer(\n",
     "    accelerator=\"gpu\",\n",
-    "    max_epochs=20,\n",
-    "    log_every_n_steps=8,\n",
-    "    default_root_dir=os.path.expanduser(\"~\"),\n",
+    "    devices=[GPU_ID],\n",
+    "    max_epochs=n_epochs,\n",
+    "    log_every_n_steps=steps_per_epoch,\n",
+    "    default_root_dir=Path(log_dir, \"fluor2phase\"),\n",
+    ")\n",
+    "trainer.logger.log_graph(fluor2phase_model, fluor2phase_data.train_dataset[0][\"source\"])\n",
+    "trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4eb094e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualize the graph of fluor2phase model.\n",
+    "model_graph_fluor2phase = torchview.draw_graph(\n",
+    "    phase2fluor_model,\n",
+    "    phase2fluor_data.train_dataset[0][\"source\"],\n",
+    "    depth=2,  # adjust depth to zoom in.\n",
+    "    device=\"cpu\",\n",
     ")\n",
+    "model_graph_fluor2phase.visual_graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "756351fa",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "We now look at some metrics of performance. Loss is a differentiable metric. But, several non-differentiable metrics are useful to assess the performance of the model. We typically evaluate the model performance on a held out test data. We will use the following metrics to evaluate the accuracy of regression of the model:\n",
+    "- [Coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination): $R^2$ \n",
+    "- [Person Correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).\n",
+    "- [Structural similarity](https://en.wikipedia.org/wiki/Structural_similarity) (SSIM):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46c6b30c",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
     "\n",
-    "trainer.fit(model, datamodule=data_module)"
+    "# TODO: set following parameters, specifically path to checkpoint, and log the metrics.\n",
+    "test_data_path = Path(\n",
+    "    \"~/data/04_image_translation/HEK_nuclei_membrane_test.zarr\"\n",
+    ").expanduser()\n",
+    "model_version = \"phase2fluor\"\n",
+    "save_dir = Path(log_dir, \"test\")\n",
+    "ckpt_path = Path(\n",
+    "    r\"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt\"\n",
+    ")  # prefix the string with 'r' to avoid the need for escape characters.\n",
+    "### END TODO\n",
+    "\n",
+    "test_data = HCSDataModule(\n",
+    "    test_data_path,\n",
+    "    source_channel=\"Phase\",\n",
+    "    target_channel=[\"Nuclei\", \"Membrane\"],\n",
+    "    z_window_size=1,\n",
+    "    batch_size=1,\n",
+    "    num_workers=8,\n",
+    "    architecture=\"2D\",\n",
+    ")\n",
+    "test_data.setup(\"test\")\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    devices=[GPU_ID],\n",
+    "    logger=CSVLogger(save_dir=save_dir, version=model_version),\n",
+    ")\n",
+    "trainer.test(\n",
+    "    phase2fluor_model,\n",
+    "    datamodule=test_data,\n",
+    "    ckpt_path=ckpt_path,\n",
+    ")\n",
+    "# read metrics and plot\n",
+    "metrics = pd.read_csv(Path(save_dir, \"lightning_logs\", model_version, \"metrics.csv\"))\n",
+    "metrics.boxplot(\n",
+    "    column=[\n",
+    "        \"test_metrics/r2_step\",\n",
+    "        \"test_metrics/pearson_step\",\n",
+    "        \"test_metrics/SSIM_step\",\n",
+    "    ],\n",
+    "    rot=30,\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27a7136e",
+   "id": "74972942",
    "metadata": {
     "cell_marker": "\"\"\""
    },
    "source": [
-    "Launch TensorBoard with:\n",
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 2\n",
+    "Please summarize hyperparameters and performance of your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)\n",
     "\n",
-    "```\n",
-    "%load_ext tensorboard\n",
-    "%tensorboard --logdir /path/to/lightning_logs\n",
-    "```"
+    "Now that you have trained two models, let's think about the following questions:\n",
+    "- What is the information content of each channel in the dataset?\n",
+    "- How would you use image translation models?\n",
+    "- What can you try to improve the performance of each model?\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0c4204",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "incorrectly_encoded_metadata": "id='3_tuning'></a>",
+    "title": "<a"
+   },
+   "source": [
+    "# Part 3: Tune the models.\n",
+    "--------------------------------------------------\n",
+    "\n",
+    "Learning goals:\n",
+    "\n",
+    "- Tweak model hyperparameters, such as number of filters at each depth.\n",
