diff --git "a/recon_inference-multisession_union_mask_sdxl_turbo.ipynb" "b/recon_inference-multisession_union_mask_sdxl_turbo.ipynb"
new file mode 100644--- /dev/null
+++ "b/recon_inference-multisession_union_mask_sdxl_turbo.ipynb"
@@ -0,0 +1,5648 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f16c9d4c-66cb-4692-a61d-9aa86a8765d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "importing modules\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/rt_sdxlturbo/lib/python3.11/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers\n",
+      "  warnings.warn(f\"Importing from {__name__} is deprecated, please import via timm.layers\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"importing modules\")\n",
+    "import os\n",
+    "import sys\n",
+    "import json\n",
+    "import argparse\n",
+    "import numpy as np\n",
+    "import time\n",
+    "import random\n",
+    "import string\n",
+    "import h5py\n",
+    "from tqdm import tqdm\n",
+    "import webdataset as wds\n",
+    "from PIL import Image\n",
+    "import pandas as pd\n",
+    "import nibabel as nib\n",
+    "import nilearn\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "# tf32 data type is faster than standard float32\n",
+    "torch.backends.cuda.matmul.allow_tf32 = True\n",
+    "\n",
+    "import utils\n",
+    "from utils import load_preprocess_betas, resample, applyxfm, apply_thresh, resample_betas\n",
+    "\n",
+    "# this block imports utils from mindeye_preproc as \"preproc\"\n",
+    "import importlib.util\n",
+    "parent_utils_path =  \"/home/ubuntu/mindeye_preproc/analysis/utils.py\" # \"/home/ri4541/mindeye_preproc/analysis/utils.py\" \n",
+    "spec = importlib.util.spec_from_file_location(\"utils\", parent_utils_path)\n",
+    "preproc = importlib.util.module_from_spec(spec)\n",
+    "parent_dir = os.path.dirname(parent_utils_path)\n",
+    "if parent_dir not in sys.path:\n",
+    "    sys.path.append(parent_dir)\n",
+    "spec.loader.exec_module(preproc)\n",
+    "\n",
+    "if utils.is_interactive():\n",
+    "    from IPython.display import clear_output # function to clear print outputs in cell\n",
+    "    %load_ext autoreload \n",
+    "    # this allows you to change functions in models.py or utils.py and have this notebook automatically update with your revisions\n",
+    "    %autoreload 2 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "33a4a539-7c94-4447-b3a4-9208c6af7920",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "no module 'xformers'. Processing without...\n",
+      "no module 'xformers'. Processing without...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LOCAL RANK  0\n",
+      "device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from accelerate import Accelerator, DeepSpeedPlugin\n",
+    "from generative_models.sgm.models.diffusion import DiffusionEngine\n",
+    "from omegaconf import OmegaConf\n",
+    "\n",
+    "import os\n",
+    "### Multi-GPU config ###\n",
+    "local_rank = os.getenv('RANK')\n",
+    "if local_rank is None: \n",
+    "    local_rank = 0\n",
+    "else:\n",
+    "    local_rank = int(local_rank)\n",
+    "print(\"LOCAL RANK \", local_rank)  \n",
+    "\n",
+    "accelerator = Accelerator(split_batches=False, mixed_precision=\"fp16\")\n",
+    "device = accelerator.device\n",
+    "print(\"device:\",device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d2d8de1-d0ca-4b5f-84d8-2560f0399a5a",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c47b5b-869f-468c-bb93-43610ee5dbe0",
+   "metadata": {},
+   "source": [
+    "## New Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "69037852-cdbd-4eac-a720-3fca5dc48a61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if utils.is_interactive():\n",
+    "    sub = \"sub-005\"\n",
+    "    session = \"all\"\n",
+    "    task = 'C'  # 'study' or 'A'; used to search for functional run in bids format\n",
+    "else:\n",
+    "    sub = os.environ[\"sub\"]\n",
+    "    session = os.environ[\"session\"]\n",
+    "    task = os.environ[\"task\"]\n",
+    "\n",
+    "if session == \"all\":\n",
+    "    ses_list = [\"ses-01\", \"ses-02\"]  # list of actual session IDs\n",
