From 76c3b31618f09a4fb54b1b8d3dc2d1ab55a561eb Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Wed, 23 Aug 2023 09:51:44 -0700 Subject: [PATCH] DLMBL 2023 excercise (#36) * updated intro and paths * updated figures, tested data loader * setup.sh fetches correct dataset * finalized the exercise outline * semi-final exercise * parts 1 and 2 tested, part 3 outline ready * clearer variables, train with larger patch size * fix typo * clarify variable names * trying to log graph * match example size with training * reuse globals * fix reference * log sample images from the first batch * wider model * low LR solution * fix path * seed everything * fix test dataset without masks * metrics solution this needs a new test dataset * fetch test data, compute metrics * byass cellpose import error due to numpy version conflicts * final exercise * moved files * fixed formatting - ready for review * viscy -> VisCy (#34) (#39) Introducing capitalization to highlight vision and single-cell aspects of the pipeline. * trying to log graph * log graph * black --------- Co-authored-by: Shalin Mehta Co-authored-by: Shalin Mehta --- README.md | 4 +- docs/figures/phase_to_nuclei_membrane.png | Bin 0 -> 1441863 bytes examples/demo_dlmbl/convert-solution.py | 41 + examples/demo_dlmbl/debug_log_graph.py | 96 +++ examples/demo_dlmbl/exercise.ipynb | 754 ++++++++++++++++++ examples/demo_dlmbl/prepare-exercise.sh | 10 + examples/demo_dlmbl/python/excercise_1.py | 285 ------- examples/demo_dlmbl/setup.sh | 35 + examples/demo_dlmbl/solution.ipynb | 929 ++++++++++++++++++++++ examples/demo_dlmbl/solution.py | 694 ++++++++++++++++ pyproject.toml | 2 +- viscy/light/data.py | 19 +- viscy/light/engine.py | 30 +- viscy/scripts/count_flops.py | 2 +- 14 files changed, 2596 insertions(+), 305 deletions(-) create mode 100644 docs/figures/phase_to_nuclei_membrane.png create mode 100644 examples/demo_dlmbl/convert-solution.py create mode 100644 examples/demo_dlmbl/debug_log_graph.py create mode 100644 examples/demo_dlmbl/exercise.ipynb create mode 100644 examples/demo_dlmbl/prepare-exercise.sh delete mode 100644 examples/demo_dlmbl/python/excercise_1.py create mode 100644 examples/demo_dlmbl/setup.sh create mode 100644 examples/demo_dlmbl/solution.ipynb create mode 100644 examples/demo_dlmbl/solution.py 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. 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+ 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/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) +# %% diff --git a/examples/demo_dlmbl/exercise.ipynb b/examples/demo_dlmbl/exercise.ipynb new file mode 100644 index 00000000..b6a3a256 --- /dev/null +++ b/examples/demo_dlmbl/exercise.ipynb @@ -0,0 +1,754 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "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", + "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 pipeline, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive." + ] + }, + { + "cell_type": "markdown", + "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", + "

\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", + "Before you start,\n", + "\n", + "
\n", + "Set your python kernel to 04-image-translation\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ff42876f", + "metadata": { + "cell_marker": "\"\"\"", + "incorrectly_encoded_metadata": "id='1_phase2fluor'>
", + "title": "\n", + "Task 1.1\n", + "Use \n", + "iohub.open_ome_zarr to read the dataset and explore several FOVs with matplotlib.\n", + "\n", + "\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 \n", + "High-Content Screening (HCS) layout\n", + "specified by the Open Microscopy Environment Next Generation File Format\n", + "(OME-NGFF).\n", + "\n", + "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": "e0241037", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dataset = open_ome_zarr(data_path)\n", + "\n", + "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 = \"23\"\n", + "\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 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", + "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", + " 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", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "89e676bf", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Initialize data loaders and see the samples in tensorboard.\n", + "\n", + "
\n", + "Task 1.2\n", + "Setup the data loader and log several batches to tensorboard.\n", + "
`\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": "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", + " 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=(512, 512), # larger patch size makes it easy to see augmentations.\n", + " augment=False, # Turn off augmentation for now.\n", + ")\n", + "data_module.setup(\"fit\")\n", + "\n", + "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": "f6ffc7d1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "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", + "