+    "- Adjust learning rate to improve performance."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8fe9cbda",
-   "metadata": {},
+   "id": "fd8c7cb1",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "036a0577",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "notebook.list()"
+    "##########################\n",
+    "######## TODO ########\n",
+    "##########################\n",
+    "\n",
+    "# Choose a model you want to train (phase2fluor or fluor2phase).\n",
+    "# Create a config to double the number of filters at each stage.\n",
+    "# Use training loop illustrated in previous cells to train phase2fluor and fluor2phase models to prototype your own training loop."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f3855287",
-   "metadata": {},
+   "id": "da15aa96",
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "notebook.display(port=6006, height=800)"
+    "\n",
+    "##########################\n",
+    "######## Solution ########\n",
+    "##########################\n",
+    "\n",
+    "phase2fluor_wider_config = {\n",
+    "    \"architecture\": \"2D\",\n",
+    "    # double the number of filters at each stage\n",
+    "    \"num_filters\": [48, 96, 192, 384, 768],\n",
+    "    \"in_channels\": 1,\n",
+    "    \"out_channels\": 2,\n",
+    "    \"residual\": True,\n",
+    "    \"dropout\": 0.1,\n",
+    "    \"task\": \"reg\",\n",
+    "}\n",
+    "\n",
+    "phase2fluor_wider_model = VSUNet(\n",
+    "    model_config=phase2fluor_wider_config.copy(),\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    loss_function=torch.nn.functional.l1_loss,\n",
+    "    schedule=\"WarmupCosine\",\n",
+    "    log_num_samples=10,\n",
+    "    example_input_yx_shape=YX_PATCH_SIZE,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    devices=[GPU_ID],\n",
+    "    max_epochs=n_epochs,\n",
+    "    log_every_n_steps=steps_per_epoch,\n",
+    "    default_root_dir=Path(log_dir, \"phase2fluor\"),\n",
+    "    fast_dev_run=True,\n",
+    ")  # Set fast_dev_run to False to train the model.\n",
+    "trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "4a3e91f8",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0181cde7",
    "metadata": {
-    "cell_marker": "\"\"\""
+    "lines_to_next_cell": 2
    },
+   "outputs": [],
    "source": [
-    "<div class=\"alert alert-success\">\n",
-    "Checkpoint 1\n",
+    "##########################\n",
+    "######## TODO ########\n",
+    "##########################\n",
     "\n",
-    "Now the training has started,\n",
-    "we can come back after a while and evaluate the performance!\n",
-    "</div>"
+    "# Choose a model you want to train (phase2fluor or fluor2phase).\n",
+    "# Train it with lower learning rate to see how the performance changes."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "799048df",
-   "metadata": {},
+   "id": "8d85c9f4",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "tags": [
+     "solution"
+    ]
+   },
    "outputs": [],
-   "source": []
+   "source": [
+    "\n",
+    "##########################\n",
+    "######## Solution ########\n",
+    "##########################\n",
+    "\n",
+    "phase2fluor_slow_model = VSUNet(\n",
+    "    model_config=phase2fluor_config.copy(),\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    loss_function=torch.nn.functional.l1_loss,\n",
+    "    # lower learning rate by 5 times\n",
+    "    lr=2e-4,\n",
+    "    schedule=\"WarmupCosine\",\n",
+    "    log_num_samples=10,\n",
+    "    example_input_yx_shape=YX_PATCH_SIZE,\n",
+    ")\n",
+    "\n",
+    "trainer = VSTrainer(\n",
+    "    accelerator=\"gpu\",\n",
+    "    devices=[GPU_ID],\n",
+    "    max_epochs=n_epochs,\n",
+    "    log_every_n_steps=steps_per_epoch,\n",
+    "    default_root_dir=Path(log_dir, \"phase2fluor\"),\n",
+    "    fast_dev_run=True,\n",
+    ")\n",
+    "trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b735a925",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "Checkpoint 3\n",
+    "\n",
+    "Congratulations! You have trained several image translation models now!\n",
+    "Please document hyperparameters, snapshots of predictioons on validation set, and loss curves for your models in [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z)\n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/demo_dlmbl/solution.py b/examples/demo_dlmbl/solution.py
index 83be3748..b318ebd5 100644
--- a/examples/demo_dlmbl/solution.py
+++ b/examples/demo_dlmbl/solution.py
@@ -4,7 +4,7 @@
 ---
 