+    "    design_ses_list = [\"ses-01\", \"ses-02\"]  # list of session IDs to search for design matrix\n",
+    "else:\n",
+    "    ses_list = [session]\n",
+    "    design_ses_list = [session]\n",
+    "    \n",
+    "task_name = f\"_task-{task}\" if task != 'study' else ''\n",
+    "resample_voxel_size = False\n",
+    "resample_post_glmsingle = False  # do you want to do voxel resampling here? if resample_voxel_size = True and resample_post_glmsingle = False, assume the resampling has been done prior to GLMsingle, so just use resampled directory but otherwise proceed as normal\n",
+    "load_from_resampled_file = False  # do you want to load resampled data from file? if True, assume resampling was done in this notebook before, and that we're not using the GLMsingle resampled data\n",
+    "    \n",
+    "train_test_split = 'MST' # 'MST', 'orig', 'unique'\n",
+    "remove_close_to_MST = False\n",
+    "remove_random_n = False\n",
+    "\n",
+    "if remove_close_to_MST or remove_random_n:\n",
+    "    assert remove_close_to_MST != remove_random_n  # don't remove both sets of images\n",
+    "\n",
+    "n_to_remove = 0\n",
+    "if remove_random_n:\n",
+    "    assert train_test_split == 'MST'  # MST images are excluded from the n images removed, so only makes sense if they're not in the training set\n",
+    "    n_to_remove = 150\n",
+    "    \n",
+    "if resample_voxel_size:\n",
+    "    # voxel size was unchanged in glmsingle, want to perform resampling here\n",
+    "    resampled_vox_size = 2.5\n",
+    "    resample_method = \"sinc\"  # {trilinear,nearestneighbour,sinc,spline}, credit: https://johnmuschelli.com/fslr/reference/flirt.help.html\n",
+    "    \n",
+    "    # file name helper variables\n",
+    "    vox_dim_str = str(resampled_vox_size).replace('.', '_')  # in case the voxel size has a decimal, replace with an underscore\n",
+    "    resampled_suffix = f\"resampled_{vox_dim_str}mm_{resample_method}\"\n",
+    "    mask_resampled_suffix = resampled_suffix\n",
+    "    if resample_post_glmsingle:\n",
+    "        resampled_suffix += '_postglmsingle'\n",
+    "    else:\n",
+    "        resampled_suffix += '_preglmsingle'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2ece766e-4272-4ca3-81e9-9ea5dccd2279",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "session label: ses-01-02\n"
+     ]
+    }
+   ],
+   "source": [
+    "session_label = preproc.get_session_label(ses_list)\n",
+    "print('session label:', session_label)\n",
+    "n_runs, _ = preproc.get_runs_per_session(sub, session, ses_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e52985b1-95ff-487b-8b2d-cc1ad1c190b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model_name: testing_MST_ViT-H_1024_scalf\n",
+      "glmsingle_path: /home/ubuntu/glmsingle/glmsingle_sub-005_ses-01-02_task-C\n",
+      "glmsingle path exists!\n",
+      "--data_path=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2                     --glmsingle_path=/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01-02_task-C                     --model_name=testing_MST_ViT-H_1024_scalf --subj=1                     --no-blurry_recon --use_prior                     --hidden_dim=1024 --n_blocks=4\n",
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "# if running this interactively, can specify jupyter_args here for argparser to use\n",
+    "if utils.is_interactive():\n",
+    "    # model_name=f\"{sub}_{session}_task-{task}_bs24_MST_rishab_{train_test_split}split\"\n",
+    "    model_name = \"testing_MST_ViT-H_1024_scalf\"\n",
+    "    print(\"model_name:\", model_name)\n",
+    "    glmsingle_path = f\"/home/ubuntu/glmsingle/glmsingle_{sub}_{session_label}_task-{task}\" # f\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_{sub}_{session_label}_task-{task}\"\n",
+    "    print(\"glmsingle_path:\", glmsingle_path)\n",
+    "    assert os.path.exists(glmsingle_path)\n",
+    "    print(\"glmsingle path exists!\")\n",
+    "    # global_batch_size and batch_size should already be defined in the above cells\n",
+    "    # other variables can be specified in the following string:\n",