\n", + "Task 1.3\n", + "Turn on augmentation and view the batch in tensorboard." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "245ae4dd", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "##########################\n", + "######## TODO ########\n", + "##########################\n", + "\n", + "# 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": "85743be9", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Construct a 2D U-Net for image translation.\n", + "See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.\n", + "We setup a fresh data module and instantiate the trainer class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44fddf02", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "\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, # dropout randomly turns off weights to avoid overfitting of the model to data.\n", + " \"task\": \"reg\", # reg = regression task.\n", + "}\n", + "\n", + "phase2fluor_model = VSUNet(\n", + " model_config=phase2fluor_config.copy(),\n", + " batch_size=BATCH_SIZE,\n", + " loss_function=torch.nn.functional.l1_loss,\n", + " schedule=\"WarmupCosine\",\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", + "# 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": "292cb22a", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "
\n", + "Task 1.4\n", + "Setup the training for ~30 epochs\n", + "
\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": "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 class takes the model and the data module as inputs.\n", + "trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f11d604", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "\n", + "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": "157a989c", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "
\n", + "Checkpoint 1\n", + "\n", + "Now the training has started,\n", + "we can come back after a while and evaluate the performance!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "d68dab93", + "metadata": { + "cell_marker": "\"\"\"", + "incorrectly_encoded_metadata": "id='1_fluor2phase'>", + "title": "\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", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7d0c4204", + "metadata": { + "cell_marker": "\"\"\"", + "incorrectly_encoded_metadata": "id='3_tuning'>", + "title": "\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", + "" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/demo_dlmbl/prepare-exercise.sh b/examples/demo_dlmbl/prepare-exercise.sh new file mode 100644 index 00000000..88f2ba1b --- /dev/null +++ b/examples/demo_dlmbl/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/excercise_1.py b/examples/demo_dlmbl/python/excercise_1.py deleted file mode 100644 index 0904b75a..00000000 --- a/examples/demo_dlmbl/python/excercise_1.py +++ /dev/null @@ -1,285 +0,0 @@ -# %% [markdown] -""" -# Image translation excercise part 1 - -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). - -Learning goals of part 1: - -- Load the and visualize the images from OME-Zarr -- Configure the data loaders -- Initialize a 2D U-Net model for virtual staining - - -
-Set your python kernel to 004-image-translation -
-""" - -# %% -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 viscy.light.data import HCSDataModule -from viscy.light.engine import VSTrainer, VSUNet - -BATCH_SIZE = 32 -GPU_ID = 0 - -# %% [markdown] -""" -Load Dataset. - -
-Task 1.1 - -Use -iohub.open_ome_zarr to read the dataset. - -There should be 301 FOVs in the dataset (9.3 GB compressed). - -Each FOV consists of 3 channels of 2048x2048 images, -saved in the -High-Content Screening (HCS) layout -specified by the Open Microscopy Environment Next Generation File Format -(OME-NGFF). - -Run open_ome_zarr? in a cell to see the docstring. - -""" - -# %% -# set dataset path here -data_path = "/hpc/projects/comp.micro/virtual_staining/datasets/dlmbl/HEK_nuclei_membrane_pyramid.zarr" - -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/resolution/timepoint/channel/z/y/x. - - -Note that labelling is not perfect, -as some cells are not expressing the fluorophore. -""" - -# %% - -row = "0" -col = "0" -field = "0" -# '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") - -plt.tight_layout() - -# %% [markdown] -""" -Configure the data loaders for training and validation. -""" - -# %% -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), -) - -data_module.setup("fit") - -print(len(data_module.train_dataset), len(data_module.val_dataset)) - -# %% [markdown] -""" -
-Task 1.2 - -Validate that the data can be loaded in batches correctly. -