 Written by Ziwen Liu and Shalin Mehta, CZ Biohub San Francisco.
-
+---
 
 In this exercise, we will solve an image translation task to predict fluorescence images of nuclei and membrane markers from quantitative phase images of cells. In other words, we will _virtually stain_ the nuclei and membrane visible in the phase image. 
 
@@ -25,20 +25,22 @@
 Today, we will train a 2D image translation model using a 2D U-Net with residual connections. We will use a dataset of 301 fields of view (FOVs) of Human Embryonic Kidney (HEK) cells, each FOV has 3 channels (phase, membrane, and nuclei). The cells were labeled with CRISPR editing. Intrestingly, not all cells during this experiment were labeled due to the stochastic nature of CRISPR editing. In such situations, virtual staining rescues missing labels.
 ![HEK](https://github.com/mehta-lab/VisCy/blob/dlmbl2023/docs/figures/phase_to_nuclei_membrane.svg?raw=true)
 
+<div class="alert alert-info">
 The exercise is organized in 3 parts.
 
-* [Part 1](#1_phase2fluor) - Explore the data and model using tensorboard. Launch the training before lunch.
+* **Part 1** - Explore the data using tensorboard. Launch the training before lunch.
 * Lunch break - The model will continue training during lunch.
-* [Part 2](#2_fluor2phase) - Evaluate the training with tensorboard. Train a model to predict phase from fluorescence.
-* [Part 3, bonus](#3_tuning) - Tune the capacity of networks and other hyperparameters to improve the performance.
-
+* **Part 2** - Evaluate the training with tensorboard. Train another model.
+* **Part 3** - Tune the models to improve performance.
+</div>
 
 📖 As you work through parts 2 and 3, please share the layouts of the models you train and their performance with everyone via [this google doc](https://docs.google.com/document/d/1hZWSVRvt9KJEdYu7ib-vFBqAVQRYL8cWaP_vFznu7D8/edit#heading=h.n5u485pmzv2z) 📖.
 
-<div class="alert alert-info">
+
 Our guesstimate is that each of the three parts will take ~1.5 hours, but don't rush parts 1 and 2 if you need more time with them.
-We will discuss your observations on google doc after checkpoints 2 and 3.
-</div>
+We will discuss your observations on google doc after checkpoints 2 and 3. The exercise is focused on understanding information contained in data, process of training and evaluating image translation models, and parameter exploration.
+There are a few coding tasks sprinkled in.
+
 
 Before you start,
 
@@ -48,7 +50,7 @@
 """
 # %% [markdown] <a id='1_phase2fluor'></a>
 """
-# Part 1: Visualize training data using tensorboard, start training a model.
+# Part 1: Log training data to tensorboard, start training a model.
 ---------
 
 Learning goals:
@@ -97,8 +99,10 @@
 # Create log directory if needed, and launch tensorboard
 log_dir.mkdir(parents=True, exist_ok=True)
 
+# fmt: off
 %reload_ext tensorboard
 %tensorboard --logdir {log_dir}
+# fmt: on
 
 
 # %% [markdown]
@@ -179,9 +183,10 @@
 
 """
 
-# %% 
+# %%
 # Define a function to write a batch to tensorboard log.
 