+    "    jupyter_args = f\"--data_path=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2 \\\n",
+    "                    --glmsingle_path={glmsingle_path} \\\n",
+    "                    --model_name={model_name} --subj=1 \\\n",
+    "                    --no-blurry_recon --use_prior \\\n",
+    "                    --hidden_dim=1024 --n_blocks=4\"\n",
+    "    \n",
+    "    print(jupyter_args)\n",
+    "    jupyter_args = jupyter_args.split()\n",
+    "    \n",
+    "    from IPython.display import clear_output # function to clear print outputs in cell\n",
+    "    %load_ext autoreload \n",
+    "    # this allows you to change functions in models.py or utils.py and have this notebook automatically update with your revisions\n",
+    "    %autoreload 2 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "49e5dae4-606d-4dc6-b420-df9e4c14737e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "parser = argparse.ArgumentParser(description=\"Model Training Configuration\")\n",
+    "parser.add_argument(\n",
+    "    \"--model_name\", type=str, default=\"testing\",\n",
+    "    help=\"will load ckpt for model found in ../train_logs/model_name\",\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--data_path\", type=str, default=\"/weka/proj-fmri/shared/mindeyev2_dataset\",\n",
+    "    help=\"Path to where NSD data is stored / where to download it to\",\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--subj\",type=int, default=1, choices=[1,2,3,4,5,6,7,8],\n",
+    "    help=\"Validate on which subject?\",\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--blurry_recon\",action=argparse.BooleanOptionalAction,default=True,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--use_prior\",action=argparse.BooleanOptionalAction,default=False,\n",
+    "    help=\"whether to train diffusion prior (True) or just rely on retrieval part of the pipeline (False)\",\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--clip_scale\",type=float,default=1.,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--n_blocks\",type=int,default=4,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--hidden_dim\",type=int,default=2048,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--new_test\",action=argparse.BooleanOptionalAction,default=True,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--seq_len\",type=int,default=1,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--seed\",type=int,default=42,\n",
+    ")\n",
+    "parser.add_argument(\n",
+    "    \"--glmsingle_path\",type=str,default=\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_ses-01\",\n",
+    ")\n",
+    "if utils.is_interactive():\n",
+    "    args = parser.parse_args(jupyter_args)\n",
+    "else:\n",
+    "    args = parser.parse_args()\n",
+    "\n",
+    "# create global variables without the args prefix\n",
+    "for attribute_name in vars(args).keys():\n",
+    "    globals()[attribute_name] = getattr(args, attribute_name)\n",
+    "    \n",
+    "# make output directory\n",
+    "# os.makedirs(\"evals\",exist_ok=True)\n",
+    "# os.makedirs(f\"evals/{model_name}\",exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "34c1e0c6-0641-4239-8201-f2c676532302",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "csv/sub-005_ses-01.csv\n",
+      "(790, 122)\n",
+      "csv/sub-005_ses-02.csv\n",
+      "(1575, 122)\n",
+      "len_unique_images 1001\n",
+      "n_runs 22\n",
+      "['all_stimuli/special515/special_15939.jpg'\n",
+      " 'all_stimuli/special515/special_23241.jpg'\n",
+      " 'all_stimuli/special515/special_32232.jpg'\n",
+      " 'all_stimuli/special515/special_34238.jpg']\n",
+      "[190.2773371 194.2907918 198.3011098 202.3095724]\n",
+      "[0. 0. 0. 0.]\n",
+      "(1386,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if session == \"all\":\n",
+    "    filename = f\"csv/{sub}_{ses_list[0]}.csv\"\n",
+    "    data = pd.read_csv(filename)[14:]\n",
+    "    print(filename)\n",
+    "    print(data.shape)\n",
+    "    for s in ses_list[1:]:\n",
+    "        filename = f\"csv/{sub}_{s}.csv\"\n",
