diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
new file mode 100644
index 0000000..ab545ad
--- /dev/null
+++ b/.github/workflows/python-app.yml
@@ -0,0 +1,33 @@
+# This workflow will install Python dependencies, lint with a single version of Python
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+# pylint: disable=<warning>
+
+name: Python application
+
+on:
+  push:
+    branches: [ main,mammo,optho ]
+  pull_request:
+    branches: [ main,mammo,optho ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install toml==0.10.2 yapf==0.31.0 isort==5.9.3 pylint==2.11.0
+    - name: Lint with pylint, format with yapf and isort
+      run: |
+        pylint * --verbose -r y
+        isort . --check-only --diff
+        yapf --recursive --verbose --in-place .
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b6e4761
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/LICENSE b/LICENSE
index 8708637..37253a7 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Rajpurkar Lab / Harvard Medical AI
+Copyright (c) 2021 Alex Tamkin
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/conf/dataset/HAM10000.yaml b/conf/dataset/ham10000.yaml
similarity index 84%
rename from conf/dataset/HAM10000.yaml
rename to conf/dataset/ham10000.yaml
index cbeaf06..e930b17 100644
--- a/conf/dataset/HAM10000.yaml
+++ b/conf/dataset/ham10000.yaml
@@ -2,6 +2,6 @@
 loss: cross_entropy
 metric: auroc
 
-name: HAM10000
+name: ham10000
 batch_size: 64
 num_workers: 8
diff --git a/derm-tutorial.ipynb b/derm-tutorial.ipynb
new file mode 100644
index 0000000..c535d78
--- /dev/null
+++ b/derm-tutorial.ipynb
@@ -0,0 +1,1192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BenchMD Tutorial: Dermascopic Images\n",
+    "\n",
+    "In this tutorial, we will demonstrate the full training pipeline for generating results on the dermascopic images modality, including how to manipulate the associated config files, generate statistics on datasets, and train+test a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "import yaml\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "import torchvision.transforms as T\n",
+    "from PIL import Image\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from src.datasets.catalog import DATASET_DICT\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default config files for training are under the ``/conf`` directory, including the training config files ``./conf/pretrain.yaml`` and ``./conf/transfer.py``, as well as config files for each dataset. Below, we demonstrate the structure of the config files, and how to load a particular dermascopic dataset, ISIC 2019."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'algorithm': 'emix', 'shed_frac': 0.85, 'data_root': '/home/ubuntu/2022-spr-benchmarking/src/datasets/', 'gpus': 0, 'exp': {'base_dir': '/home/ubuntu/res/', 'name': '???'}, 'trainer': {'weights_summary': 'top', 'seed': 0, 'val_check_interval': 1.0, 'limit_val_batches': 1.0, 'resume_from_checkpoint': None, 'precision': 16, 'max_steps': 100000, 'gradient_clip_val': 0, 'shed_frac': 0.85}, 'optim': {'name': 'adam', 'lr': 0.0001, 'weight_decay': 0.0001, 'momentum': 0.9}, 'defaults': [{'dataset': 'mimic-cxr'}, {'model': 'transformer'}]}\n",
+      "Building index...\n",
+      "Done\n",
+      "Building index...\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('./conf/pretrain.yaml') as f:\n",
+    "    config= yaml.safe_load(f)\n",
+    "print(config)\n",
+    "\n",
+    "dataset = \"isic2019\"\n",
+    "\n",
+    "train_ds_kwargs = {\"base_root\": config[\"data_root\"], \"download\": True, \"train\": True}\n",
+    "val_ds_kwargs = {\"base_root\": config[\"data_root\"], \"download\": True, \"train\": False}\n",
+    "\n",
+    "dataset = DATASET_DICT[dataset]\n",
+    "train_dataset = dataset(**train_ds_kwargs)\n",
+    "val_dataset = dataset(**val_ds_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we generate summary statistics on the dataset, including the mean and standard deviation of the training (which can be used to standardize datasets) and distribution of modality labels for ISIC 2019. Note, however, that we already standardize and pad the dataset elsewhere in this repo, so this code serves mostly as a reference for transforming your new datasets. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|▏                  | 180/20265 [00:01<03:23, 98.66it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 24\u001b[0m\n\u001b[1;32m     22\u001b[0m count \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind, images, label \u001b[38;5;129;01min\u001b[39;00m tqdm(train_loader):\n\u001b[0;32m---> 24\u001b[0m     psum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mimages\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m     psum_sq \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (images\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m])\n\u001b[1;32m     26\u001b[0m     count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m images\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m images\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m images\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m3\u001b[39m]\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train_loader = DataLoader(\n",
+    "    train_dataset,\n",
+    "    batch_size=1,\n",
+    "    num_workers=8,\n",
+    "    shuffle=False,\n",
+    "    drop_last=False,\n",
+    "    pin_memory=True,\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset,\n",
+    "    batch_size=1,\n",
+    "    num_workers=8,\n",
+    "    shuffle=False,\n",
+    "    drop_last=False,\n",
+    "    pin_memory=True,\n",
+    ")\n",
+    "\n",
+    "psum = torch.tensor([0.0, 0.0, 0.0])\n",
+    "psum_sq = torch.tensor([0.0, 0.0, 0.0])\n",
+    "train_labels = []\n",
+    "val_labels = []\n",
+    "count = 0\n",
+    "for ind, images, label in tqdm(train_loader):\n",
+    "    psum += images.sum(axis=[0, 2, 3])\n",
+    "    psum_sq += (images**2).sum(axis=[0, 2, 3])\n",