-""" - -# %% -train_dataloader = data_module.train_dataloader() - -for i, batch in enumerate(train_dataloader): - ... - # plot one image from each of the batch and break - break - -# %% tags=["solution"] -train_dataloader = data_module.train_dataloader() - - -fig, axs = plt.subplots(3, 8, figsize=(20, 6)) - -# Draw 8 batches, each with 32 images. Show the first image in each 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() - - -# %% [markdown] -""" -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. -""" - -# %% -model_config = { - "architecture": "2D", - "in_channels": 1, - "out_channels": 2, - "residual": True, - "dropout": 0.1, - "task": "reg", -} - -model = VSUNet( - model_config=model_config.copy(), - batch_size=BATCH_SIZE, - loss_function=torch.nn.functional.mse_loss, - schedule="WarmupCosine", - 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. -""" - -# %% -trainer = VSTrainer(accelerator="gpu", devices=[GPU_ID], fast_dev_run=True) - -trainer.fit(model, datamodule=data_module) - -# %% [markdown] -""" -
-Task 1.3 - -Modify the trainer to train the model for 20 epochs. -
-""" - -# %% [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, - schedule="WarmupCosine", - log_num_samples=10, -) - - -trainer = VSTrainer( - accelerator="gpu", max_epochs=20, log_every_n_steps=8, default_root_dir=os.path.expanduser("~") -) - -trainer.fit(model, datamodule=data_module) - -# %% [markdown] -""" -Launch TensorBoard with: - -``` -%load_ext tensorboard -%tensorboard --logdir /path/to/lightning_logs -``` -""" - -# %% -notebook.list() - -# %% -notebook.display(port=6006, height=800) - -# %% [markdown] -""" -
-Checkpoint 1 - -Now the training has started, -we can come back after a while and evaluate the performance! -
-""" - -# %% diff --git a/examples/demo_dlmbl/setup.sh b/examples/demo_dlmbl/setup.sh new file mode 100644 index 00000000..6e1f1b57 --- /dev/null +++ b/examples/demo_dlmbl/setup.sh @@ -0,0 +1,35 @@ +#!/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 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 $2}') +# install viscy and its dependencies in the environment using pip. +$ENV_PATH/bin/pip install "viscy[metrics] @ git+https://github.com/mehta-lab/viscy.git@dlmbl2023" + +# 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 +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 + + +# 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 diff --git a/examples/demo_dlmbl/solution.ipynb b/examples/demo_dlmbl/solution.ipynb new file mode 100644 index 00000000..c97b8577 --- /dev/null +++ b/examples/demo_dlmbl/solution.ipynb @@ -0,0 +1,929 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "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", + "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 pipeline, [microDL](https://github.com/mehta-lab/microDL), is deprecated and is now a public archive." + ] + }, + { + "cell_type": "markdown", + "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", + "
\n", + "The exercise is organized in 3 parts.\n", + "\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** - Evaluate the training with tensorboard. Train another model.\n", + "* **Part 3** - Tune the models to improve performance.\n", + "
\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", + "Before you start,\n", + "\n", + "
\n", + "Set your python kernel to 04-image-translation\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ff42876f", + "metadata": { + "cell_marker": "\"\"\"", + "incorrectly_encoded_metadata": "id='1_phase2fluor'>", + "title": "\n", + "Task 1.1\n", + "Use \n", + "iohub.open_ome_zarr to read the dataset and explore several FOVs with matplotlib.\n", + "
\n", + "\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 \n", + "High-Content Screening (HCS) layout\n", + "specified by the Open Microscopy Environment Next Generation File Format\n", + "(OME-NGFF).\n", + "\n", + "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": "e0241037", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dataset = open_ome_zarr(data_path)\n", + "\n", + "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 = \"23\"\n", + "\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 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", + "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", + " 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", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "89e676bf", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Initialize data loaders and see the samples in tensorboard.\n", + "\n", + "
\n", + "Task 1.2\n", + "Setup the data loader and log several batches to tensorboard.\n", + "
`\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": "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", + " 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=(512, 512), # larger patch size makes it easy to see augmentations.\n", + " augment=False, # Turn off augmentation for now.\n", + ")\n", + "data_module.setup(\"fit\")\n", + "\n", + "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": "f6ffc7d1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "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", + "