+    "        print(filename)\n",
+    "        data = pd.concat([data, pd.read_csv(filename)[14:]])\n",
+    "        print(data.shape)\n",
+    "else:\n",
+    "    filename = f\"csv/{sub}_{session}.csv\"\n",
+    "    if sub == 'sub-001' and session == 'ses-01':\n",
+    "        data = pd.read_csv(filename)[23:]\n",
+    "    else: \n",
+    "        data = pd.read_csv(filename)[14:]\n",
+    "    print(filename)\n",
+    "    print(data.shape)\n",
+    "\n",
+    "image_names = data['current_image'].values\n",
+    "starts = data['trial.started'].values\n",
+    "is_new_run = data['is_new_run'].values\n",
+    "\n",
+    "if sub == 'sub-001':\n",
+    "    if session == 'ses-01':\n",
+    "        assert image_names[0] == 'images/image_686_seed_1.png'\n",
+    "    elif session in ('ses-02', 'all'):\n",
+    "        assert image_names[0] == 'all_stimuli/special515/special_40840.jpg'\n",
+    "    elif session == 'ses-03':\n",
+    "        assert image_names[0] == 'all_stimuli/special515/special_69839.jpg'\n",
+    "    elif session == 'ses-04':\n",
+    "        assert image_names[0] == 'all_stimuli/rtmindeye_stimuli/image_686_seed_1.png'\n",
+    "elif sub == 'sub-003':\n",
+    "    assert image_names[0] == 'all_stimuli/rtmindeye_stimuli/image_686_seed_1.png'\n",
+    "\n",
+    "unique_images = np.unique(image_names.astype(str))\n",
+    "unique_images = unique_images[(unique_images!=\"nan\")]\n",
+    "# unique_images = unique_images[(unique_images!=\"blank.jpg\")]\n",
+    "len_unique_images = len(unique_images)\n",
+    "print(\"len_unique_images\",len_unique_images)\n",
+    "print(\"n_runs\",n_runs)\n",
+    "\n",
+    "if (sub == 'sub-001' and session == 'ses-04') or (sub == 'sub-003' and session == 'ses-01'):\n",
+    "    assert len(unique_images) == 851\n",
+    "\n",
+    "print(image_names[:4])\n",
+    "print(starts[:4])\n",
+    "print(is_new_run[:4])\n",
+    "\n",
+    "if remove_random_n:\n",
+    "    # want to remove 150 imgs\n",
+    "    # 100 special515 imgs are repeated 3x (300 total)\n",
+    "    # all other train imgs are only shown once (558 total)\n",
+    "    # of the 150, want to sample proportionally since we're cutting all repeats for special515\n",
+    "    # so take out 51 (17 unique) from special515 and 99 from rest = removing 150 total\n",
+    "    np.random.seed(seed)\n",
+    "    options_to_remove = [x for x in set(image_names) if str(x) != 'nan' and x != 'blank.jpg' and 'MST_pairs' not in x and 'special515' not in x and list(image_names).count(x)==1]  # all the imgs that only appear once (this is O(N^2) b/c of count() within list comprehension but image_names is a relatively small list)\n",
+    "    options_to_remove_special515 = [x for x in set(image_names) if str(x) != 'nan' and x != 'blank.jpg' and 'MST_pairs' not in x and 'special515' in x and list(image_names).count(x)>1]  # all the special515 images that are repeated (count()>1 necessary because there are special515 that are not repeated)\n",
+    "    imgs_to_remove = np.random.choice(options_to_remove, size=99, replace=False)\n",
+    "    imgs_to_remove = np.append(imgs_to_remove, np.random.choice(options_to_remove_special515, size=17, replace=False))\n",
+    "\n",
+    "image_idx = np.array([])  # contains the unique index of each presented image\n",
+    "vox_image_names = np.array([])  # contains the names of the images corresponding to image_idx\n",
+    "all_MST_images = dict()\n",
+    "for i, im in enumerate(image_names):\n",
+    "    # skip if blank, nan\n",
+    "    if im == \"blank.jpg\":\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    if str(im) == \"nan\":\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    vox_image_names = np.append(vox_image_names, im)\n",
+    "    if remove_close_to_MST:  # optionally skip close_to_MST images \n",
+    "        if \"closest_pairs\" in im:\n",
+    "            i+=1\n",
+    "            continue\n",
+    "    elif remove_random_n:\n",
+    "        if im in imgs_to_remove:\n",
+    "            i+=1\n",
+    "            continue\n",
+    "            \n",
+    "    image_idx_ = np.where(im==unique_images)[0].item()\n",