+    "    count += images.shape[0] * images.shape[2] * images.shape[3]\n",
+    "    train_labels.append(label.item())\n",
+    "\n",
+    "for ind, images, label in tqdm(val_loader):\n",
+    "    val_labels.append(label.item())\n",
+    "\n",
+    "# mean and std\n",
+    "total_mean = psum / count\n",
+    "total_var = (psum_sq / count) - (total_mean**2)\n",
+    "total_std = torch.sqrt(total_var)\n",
+    "\n",
+    "# output\n",
+    "print(f'train mean: {total_mean}')\n",
+    "print(f'train std: {total_std}')\n",
+    "train_label_freq = torch.histogram(torch.tensor(train_labels, dtype=torch.float32), bins=train_dataset.NUM_CLASSES).hist\n",
+    "train_label_dist = torch.histogram(\n",
+    "    torch.tensor(train_labels, dtype=torch.float32), bins=train_dataset.NUM_CLASSES, density=True\n",
+    ").hist\n",
+    "train_label_dist = train_label_dist / train_label_dist.sum()\n",
+    "\n",
+    "val_label_freq = torch.histogram(torch.tensor(val_labels, dtype=torch.float32), bins=train_dataset.NUM_CLASSES).hist\n",
+    "val_label_dist = torch.histogram(\n",
+    "    torch.tensor(val_labels, dtype=torch.float32), bins=train_dataset.NUM_CLASSES, density=True\n",
+    ").hist\n",
+    "val_label_dist = val_label_dist / val_label_dist.sum()\n",
+    "\n",
+    "print(f'train label frequencies ({train_dataset.NUM_CLASSES} classes): {train_label_freq}')\n",
+    "print(f'train label distribution ({train_dataset.NUM_CLASSES} classes): {train_label_dist}')\n",
+    "print(f'val label frequencies ({train_dataset.NUM_CLASSES} classes): {val_label_freq}')\n",
+    "print(f'val label distribution ({train_dataset.NUM_CLASSES} classes): {val_label_dist}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example of a raw image from the training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Building index...\n",
+      "Done\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=266x200>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transform = transforms.Resize(200)\n",
+    "train_dataset.build_index()\n",
+    "fname = train_dataset.fnames[0]\n",
+    "img = transform(Image.open(fname).convert('RGB'))\n",
+    "display(img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To demonstrate the training pipeline, we will train our model for several epochs (2000 steps, when the first automatic checkpoint is saved). Below, we will export to environment variables the command line arguments for Hydra. Path variables should be modified to suit your system.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: source_ds=isic2019\n",
+      "env: pt_steps=2000\n",
+      "env: alg=emix\n",
+      "env: base_dir=/home/ubuntu/tutorial/\n",
+      "env: pt_exp_name=tutorial-derm-emix\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dataset for pretraining. The source dataset for the dermascopic images modality is ISIC2019.\n",
+    "%env source_ds=isic2019\n",
+    "\n",
+    "# Max steps to run pretraining. Our script automatically checkpoints every 2000 steps, \n",
+    "# which is roughly 5 epochs of training on ISIC2019.\n",
+    "%env pt_steps=2000\n",
+    "\n",
+    "# Algorithm for pretraining. Choose from emix, shed, and mae.\n",
+    "%env alg=emix\n",
+    "\n",
+    "# Base directory for checkpointing. You should modify this to a suitable existing directory on your system!\n",
+    "%env base_dir=/home/ubuntu/tutorial/\n",
+    "\n",
+    "#Experiment name; also the sub-directory under the base directory where checkpoints will be stored.\n",
+    "%env pt_exp_name=tutorial-derm-emix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pretrain.py:6: UserWarning: \n",
+      "The version_base parameter is not specified.\n",
+      "Please specify a compatability version level, or None.\n",
+      "Will assume defaults for version 1.1\n",
+      "  @hydra.main(config_path='conf', config_name='pretrain')\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'pretrain': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'model/transformer': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'dataset/isic2019': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.\n",
+      "  ret = run_job(\n",
+      "[2023-04-21 19:21:40,419][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkwantlin\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.15.0 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/ubuntu/2022-spr-benchmarking/outputs/2023-04-21/19-21-36/wandb/run-20230421_192141-175syo7b\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtutorial-derm-emix\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/175syo7b\u001b[0m\n",
+      "2d\n",
+      "[2023-04-21 19:21:44,464][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True\n",
+      "[2023-04-21 19:21:44,464][pytorch_lightning.utilities.distributed][INFO] - TPU available: False, using: 0 TPU cores\n",
+      "[2023-04-21 19:21:44,464][pytorch_lightning.trainer.connectors.accelerator_connector][INFO] - Using native 16bit precision.\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "20265 train examples, 5066 val examples\n",
+      "[2023-04-21 19:21:44,687][pytorch_lightning.accelerators.gpu][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+      "[2023-04-21 19:21:45,551][pytorch_lightning.core.lightning][INFO] - \n",
+      "  | Name  | Type                      | Params\n",
+      "----------------------------------------------------\n",
+      "0 | model | DomainAgnosticTransformer | 9.7 M \n",
+      "1 | ce    | CrossEntropyLoss          | 0     \n",
+      "----------------------------------------------------\n",
+      "9.7 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "9.7 M     Total params\n",
+      "38.971    Total estimated model params size (MB)\n",
+      "Validation sanity check: 0it [00:00, ?it/s]batch size 64\n",