+
 def log_batch_tensorboard(batch, batchno, writer, card_name):
     """
     Logs a batch of images to TensorBoard.
@@ -227,7 +232,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # Initialize the data module.
 
-BATCH_SIZE = 42 
+BATCH_SIZE = 42
 # 42 is a perfectly reasonable batch size. After all, it is the answer to the ultimate question of life, the universe and everything.
 # More seriously, batch size does not have to be a power of 2.
 # See: https://sebastianraschka.com/blog/2022/batch-size-2.html
@@ -317,7 +322,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 We setup a fresh data module and instantiate the trainer class.
 """
 
-# %% 
+# %%
 
 # The entire training loop is contained in this cell.
 
@@ -342,7 +347,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     batch_size=BATCH_SIZE,
     loss_function=torch.nn.functional.l1_loss,
     schedule="WarmupCosine",
-    log_num_samples=10, # Number of samples from each batch to log to tensorboard.
+    log_num_samples=10,  # Number of samples from each batch to log to tensorboard.
     example_input_yx_shape=YX_PATCH_SIZE,
 )
 
@@ -377,16 +382,13 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # %% Setup trainer and check for errors.
 
 # fast_dev_run runs a single batch of data through the model to check for errors.
-trainer = VSTrainer(
-    accelerator="gpu", devices=[GPU_ID], fast_dev_run=True
-)
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True)
 
 # trainer class takes the model and the data module as inputs.
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
 
-
-# %% 
+# %%
 
 GPU_ID = 0
 n_samples = len(phase2fluor_data.train_dataset)
@@ -397,9 +399,11 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     accelerator="gpu",
     devices=[GPU_ID],
     max_epochs=n_epochs,
-    log_every_n_steps=steps_per_epoch // 2, 
+    log_every_n_steps=steps_per_epoch // 2,
     # log losses and image samples 2 times per epoch.
-    default_root_dir=Path(log_dir, "phase2fluor"), # lightning trainer transparently saves logs and model checkpoints in this directory.
+    default_root_dir=Path(
+        log_dir, "phase2fluor"
+    ),  # lightning trainer transparently saves logs and model checkpoints in this directory.
 )
 
 # Log graph
@@ -420,7 +424,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # %% [markdown] <a id='1_fluor2phase'></a>
 """
-# Part 2: Visualize the previous model and training with tensorboard. Train fluorescence to phase contrast translation model.
+# Part 2: Assess previous model, train fluorescence to phase contrast translation model.
 --------------------------------------------------
 
 Learning goals:
@@ -434,11 +438,11 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # PyTorch uses dynamic graphs under the hood. The graphs are constructed on the fly. This is in contrast to TensorFlow, where the graph is constructed before the training loop and remains static. In other words, the graph of the network can change with every forward pass. Therefore, we need to supply an input tensor to construct the graph. The input tensor can be a random tensor of the correct shape and type. We can also supply a real image from the dataset. The latter is more useful for debugging.
 
-# visualize graph. 
+# visualize graph.
 model_graph_phase2fluor = torchview.draw_graph(
     phase2fluor_model,
     phase2fluor_data.train_dataset[0]["source"],
-    depth=2, # adjust depth to zoom in.
+    depth=2,  # adjust depth to zoom in.
     device="cpu",
 )
 # Increase the depth to zoom in.
@@ -493,12 +497,12 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 trainer.logger.log_graph(fluor2phase_model, fluor2phase_data.train_dataset[0]["source"])
 trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)
 