\n", + "Task 1.3\n", + "Turn on augmentation and view the batch in tensorboard." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "245ae4dd", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "##########################\n", + "######## TODO ########\n", + "##########################\n", + "\n", + "# 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": "e14f4faf", + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "##########################\n", + "######## Solution ########\n", + "##########################\n", + "\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", + " log_batch_tensorboard(batch, i, writer, \"augmentation/some\")\n", + "writer.close()" + ] + }, + { + "cell_type": "markdown", + "id": "85743be9", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Construct a 2D U-Net for image translation.\n", + "See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details.\n", + "We setup a fresh data module and instantiate the trainer class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44fddf02", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "\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, # dropout randomly turns off weights to avoid overfitting of the model to data.\n", + " \"task\": \"reg\", # reg = regression task.\n", + "}\n", + "\n", + "phase2fluor_model = VSUNet(\n", + " model_config=phase2fluor_config.copy(),\n", + " batch_size=BATCH_SIZE,\n", + " loss_function=torch.nn.functional.l1_loss,\n", + " schedule=\"WarmupCosine\",\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", + "# 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": "292cb22a", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "
\n", + "Task 1.4\n", + "Setup the training for ~30 epochs\n", + "
\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": "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 class takes the model and the data module as inputs.\n", + "trainer.fit(phase2fluor_model, datamodule=phase2fluor_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f11d604", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "\n", + "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": "157a989c", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "
\n", + "Checkpoint 1\n", + "\n", + "Now the training has started,\n", + "we can come back after a while and evaluate the performance!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "d68dab93", + "metadata": { + "cell_marker": "\"\"\"", + "incorrectly_encoded_metadata": "id='1_fluor2phase'>", + "title": "\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", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7d0c4204", + "metadata": { + "cell_marker": "\"\"\"", + "incorrectly_encoded_metadata": "id='3_tuning'>", + "title": "\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", + "" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/demo_dlmbl/solution.py b/examples/demo_dlmbl/solution.py new file mode 100644 index 00000000..661a1fb4 --- /dev/null +++ b/examples/demo_dlmbl/solution.py @@ -0,0 +1,694 @@ +# %% [markdown] +""" +# Image translation +--- + +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. + +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). + +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 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.svg?raw=true) + +
+The exercise is organized in 3 parts. + +* **Part 1** - Explore the data using tensorboard. Launch the training before lunch. +* Lunch break - The model will continue training during lunch. +* **Part 2** - Evaluate the training with tensorboard. Train another model. +* **Part 3** - Tune the models to improve 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) 📖. + + +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. 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, + +
+Set your python kernel to 04-image-translation +
+""" +# %% [markdown] +""" +# Part 1: Log training data to tensorboard, start training a model. +--------- + +Learning goals: + +- 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. + +""" + +# %% 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, TensorBoardLogger + +# 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} +# fmt: on + + +# %% [markdown] +""" +## Load Dataset. + +
+Task 1.1 +Use +iohub.open_ome_zarr to read the dataset and explore several FOVs with matplotlib. +
+ +There should be 301 FOVs in the dataset (12 GB compressed). + +Each FOV consists of 3 channels of 2048x2048 images, +saved in the +High-Content Screening (HCS) layout +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. + +""" + +# %% + +dataset = open_ome_zarr(data_path) + +print(f"Number of positions: {len(list(dataset.positions()))}") + +# Use the field and pyramid_level below to visualize data. +row = "0" +col = "0" +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 = "0" + +# `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() +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. + 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]) +plt.tight_layout() + +# %% [markdown] +""" +## Initialize data loaders and see the samples in tensorboard. + +
+Task 1.2 +Setup the data loader and log several batches to tensorboard. +