+      "[2023-04-21 19:21:48,529][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "batch size 64\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:69: RuntimeWarning: You are using `LearningRateMonitor` callback with models that have no learning rate schedulers. Please see documentation for `configure_optimizers` method.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "Epoch 0:  80%|▊| 316/396 [00:58<00:14,  5.43it/s, loss=2.36, v_num=yo7b, val_acc\n",
+      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
+      "Validating:   0%|                                        | 0/80 [00:00<?, ?it/s]\u001b[A\n",
+      "Epoch 0:  80%|▊| 318/396 [00:59<00:14,  5.36it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
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+      "Epoch 0:  86%|▊| 342/396 [01:01<00:09,  5.55it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 0:  87%|▊| 344/396 [01:01<00:09,  5.57it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 0:  87%|▊| 346/396 [01:01<00:08,  5.60it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
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+      "Epoch 0:  90%|▉| 356/396 [01:02<00:07,  5.69it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 0:  90%|▉| 358/396 [01:03<00:06,  5.64it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 0:  91%|▉| 360/396 [01:03<00:06,  5.65it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0:  91%|▉| 362/396 [01:03<00:05,  5.67it/s, loss=2.36, v_num=yo7b, val_acc\u001b[A\n",
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+      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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+      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2:  97%|▉| 386/396 [01:08<00:01,  5.63it/s, loss=2.04, v_num=yo7b, val_acc\u001b[A\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Epoch 5:  96%|▉| 380/396 [01:06<00:02,  5.70it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  96%|▉| 382/396 [01:07<00:02,  5.64it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  97%|▉| 384/396 [01:07<00:02,  5.65it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  97%|▉| 386/396 [01:08<00:01,  5.67it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  98%|▉| 388/396 [01:08<00:01,  5.69it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  98%|▉| 390/396 [01:09<00:01,  5.63it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  99%|▉| 392/396 [01:09<00:00,  5.64it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5:  99%|▉| 394/396 [01:09<00:00,  5.66it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 5: 100%|█| 396/396 [01:09<00:00,  5.67it/s, loss=1.86, v_num=yo7b, val_acc\u001b[A\n",
+      "Epoch 6:  26%|▎| 104/396 [00:21<00:59,  4.92it/s, loss=1.89, v_num=yo7b, val_acc\u001b[A\n",
+      "Pretrained model saved to /home/ubuntu/tutorial/tutorial-derm-emix/model.ckpt\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:               epoch ▁▁▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇██\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:           train_acc ▂▂▁▃▃▃▄▄▄▆▆▆▆▆▆▆▅▅▅▆▆▅▅▅▆▅▆▆▇▆█▆▇▆▆▆▇▆▇▇\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:          train_loss █▇█▆▅▆▄▄▄▃▃▄▄▄▂▃▃▄▃▃▃▃▃▃▄▃▃▃▃▃▁▂▁▃▃▂▂▂▂▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇████\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:             val_acc ▁▃▅▆▇█\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:            val_loss █▅▃▂▂▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:               epoch 6\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:           train_acc 0.9375\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:          train_loss 1.79744\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 1999\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:             val_acc 0.86168\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:            val_loss 2.13764\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mtutorial-derm-emix\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/175syo7b\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230421_192141-175syo7b/logs\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!(python pretrain.py \\\n",
+    "  dataset=$source_ds \\\n",
+    "  trainer.max_steps=$pt_steps \\\n",
+    "  algorithm=$alg \\\n",
+    "  exp.base_dir=$base_dir \\\n",
+    "  exp.name=$pt_exp_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should now have a pretraining checkpoint named ``epoch6-step1999.ckpt`` stored under the specified ``base_dir/pt_exp_name``, which can now be used for transfer learning (still on the source dataset, ISIC2019) on the 5-class task we have defined for the dermascopic images modality. First, we will run linear evaluation for 1 epoch on the pretrained checkpoint, using all labels in the source dataset.\n",
+    "\n",
+    "Below, we will export to environment variables the command line arguments for Hydra. We encourage you to experiment with these variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: transfer_epochs=1\n",
+      "env: lineval_exp_name=tutorial-derm-emix-lineval\n",
+      "env: pt_ckpt=/home/ubuntu/tutorial/tutorial-derm-emix/epoch6-step1999.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Max epochs to run pretraining. Our script automatically checkpoints every 2000 steps, \n",
+    "# which is roughly 5 epochs of training on ISIC2019.\n",
+    "%env transfer_epochs=1\n",
+    "\n",
+    "# Experiment name; also the sub-directory under the base directory where checkpoints will be stored.\n",
+    "%env lineval_exp_name=tutorial-derm-emix-lineval\n",
+    "\n",
+    "# Pretraining checkpoint location. You should modify this to match the path on your system.\n",
+    "%env pt_ckpt=/home/ubuntu/tutorial/tutorial-derm-emix/epoch6-step1999.ckpt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'transfer': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'model/transformer': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'dataset/isic2019': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.\n",
+      "  ret = run_job(\n",