-# %% 
-# Visualize the graph of fluor2phase model. 
+# %%
+# Visualize the graph of fluor2phase model.
 model_graph_fluor2phase = torchview.draw_graph(
     phase2fluor_model,
     phase2fluor_data.train_dataset[0]["source"],
-    depth=2, # adjust depth to zoom in.
+    depth=2,  # adjust depth to zoom in.
     device="cpu",
 )
 model_graph_fluor2phase.visual_graph
@@ -514,10 +518,14 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 # %%
 
 # TODO: set following parameters, specifically path to checkpoint, and log the metrics.
-test_data_path = Path("~/data/04_image_translation/HEK_nuclei_membrane_test.zarr").expanduser()
+test_data_path = Path(
+    "~/data/04_image_translation/HEK_nuclei_membrane_test.zarr"
+).expanduser()
 model_version = "phase2fluor"
 save_dir = Path(log_dir, "test")
-ckpt_path = Path(r"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt") #prefix the string with 'r' to avoid the need for escape characters.
+ckpt_path = Path(
+    r"/home/mehtas/data/04_image_translation/logs/phase2fluor/lightning_logs/version_0/checkpoints/epoch=29-step=720.ckpt"
+)  # prefix the string with 'r' to avoid the need for escape characters.
 ### END TODO
 
 test_data = HCSDataModule(
@@ -567,7 +575,7 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
 
 # %% [markdown] <a id='3_tuning'></a>
 """
-# Part 3: Run computational experiments to tune the capacity of models.
+# Part 3: Tune the models.
 --------------------------------------------------
 
 Learning goals:
@@ -612,9 +620,16 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     log_num_samples=10,
     example_input_yx_shape=YX_PATCH_SIZE,
 )
- 
 
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs=n_epochs, log_every_n_steps=steps_per_epoch, default_root_dir=Path(log_dir, "phase2fluor"), fast_dev_run=True) # Set fast_dev_run to False to train the model.
+
+trainer = VSTrainer(
+    accelerator="gpu",
+    devices=[GPU_ID],
+    max_epochs=n_epochs,
+    log_every_n_steps=steps_per_epoch,
+    default_root_dir=Path(log_dir, "phase2fluor"),
+    fast_dev_run=True,
+)  # Set fast_dev_run to False to train the model.
 trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
 
 # %%
@@ -643,7 +658,14 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     example_input_yx_shape=YX_PATCH_SIZE,
 )
 
-trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs=n_epochs, log_every_n_steps=steps_per_epoch, default_root_dir=Path(log_dir, "phase2fluor"), fast_dev_run=True)
+trainer = VSTrainer(
+    accelerator="gpu",
+    devices=[GPU_ID],
+    max_epochs=n_epochs,
+    log_every_n_steps=steps_per_epoch,
+    default_root_dir=Path(log_dir, "phase2fluor"),
+    fast_dev_run=True,
+)
 trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)
 
 

From 75cfee1c476dd1c9edbb46f44338fbd8c7090e2b Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@czbiohub.org>
Date: Mon, 21 Aug 2023 23:06:38 -0400
Subject: [PATCH 26/29] viscy -> VisCy (#34) (#39)

Introducing capitalization to highlight vision and single-cell aspects of the pipeline.
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 65e0f5eb..843a3066 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
-# viscy
+# VisCy
 
-viscy is a deep learning pipeline for training and deploying computer vision models for image-based phenotyping at single cell resolution.
+VisCy is a deep learning pipeline for training and deploying computer vision models for image-based phenotyping at single cell resolution.
 
 The current focus of the pipeline is on the image translation models for virtual staining of multiple cellular compartments from label-free images.
 We are building these models for simultaneous segmentation of nuclei and membrane, which are the first steps in a single-cell phenotyping pipeline.