` + +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. + +""" + +# %% +# 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. + + 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. + + 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) + + 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)) + 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) + + # add the grid to tensorboard + writer.add_image(card_name, grid, batchno) + + +# %% + +# Initialize the data module. + +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() + +# %% [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] +""" +## View augmentations using tensorboard. + +
+Task 1.3 +Turn on augmentation and view the batch in tensorboard. +""" +# %% +########################## +######## 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 +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(batch, i, writer, "augmentation/some") +writer.close() + +# %% [markdown] +""" +## Construct a 2D U-Net for image translation. +See ``viscy.unet.networks.Unet2D.Unet2d`` for configuration details. +We setup a fresh data module and instantiate the trainer class. +""" + +# %% + +# 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") + + +# %% [markdown] +""" +
+Task 1.4 +Setup the training for ~30 epochs +
+ +Tips: +- Set ``default_root_dir`` to store the logs and checkpoints +in a specific directory. +""" + +# %% 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) +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 losses and image samples 2 times per epoch. + 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, + ), +) + +# Launch training. +trainer.fit(phase2fluor_model, datamodule=phase2fluor_data) + + +# %% [markdown] +""" +
+Checkpoint 1 + +Now the training has started, +we can come back after a while and evaluate the performance! +
+""" + +# %% [markdown] +""" +# Part 2: Assess previous model, train fluorescence to phase contrast translation model. +-------------------------------------------------- + +Learning goals: +- Visualize the previous model and training with tensorboard +- Train fluorescence to phase contrast translation model +- Compare the performance of the two models. + +""" + +# %% + +# 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, # adjust depth to zoom in. + device="cpu", +) +# Increase the depth to zoom in. +model_graph_phase2fluor.visual_graph + +# %% tags = ["solution"] +fluor2phase_data = 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=YX_PATCH_SIZE, + augment=True, +) +fluor2phase_data.setup("fit") + +# Dictionary that specifies key parameters of the model. +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], +} + +fluor2phase_model = VSUNet( + model_config=fluor2phase_config.copy(), + batch_size=BATCH_SIZE, + 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 = 30 + +trainer = VSTrainer( + accelerator="gpu", + devices=[GPU_ID], + max_epochs=n_epochs, + log_every_n_steps=steps_per_epoch, + logger=TensorBoardLogger( + save_dir=log_dir, + name="fluor2phase", + log_graph=True, + ), +) +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] +""" +
+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? + + +
+""" + +# %% [markdown] +""" +# Part 3: Tune the models. +-------------------------------------------------- + +Learning goals: + +- 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 + "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], + max_epochs=n_epochs, + log_every_n_steps=steps_per_epoch, + 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) + +# %% +########################## +######## 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, + 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], + max_epochs=n_epochs, + log_every_n_steps=steps_per_epoch, + 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) + + +# %% [markdown] +""" +
+Checkpoint 3 + +Congratulations! You have trained several image translation models now! +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) +
+""" diff --git a/pyproject.toml b/pyproject.toml index 42ae853e..485f823f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ dependencies = [ "ipykernel", # used by demo_dlmbl "graphviz", # used by demo_dlmbl "torchview", # used by demo_dlmbl -] + ] dynamic = ["version"] [project.optional-dependencies] diff --git a/viscy/light/data.py b/viscy/light/data.py index fe58f25b..41eb62f6 100644 --- a/viscy/light/data.py +++ b/viscy/light/data.py @@ -460,12 +460,19 @@ 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 diff --git a/viscy/light/engine.py b/viscy/light/engine.py index a880920b..30395c56 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -112,7 +112,8 @@ 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: @@ -187,8 +188,8 @@ def training_step(self, batch: Sample, batch_idx: int): logger=True, 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 @@ -199,8 +200,8 @@ def validation_step(self, batch: Sample, batch_idx: int): pred = self.forward(source) loss = self.loss_function(pred, target) self.log("loss/validate", loss, 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)) ) @@ -311,11 +312,17 @@ 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 @@ -343,9 +350,12 @@ 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 = [] 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), }, )