+      "[2023-04-21 19:37:20,647][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkwantlin\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.15.0 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/ubuntu/2022-spr-benchmarking/outputs/2023-04-21/19-37-17/wandb/run-20230421_193721-1t655bdh\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtutorial-derm-emix-lineval\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/1t655bdh\u001b[0m\n",
+      "[2023-04-21 19:37:24,617][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True\n",
+      "[2023-04-21 19:37:24,617][pytorch_lightning.utilities.distributed][INFO] - TPU available: False, using: 0 TPU cores\n",
+      "[2023-04-21 19:37:24,617][pytorch_lightning.trainer.connectors.accelerator_connector][INFO] - Using native 16bit precision.\n",
+      "2d\n",
+      "Starting Linear Evaluation.\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "20265 train examples, 5066 val examples\n",
+      "[2023-04-21 19:37:25,853][pytorch_lightning.accelerators.gpu][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+      "[2023-04-21 19:37:25,867][pytorch_lightning.core.lightning][INFO] - \n",
+      "  | Name      | Type                      | Params\n",
+      "--------------------------------------------------------\n",
+      "0 | model     | DomainAgnosticTransformer | 9.7 M \n",
+      "1 | linear    | Linear                    | 1.3 K \n",
+      "2 | metric_fn | AUROC                     | 0     \n",
+      "--------------------------------------------------------\n",
+      "1.3 K     Trainable params\n",
+      "9.7 M     Non-trainable params\n",
+      "9.7 M     Total params\n",
+      "38.976    Total estimated model params size (MB)\n",
+      "Validation sanity check: 0it [00:00, ?it/s]batch size 64\n",
+      "Validation sanity check:  50%|██████████          | 1/2 [00:02<00:02,  2.20s/it]/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: No positive samples in targets, true positive value should be meaningless. Returning zero tensor in true positive score\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/pytorch_lightning/core/step_result.py:115: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  value = torch.tensor(value, device=device, dtype=torch.float)\n",
+      "[2023-04-21 19:37:28,844][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "batch size 64\n",
+      "Epoch 0:  80%|▊| 316/396 [00:47<00:11,  6.68it/s, loss=1.16, v_num=5bdh, transfe\n",
+      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
+      "Validating:   0%|                                        | 0/80 [00:00<?, ?it/s]\u001b[A\n",
+      "Epoch 0:  81%|▊| 319/396 [00:48<00:11,  6.58it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  82%|▊| 323/396 [00:48<00:10,  6.64it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Validating:   9%|██▊                             | 7/80 [00:01<00:09,  7.89it/s]\u001b[A\n",
+      "Epoch 0:  83%|▊| 327/396 [00:49<00:10,  6.63it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  84%|▊| 331/396 [00:49<00:09,  6.69it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  85%|▊| 335/396 [00:50<00:09,  6.70it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  86%|▊| 339/396 [00:50<00:08,  6.76it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  87%|▊| 343/396 [00:50<00:07,  6.77it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  88%|▉| 347/396 [00:50<00:07,  6.83it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  89%|▉| 351/396 [00:51<00:06,  6.83it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  90%|▉| 355/396 [00:51<00:05,  6.90it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  91%|▉| 359/396 [00:52<00:05,  6.80it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Validating:  54%|████████████████▋              | 43/80 [00:05<00:05,  7.29it/s]\u001b[A\n",
+      "Epoch 0:  92%|▉| 363/396 [00:52<00:04,  6.86it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  93%|▉| 367/396 [00:54<00:04,  6.75it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Validating:  64%|███████████████████▊           | 51/80 [00:07<00:05,  5.52it/s]\u001b[A\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0:  94%|▉| 371/396 [00:54<00:03,  6.81it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Validating:  70%|█████████████████████▋         | 56/80 [00:07<00:02,  8.66it/s]\u001b[A\n",
+      "Epoch 0:  95%|▉| 375/396 [00:55<00:03,  6.71it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  96%|▉| 379/396 [00:56<00:02,  6.75it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Validating:  80%|████████████████████████▊      | 64/80 [00:08<00:02,  7.31it/s]\u001b[A\n",
+      "Epoch 0:  97%|▉| 383/396 [00:57<00:01,  6.66it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0:  98%|▉| 387/396 [00:57<00:01,  6.71it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Validating:  90%|███████████████████████████▉   | 72/80 [00:10<00:01,  6.87it/s]\u001b[A\n",
+      "Epoch 0:  99%|▉| 391/396 [00:59<00:00,  6.62it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0: 100%|█| 396/396 [00:59<00:00,  6.67it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Epoch 0: 100%|█| 396/396 [00:59<00:00,  6.66it/s, loss=1.16, v_num=5bdh, transfe\u001b[A\n",
+      "Pretrained model saved to /home/ubuntu/tutorial/tutorial-derm-emix-lineval/model.ckpt\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:                 epoch ▁▁▁▁▁▁▁▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   trainer/global_step ▁▂▄▅▆███\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   transfer/train_loss ██▆▄▃▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: transfer/train_metric ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:     transfer/val_loss ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   transfer/val_metric ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:                 epoch 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   trainer/global_step 315\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   transfer/train_loss 1.07739\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: transfer/train_metric 0.63564\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:     transfer/val_loss 1.13542\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   transfer/val_metric 0.72116\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mtutorial-derm-emix-lineval\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/1t655bdh\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230421_193721-1t655bdh/logs\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!(python transfer.py \\\n",