From 0db12c76762a11ee3803c3c886f7215af0b2d2f7 Mon Sep 17 00:00:00 2001
From: Shalin Mehta <shalin.mehta@gmail.com>
Date: Tue, 22 Aug 2023 04:12:41 +0000
Subject: [PATCH 27/29] trying to log graph

---
 examples/demo_dlmbl/debug_log_graph.py | 96 ++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)
 create mode 100644 examples/demo_dlmbl/debug_log_graph.py

diff --git a/examples/demo_dlmbl/debug_log_graph.py b/examples/demo_dlmbl/debug_log_graph.py
new file mode 100644
index 00000000..34b8c84a
--- /dev/null
+++ b/examples/demo_dlmbl/debug_log_graph.py
@@ -0,0 +1,96 @@
+
+# %% 
+# %% Imports and paths
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+import torchview
+import torchvision
+from iohub import open_ome_zarr
+from lightning.pytorch import seed_everything
+from lightning.pytorch.loggers import CSVLogger
+
+# pytorch lightning wrapper for Tensorboard.
+from tensorboard import notebook  # for viewing tensorboard in notebook
+from torch.utils.tensorboard import SummaryWriter  # for logging to tensorboard
+
+# HCSDataModule makes it easy to load data during training.
+from viscy.light.data import HCSDataModule
+
+# Trainer class and UNet.
+from viscy.light.engine import VSTrainer, VSUNet
+
+seed_everything(42, workers=True)
+
+# Paths to data and log directory
+data_path = Path(
+    Path("~/data/04_image_translation/HEK_nuclei_membrane_pyramid.zarr/")
+).expanduser()
+
+log_dir = Path("~/data/04_image_translation/logs/").expanduser()
+
+# Create log directory if needed, and launch tensorboard
+log_dir.mkdir(parents=True, exist_ok=True)
+
+# fmt: off
+%reload_ext tensorboard
+%tensorboard --logdir {log_dir} --port 6007 --bind_all
+# fmt: on
+
+# %%  The entire training loop is contained in this cell.
+
+GPU_ID = 0
+BATCH_SIZE = 10
+YX_PATCH_SIZE = (512, 512)
+
+
+# Dictionary that specifies key parameters of the model.
+phase2fluor_config = {
+    "architecture": "2D",
+    "num_filters": [24, 48, 96, 192, 384],
+    "in_channels": 1,
+    "out_channels": 2,
+    "residual": True,
+    "dropout": 0.1,  # dropout randomly turns off weights to avoid overfitting of the model to data.
+    "task": "reg",  # reg = regression task.
+}
+
+phase2fluor_model = VSUNet(
+    model_config=phase2fluor_config.copy(),
+    batch_size=BATCH_SIZE,
+    loss_function=torch.nn.functional.l1_loss,
+    schedule="WarmupCosine",
+    log_num_samples=10,  # Number of samples from each batch to log to tensorboard.
+    example_input_yx_shape=YX_PATCH_SIZE,
+)
+
+# Reinitialize the data module.
+phase2fluor_data = HCSDataModule(
+    data_path,
+    source_channel="Phase",
+    target_channel=["Nuclei", "Membrane"],
+    z_window_size=1,
+    split_ratio=0.8,
+    batch_size=BATCH_SIZE,
+    num_workers=8,
+    architecture="2D",
+    yx_patch_size=YX_PATCH_SIZE,
+    augment=True,
+)
+phase2fluor_data.setup("fit")
+
+
+# Train for 3 epochs to see if you can log graph.
+trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], max_epochs=3, default_root_dir=log_dir)
+
+# trainer class takes the model and the data module as inputs.
+trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
+
+# %% Is exmple_input_array present?
+print(f'{phase2fluor_model.example_input_array.shape},{phase2fluor_model.example_input_array.dtype}')
+trainer.logger.log_graph(phase2fluor_model, phase2fluor_model.example_input_array)
+# %%

From 7c5e4c1d68e70163cf514d22c475da8ea7dc3a88 Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Tue, 22 Aug 2023 11:20:42 -0700
Subject: [PATCH 28/29] log graph