+    "  dataset=$source_ds \\\n",
+    "  trainer.max_epochs=$transfer_epochs \\\n",
+    "  exp.base_dir=$base_dir \\\n",
+    "  exp.name=$lineval_exp_name \\\n",
+    "  ckpt=$pt_ckpt)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can also perform finetuning (again, for 1 epoch) on the pretrained model by passing in an addition argument, ``finetune_size``, which we can set to either ``small`` (8 labels/class), ``medium`` (64 labels/class), ``large`` (256 labels/class), or ``full`` (all labels), depending on how many labels we want to use for finetuning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: f_size=small\n",
+      "env: finetune_exp_name=tutorial-derm-emix-finetune\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finetune size\n",
+    "%env f_size=small\n",
+    "\n",
+    "# Experiment name; also the sub-directory under the base directory where checkpoints will be stored.\n",
+    "%env finetune_exp_name=tutorial-derm-emix-finetune"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'transfer': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'model/transformer': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'dataset/isic2019': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.\n",
+      "  ret = run_job(\n",
+      "[2023-04-21 19:39:05,168][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkwantlin\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.15.0 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/ubuntu/2022-spr-benchmarking/outputs/2023-04-21/19-39-01/wandb/run-20230421_193906-21a6l29h\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtutorial-derm-emix-lineval\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/21a6l29h\u001b[0m\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: Checkpoint directory /home/ubuntu/tutorial/tutorial-derm-emix-lineval exists and is not empty.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "[2023-04-21 19:39:09,140][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True\n",
+      "[2023-04-21 19:39:09,140][pytorch_lightning.utilities.distributed][INFO] - TPU available: False, using: 0 TPU cores\n",
+      "[2023-04-21 19:39:09,140][pytorch_lightning.trainer.connectors.accelerator_connector][INFO] - Using native 16bit precision.\n",
+      "2d\n",
+      "Starting Finetuning.\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/isic2019.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "Resetting batch size to 40 since finetune_size * num_classes < batch_size\n",
+      "40 train examples, 5066 val examples\n",
+      "[2023-04-21 19:39:10,371][pytorch_lightning.accelerators.gpu][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+      "[2023-04-21 19:39:10,386][pytorch_lightning.core.lightning][INFO] - \n",
+      "  | Name      | Type                      | Params\n",
+      "--------------------------------------------------------\n",
+      "0 | model     | DomainAgnosticTransformer | 9.7 M \n",
+      "1 | linear    | Linear                    | 1.3 K \n",
+      "2 | metric_fn | AUROC                     | 0     \n",
+      "--------------------------------------------------------\n",
+      "9.7 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "9.7 M     Total params\n",
+      "38.976    Total estimated model params size (MB)\n",
+      "Validation sanity check: 0it [00:00, ?it/s]batch size 40\n",
+      "Validation sanity check:  50%|██████████          | 1/2 [00:01<00:01,  1.69s/it]/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: No positive samples in targets, true positive value should be meaningless. Returning zero tensor in true positive score\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/pytorch_lightning/core/step_result.py:115: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  value = torch.tensor(value, device=device, dtype=torch.float)\n",
+      "[2023-04-21 19:39:12,569][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "batch size 40\n",
+      "Epoch 0:   1%| | 1/128 [00:01<02:24,  1.14s/it, loss=1.9, v_num=l29h, transfer/v\n",
+      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
+      "Validating:   0%|                                       | 0/127 [00:00<?, ?it/s]\u001b[A\n",
+      "Epoch 0:   2%| | 3/128 [00:01<01:18,  1.60it/s, loss=1.9, v_num=l29h, transfer/v\u001b[A\n",
+      "Epoch 0:   4%| | 5/128 [00:02<00:49,  2.49it/s, loss=1.9, v_num=l29h, transfer/v\u001b[A\n",
+      "Epoch 0:   8%| | 10/128 [00:02<00:24,  4.74it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  12%| | 15/128 [00:02<00:19,  5.71it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  12%|███▌                          | 15/127 [00:01<00:07, 14.42it/s]\u001b[A\n",
+      "Epoch 0:  16%|▏| 20/128 [00:02<00:16,  6.72it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  20%|▏| 25/128 [00:03<00:12,  8.07it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  20%|█████▉                        | 25/127 [00:02<00:06, 15.64it/s]\u001b[A\n",
+      "Epoch 0:  23%|▏| 30/128 [00:03<00:11,  8.65it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  27%|▎| 35/128 [00:03<00:09,  9.40it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  31%|▎| 40/128 [00:03<00:08, 10.17it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  32%|█████████▋                    | 41/127 [00:03<00:04, 18.15it/s]\u001b[A\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0:  35%|▎| 45/128 [00:04<00:07, 10.50it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  39%|▍| 50/128 [00:04<00:06, 11.34it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  39%|███████████▊                  | 50/127 [00:03<00:04, 17.26it/s]\u001b[A\n",