---
 examples/demo_dlmbl/solution.py | 36 +++++++++++++++++++++++----------
 1 file changed, 25 insertions(+), 11 deletions(-)

diff --git a/examples/demo_dlmbl/solution.py b/examples/demo_dlmbl/solution.py
index b318ebd5..661a1fb4 100644
--- a/examples/demo_dlmbl/solution.py
+++ b/examples/demo_dlmbl/solution.py
@@ -75,7 +75,7 @@
 import torchvision
 from iohub import open_ome_zarr
 from lightning.pytorch import seed_everything
-from lightning.pytorch.loggers import CSVLogger
+from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
 
 # pytorch lightning wrapper for Tensorboard.
 from tensorboard import notebook  # for viewing tensorboard in notebook
@@ -399,15 +399,16 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     accelerator="gpu",
     devices=[GPU_ID],
     max_epochs=n_epochs,
-    log_every_n_steps=steps_per_epoch // 2,
     # log losses and image samples 2 times per epoch.
-    default_root_dir=Path(
-        log_dir, "phase2fluor"
-    ),  # lightning trainer transparently saves logs and model checkpoints in this directory.
+    log_every_n_steps=steps_per_epoch // 2,
+    # lightning trainer transparently saves logs and model checkpoints in this directory
+    logger=TensorBoardLogger(
+        save_dir=log_dir,
+        name="phase2fluor",
+        log_graph=True,
+    ),
 )
 
-# Log graph
-trainer.logger.log_graph(phase2fluor_model, phase2fluor_data.train_dataset[0]["source"])
 # Launch training.
 trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)
 
@@ -492,9 +493,12 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     devices=[GPU_ID],
     max_epochs=n_epochs,
     log_every_n_steps=steps_per_epoch,
-    default_root_dir=Path(log_dir, "fluor2phase"),
+    logger=TensorBoardLogger(
+        save_dir=log_dir,
+        name="fluor2phase",
+        log_graph=True,
+    ),
 )
-trainer.logger.log_graph(fluor2phase_model, fluor2phase_data.train_dataset[0]["source"])
 trainer.fit(fluor2phase_model, datamodule=fluor2phase_data)
 
 # %%
@@ -627,7 +631,12 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     devices=[GPU_ID],
     max_epochs=n_epochs,
     log_every_n_steps=steps_per_epoch,
-    default_root_dir=Path(log_dir, "phase2fluor"),
+    logger=TensorBoardLogger(
+        save_dir=log_dir,
+        name="phase2fluor",
+        version="wider",
+        log_graph=True,
+    ),
     fast_dev_run=True,
 )  # Set fast_dev_run to False to train the model.
 trainer.fit(phase2fluor_wider_model, datamodule=phase2fluor_data)
@@ -663,7 +672,12 @@ def log_batch_tensorboard(batch, batchno, writer, card_name):
     devices=[GPU_ID],
     max_epochs=n_epochs,
     log_every_n_steps=steps_per_epoch,
-    default_root_dir=Path(log_dir, "phase2fluor"),
+    logger=TensorBoardLogger(
+        save_dir=log_dir,
+        name="phase2fluor",
+        version="low_lr",
+        log_graph=True,
+    ),
     fast_dev_run=True,
 )
 trainer.fit(phase2fluor_slow_model, datamodule=phase2fluor_data)

From 04075882551f2430c588e0f0e97f94b3d3c706ed Mon Sep 17 00:00:00 2001
From: Ziwen Liu <ziwen.liu@czbiohub.org>
Date: Wed, 23 Aug 2023 09:39:12 -0700
Subject: [PATCH 29/29] black

---
 viscy/scripts/count_flops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/viscy/scripts/count_flops.py b/viscy/scripts/count_flops.py
index 58662663..25bc024b 100644
--- a/viscy/scripts/count_flops.py
+++ b/viscy/scripts/count_flops.py
@@ -12,7 +12,7 @@
         "out_channels": 2,
         "in_stack_depth": 9,
         "backbone": "convnextv2_tiny",
-        "stem_kernel_size": (9, 4, 4)
+        "stem_kernel_size": (9, 4, 4),
     },
 )