+      "Epoch 0:  43%|▍| 55/128 [00:04<00:06, 11.53it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  47%|▍| 60/128 [00:05<00:05, 11.80it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  51%|▌| 65/128 [00:05<00:05, 12.37it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  51%|███████████████▎              | 65/127 [00:04<00:04, 14.73it/s]\u001b[A\n",
+      "Epoch 0:  55%|▌| 70/128 [00:06<00:05, 11.34it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  59%|▌| 75/128 [00:06<00:04, 11.37it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  62%|▋| 80/128 [00:07<00:04, 11.07it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  62%|██████████████████▋           | 79/127 [00:06<00:04, 10.48it/s]\u001b[A\n",
+      "Epoch 0:  66%|▋| 85/128 [00:08<00:04, 10.49it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  70%|▋| 90/128 [00:08<00:03, 10.89it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Validating:  70%|█████████████████████         | 89/127 [00:07<00:04,  9.31it/s]\u001b[A\n",
+      "Validating:  72%|█████████████████████▍        | 91/127 [00:07<00:04,  7.40it/s]\u001b[A\n",
+      "Epoch 0:  74%|▋| 95/128 [00:09<00:03, 10.32it/s, loss=1.9, v_num=l29h, transfer/\u001b[A\n",
+      "Epoch 0:  78%|▊| 100/128 [00:10<00:02,  9.99it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Validating:  78%|███████████████████████▍      | 99/127 [00:08<00:03,  7.16it/s]\u001b[A\n",
+      "Epoch 0:  82%|▊| 105/128 [00:10<00:02, 10.27it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Validating:  83%|███████████████████████▉     | 105/127 [00:09<00:02,  8.72it/s]\u001b[A\n",
+      "Epoch 0:  86%|▊| 110/128 [00:11<00:01,  9.87it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Validating:  87%|█████████████████████████    | 110/127 [00:10<00:01,  9.22it/s]\u001b[A\n",
+      "Epoch 0:  90%|▉| 115/128 [00:11<00:01,  9.93it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Validating:  91%|██████████████████████████▎  | 115/127 [00:10<00:01,  6.94it/s]\u001b[A\n",
+      "Epoch 0:  94%|▉| 120/128 [00:12<00:00,  9.85it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Validating:  95%|███████████████████████████▋ | 121/127 [00:11<00:00,  8.63it/s]\u001b[A\n",
+      "Epoch 0:  98%|▉| 125/128 [00:13<00:00,  9.60it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Epoch 0: 100%|█| 128/128 [00:13<00:00,  9.64it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Epoch 0: 100%|█| 128/128 [00:13<00:00,  9.51it/s, loss=1.9, v_num=l29h, transfer\u001b[A\n",
+      "Pretrained model saved to /home/ubuntu/tutorial/tutorial-derm-emix-lineval/model.ckpt\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \\ 0.018 MB of 0.018 MB uploaded (0.000 MB deduped)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:                 epoch ▁▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   trainer/global_step ▁▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: transfer/train_metric ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:     transfer/val_loss ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   transfer/val_metric ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:                 epoch 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   trainer/global_step 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: transfer/train_metric 0.57109\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:     transfer/val_loss 1.85559\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   transfer/val_metric 0.5419\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mtutorial-derm-emix-lineval\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/21a6l29h\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230421_193906-21a6l29h/logs\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!(python transfer.py \\\n",
+    "  dataset=$source_ds \\\n",
+    "  trainer.max_epochs=$transfer_epochs \\\n",
+    "  finetune_size=$f_size \\\n",
+    "  exp.base_dir=$base_dir \\\n",
+    "  exp.name=$finetune_exp_name \\\n",
+    "  ckpt=$pt_ckpt)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There should be transfer learning checkpoints stored under the specified experiment names, which can now be used for evaluation on the target datasets, HAM10000 and PAD-UFES-20. Run testing by passing in an additional argument ``test=True`` and specifying the transfer learning checkpoint you'd like to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: ckpt_to_test=/home/ubuntu/tutorial/tutorial-derm-emix-lineval/best_model_epoch0.ckpt\n",
+      "env: test1=ham10000\n",
+      "env: test2=pad_ufes_20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pretraining checkpoint location. You should modify this to select the checkpoint you'd like to use on your system.\n",
+    "%env ckpt_to_test=/home/ubuntu/tutorial/tutorial-derm-emix-lineval/best_model_epoch0.ckpt\n",
+    "\n",
+    "# Test dataset\n",
+    "%env test1=ham10000\n",
+    "\n",
+    "# Test dataset\n",
+    "%env test2=pad_ufes_20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'transfer': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'model/transformer': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/core/default_element.py:124: UserWarning: In 'dataset/ham10000': Usage of deprecated keyword in package header '# @package _group_'.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information\n",
+      "  deprecation_warning(\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.\n",
+      "See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.\n",
+      "  ret = run_job(\n",
+      "[2023-04-21 19:50:03,600][pytorch_lightning.utilities.seed][INFO] - Global seed set to 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkwantlin\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.15.0 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/ubuntu/2022-spr-benchmarking/outputs/2023-04-21/19-50-00/wandb/run-20230421_195004-3ug8a00u\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtutorial-test-derm\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/3ug8a00u\u001b[0m\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: Checkpoint directory /home/ubuntu/tutorial/tutorial-test-derm exists and is not empty.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "[2023-04-21 19:50:07,856][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True\n",
+      "[2023-04-21 19:50:07,856][pytorch_lightning.utilities.distributed][INFO] - TPU available: False, using: 0 TPU cores\n",
+      "[2023-04-21 19:50:07,856][pytorch_lightning.trainer.connectors.accelerator_connector][INFO] - Using native 16bit precision.\n",
+      "2d\n",
+      "Starting Linear Evaluation.\n",
+      "/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "Building index...\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "/home/ubuntu/2022-spr-benchmarking/src/datasets/derm/HAM10000.py:112: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  index = index.append(g)\n",
+      "Done\n",
+      "8012 train examples, 2003 val examples\n",
+      "[2023-04-21 19:50:08,899][pytorch_lightning.accelerators.gpu][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+      "batch size 64\n",
+      "Testing:  91%|██████████████████████████████▊   | 29/32 [00:03<00:00, 11.71it/s]/home/ubuntu/.conda/envs/dabs2/lib/python3.8/site-packages/pytorch_lightning/core/step_result.py:115: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  value = torch.tensor(value, device=device, dtype=torch.float)\n",
+      "Testing: 100%|██████████████████████████████████| 32/32 [00:03<00:00,  8.82it/s]\n",
+      "--------------------------------------------------------------------------------\n",
+      "DATALOADER:0 TEST RESULTS\n",
+      "{'test_loss': 1.0278538465499878, 'test_metric': 0.6922151446342468}\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:               epoch ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:           test_loss ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:         test_metric ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:               epoch 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:           test_loss 1.02785\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:         test_metric 0.69222\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mtutorial-test-derm\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/kwantlin/domain-agnostic/runs/3ug8a00u\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230421_195004-3ug8a00u/logs\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!(python transfer.py \\\n",
+    "  test=True \\\n",
+    "  dataset=$test_ds_1 \\\n",
+    "  exp.base_dir=$base_dir \\\n",
+    "  exp.name=\"tutorial-test-derm\" \\\n",
+    "  ckpt=$ckpt_to_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
index 606445e..183f08e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,3 +26,4 @@ h5py
 pylidc
 bs4
 mne
+SimpleITK
diff --git a/src/datasets/catalog.py b/src/datasets/catalog.py
index 045b5f3..b70160d 100644
--- a/src/datasets/catalog.py
+++ b/src/datasets/catalog.py
@@ -33,7 +33,7 @@
     'isic2019': isic2019.ISIC2019,
     'isic2020': isic2020.ISIC2020,
     'ddi': ddi.ddi,
-    'HAM10000': HAM10000.HAM10000,
+    'ham10000': HAM10000.HAM10000,
     'pad_ufes_20': pad_ufes_20.pad_ufes_20,
     
     # ECG
@@ -63,7 +63,7 @@
     'mimic-cxr',
     #dermatology.
     'ddi',
-    'HAM10000',
+    'ham10000',
     'isic2020',
     'pad_ufes_20',
     'isic2019',
diff --git a/src/datasets/ct/lndb_utils.py b/src/datasets/ct/lndb_utils.py
new file mode 100644
index 0000000..be0a048
--- /dev/null
+++ b/src/datasets/ct/lndb_utils.py
@@ -0,0 +1,93 @@
+import csv
+
+import numpy as np
+import SimpleITK as sitk
+from scipy.ndimage import zoom
+
+
+def readCsv(csvfname):
+    # read csv to list of lists
+    with open(csvfname, 'r') as csvf:
+        reader = csv.reader(csvf)
+        csvlines = list(reader)
+    return csvlines
+
+
+def writeCsv(csfname, rows):
+    # write csv from list of lists
+    with open(csfname, 'w', newline='') as csvf:
+        filewriter = csv.writer(csvf)
+        filewriter.writerows(rows)
+
+
+def readMhd(filename):
+    # read mhd/raw image
+    itkimage = sitk.ReadImage(filename)
+    scan = sitk.GetArrayFromImage(itkimage)  #3D image
+    #  print(scan.shape)
+
+    spacing = itkimage.GetSpacing()  #voxelsize
+    origin = itkimage.GetOrigin()  #world coordinates of origin
+    transfmat = itkimage.GetDirection()  #3D rotation matrix
+    #  print(dir(itkimage))
+    return scan, spacing, origin, transfmat
+
+
+def writeMhd(filename, scan, spacing, origin, transfmat):
+    # write mhd/raw image
+    itkim = sitk.GetImageFromArray(scan, isVector=False)  #3D image
+    itkim.SetSpacing(spacing)  #voxelsize
+    itkim.SetOrigin(origin)  #world coordinates of origin
+    itkim.SetDirection(transfmat)  #3D rotation matrix
+    sitk.WriteImage(itkim, filename, False)
+
+
+def getImgWorldTransfMats(spacing, transfmat):
+    # calc image to world to image transformation matrixes
+    transfmat = np.array([transfmat[0:3], transfmat[3:6], transfmat[6:9]])
+    for d in range(3):
+        transfmat[0:3, d] = transfmat[0:3, d] * spacing[d]
+    transfmat_toworld = transfmat  #image to world coordinates conversion matrix
+    transfmat_toimg = np.linalg.inv(transfmat)  #world to image coordinates conversion matrix
+
+    return transfmat_toimg, transfmat_toworld
+
+
+def convertToImgCoord(xyz, origin, transfmat_toimg):
+    # convert world to image coordinates
+    xyz = xyz - origin
+    xyz = np.round(np.matmul(transfmat_toimg, xyz))
+    return xyz
+
+
+def convertToWorldCoord(xyz, origin, transfmat_toworld):
+    # convert image to world coordinates
+    xyz = np.matmul(transfmat_toworld, xyz)
+    xyz = xyz + origin
+    return xyz
+
+
+def extractCube(scan, spacing, xyz, cube_size=80, cube_size_mm=51):
+    # Extract cube of cube_size^3 voxels and world dimensions of cube_size_mm^3 mm from scan at image coordinates xyz
+    xyz = np.array([xyz[i] for i in [2, 1, 0]], np.int)
+    spacing = np.array([spacing[i] for i in [2, 1, 0]])
+    scan_halfcube_size = np.array(cube_size_mm / spacing / 2, np.int)
+    if np.any(xyz < scan_halfcube_size) or np.any(xyz + scan_halfcube_size > scan.shape):  # check if padding is necessary
+        maxsize = max(scan_halfcube_size)
+        scan = np.pad(
+            scan, ((
+                maxsize,
+                maxsize,
+            )), 'constant', constant_values=0
+        )
+        xyz = xyz + maxsize
+
+    scancube = scan[xyz[0] - scan_halfcube_size[0]:xyz[0] + scan_halfcube_size[0],  # extract cube from scan at xyz
+                    xyz[1] - scan_halfcube_size[1]:xyz[1] + scan_halfcube_size[1],
+                    xyz[2] - scan_halfcube_size[2]:xyz[2] + scan_halfcube_size[2]]
+
+    sh = scancube.shape
+
+    scancube = zoom(scancube, (cube_size / sh[0], cube_size / sh[1], cube_size / sh[2]), order=2)  #resample for cube_size
+
+    return scancube
\ No newline at end of file
diff --git a/src/datasets/ct/readNoduleList.py b/src/datasets/ct/readNoduleList.py
new file mode 100644
index 0000000..78a1eae
--- /dev/null
+++ b/src/datasets/ct/readNoduleList.py
@@ -0,0 +1,107 @@
+import math
+import os
+
+import numpy as np
+
+from src.datasets.ct.lndb_utils import *
+
+
+def nodEqDiam(vol):
+    # Calc nodule equivalent diameter from volume vol
+    return 2 * (vol * 3 / (4 * math.pi))**(1 / 3)
+
+
+def joinNodules(nodules, verb=False):
+    # join nodules from different radiologists (if within radiusor 3mm from each other)
+    header = nodules[0]
+    lines = nodules[1:]
+    lndind = header.index('LNDbID')
+    radind = header.index('RadID')
+    fndind = header.index('FindingID')
+    xind = header.index('x')
+    yind = header.index('y')
+    zind = header.index('z')
+    nodind = header.index('Nodule')
+    volind = header.index('Volume')
+    texind = header.index('Text')
+    LND = [int(line[lndind]) for line in lines]
+
+    # Match nodules
+    nodules = [['LNDbID', 'RadID', 'RadFindingID', 'FindingID', 'x', 'y', 'z', 'AgrLevel', 'Nodule', 'Volume', 'Text']]
+    for lndU in np.unique(LND):  #within each CT
+        nodlnd = [line for lnd, line in zip(LND, lines) if lnd == lndU]
+        dlnd = [nodEqDiam(float(n[volind])) for n in nodlnd]
+        nodnew = []  #create merged nodule list
+        dnew = []
+        for iself, n in enumerate(nodlnd):  #for each nodule
+            dself = dlnd[iself] / 2
+            rself = n[radind]
+            nodself = n[nodind]
+            match = False
+            for inew, nnew in enumerate(nodnew):  #check distance with every nodule in merged list
+                if not float(rself) in nnew[radind] and float(nodself) in nnew[nodind]:
+                    dother = max(dnew[inew]) / 2
+                    dist = (
+                        (float(n[xind]) - np.mean(nnew[xind]))**2 + (float(n[yind]) - np.mean(nnew[yind]))**2 +
+                        (float(n[zind]) - np.mean(nnew[zind]))**2
+                    )**.5
+                    if dist < max(max(dself, dother), 3):  # if distance between nodules is smaller than maximum radius or 3mm
+                        match = True
+                        for f in range(len(n)):
+                            nnew[f].append(float(n[f]))  #append to existing nodule in merged list
+                        dnew[inew].append(np.mean([nodEqDiam(v) for v in nodnew[inew][volind]]))
+                        break
+            if not match:
+                nodnew.append([[float(l)] for l in n])  #otherwise append new nodule to merged list
+                dnew.append([np.mean([nodEqDiam(v) for v in nodnew[-1][volind]])])
+
+            if verb:
+                print(iself)
+                for inew, nnew in enumerate(nodnew):
+                    print(nnew, dnew[inew])
+
+        # Merge matched nodules
+        for ind, n in enumerate(nodnew):
+            agrlvl = n[nodind].count(1.0)
+            if agrlvl > 0:  #nodules
+                nod = 1
+                vol = np.mean(n[volind])  #volume is the average of all radiologists
+                tex = np.mean(n[texind])  #texture is the average of all radiologists
+            else:  #non-nodules
+                nod = 0
+                vol = 4 * math.pi * 1.5**3 / 3  #volume is the minimum for equivalent radius 3mm
+                tex = 0
+                agrlvl = 3  #making it convenient for label generation
+            nodules.append(
+                [
+                    int(n[lndind][0]),
+                    ','.join([str(int(r)) for r in n[radind]]),  #list radiologist IDs
+                    ','.join([str(int(f)) for f in n[fndind]]),  #list radiologist finding's IDs
+                    ind + 1,  # new finding ID
+                    np.mean(n[xind]),  #centroid is the average of centroids
+                    np.mean(n[yind]),
+                    np.mean(n[zind]),
+                    agrlvl,  # number of radiologists that annotated the finding (0 if non-nodule)
+                    nod,
+                    vol,
+                    tex
+                ]
+            )
+    if verb:
+        for n in nodules:
+            print(n)
+    return nodules
+
+
+def get_merged_nodules(root: str):
+    # Merge nodules from train set
+
+    prefix = 'train'
+    fname_gtNodulesFleischner = os.path.join(root, '{}Nodules.csv'.format(prefix))
+    gtNodules = readCsv(fname_gtNodulesFleischner)
+    for line in gtNodules:
+        print(line)
+    gtNodules = joinNodules(gtNodules)
+    path_out = os.path.join(root, '{}Nodules_gt.csv'.format(prefix))
+    writeCsv(path_out, gtNodules)  #write to csv
+    return path_out
\ No newline at end of file