+    "    image_idx = np.append(image_idx, image_idx_)\n",
+    "    \n",
+    "    if (sub == 'sub-001' and session == 'ses-04') or (sub == 'sub-003' and session == 'ses-01'):  # MST images are ones that matched these image titles\n",
+    "        import re\n",
+    "        if ('w_' in im or 'paired_image_' in im or re.match(r'all_stimuli/rtmindeye_stimuli/\\d{1,2}_\\d{1,3}\\.png$', im) or re.match(r'images/\\d{1,2}_\\d{1,3}\\.png$', im)):  \n",
+    "        # the regexp here looks for **_***.png, allows 1-2 chars before underscore and 1-3 chars after it\n",
+    "            # print(im)\n",
+    "            all_MST_images[i] = im\n",
+    "            i+=1            \n",
+    "    elif 'MST' in im:\n",
+    "        all_MST_images[i] = im\n",
+    "        i+=1\n",
+    "    \n",
+    "image_idx = torch.Tensor(image_idx).long()\n",
+    "# for im in new_image_names[MST_images]:\n",
+    "#     assert 'MST_pairs' in im\n",
+    "# assert len(all_MST_images) == 300\n",
+    "\n",
+    "unique_MST_images = np.unique(list(all_MST_images.values())) \n",
+    "\n",
+    "MST_ID = np.array([], dtype=int)\n",
+    "if remove_close_to_MST:\n",
+    "    close_to_MST_idx = np.array([], dtype=int)\n",
+    "if remove_random_n:\n",
+    "    random_n_idx = np.array([], dtype=int)\n",
+    "\n",
+    "vox_idx = np.array([], dtype=int)\n",
+    "j=0  # this is a counter keeping track of the remove_random_n used later to index vox based on the removed images; unused otherwise\n",
+    "for i, im in enumerate(image_names):  # need unique_MST_images to be defined, so repeating the same loop structure\n",
+    "    # skip if blank, nan\n",
+    "    if im == \"blank.jpg\":\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    if str(im) == \"nan\":\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    if remove_close_to_MST:  # optionally skip close_to_MST images \n",
+    "        if \"closest_pairs\" in im:\n",
+    "            close_to_MST_idx = np.append(close_to_MST_idx, i)\n",
+    "            i+=1\n",
+    "            continue\n",
+    "    if remove_random_n:\n",
+    "        if im in imgs_to_remove:\n",
+    "            vox_idx = np.append(vox_idx, j)\n",
+    "            i+=1\n",
+    "            j+=1\n",
+    "            continue\n",
+    "    j+=1\n",
+    "    curr = np.where(im == unique_MST_images)\n",
+    "    # print(curr)\n",
+    "    if curr[0].size == 0:\n",
+    "        MST_ID = np.append(MST_ID, np.array(len(unique_MST_images)))  # add a value that should be out of range based on the for loop, will index it out later\n",
+    "    else:\n",
+    "        MST_ID = np.append(MST_ID, curr)\n",
+    "        \n",
+    "assert len(MST_ID) == len(image_idx)\n",
+    "# assert len(np.argwhere(pd.isna(data['current_image']))) + len(np.argwhere(data['current_image'] == 'blank.jpg')) + len(image_idx) == len(data)\n",
+    "# MST_ID = torch.tensor(MST_ID[MST_ID != len(unique_MST_images)], dtype=torch.uint8)  # torch.tensor (lowercase) allows dtype kwarg, Tensor (uppercase) is an alias for torch.FloatTensor\n",
+    "print(MST_ID.shape)\n",
+    "if (sub == 'sub-001' and session == 'ses-04') or (sub == 'sub-003' and session == 'ses-01'):\n",
+    "    assert len(all_MST_images) == 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dd08fa34-ebd0-482a-bc29-8fb32c8b888b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# unique_images_pairs = [\n",
+    "#     (1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16),\n",
+    "#     (17,18),(19,20),(21,22),(23,24),(25,26),(27,28),(29,30),\n",
+    "#     (31,32),(33,34),(35,36),\n",
+    "#     (787, 788), (789, 790), (791, 792), (793, 794), (795, 796),\n",
+    "#     (797, 798), (799, 800), (801, 802), (803, 804), (805, 806),\n",
+    "#     (807, 808), (809, 810), (811, 812), (813, 814), (815, 816),\n",
+    "#     (817, 818), (819, 820), (821, 822), (823, 824), (825, 826),\n",
+    "#     (827, 828), (829, 830), (831, 832), (833, 834), (835, 836),\n",
+    "#     (837, 838), (839, 840), (841, 842), (843, 844), (845, 846),\n",
+    "#     (847, 848), (849, 850)\n",
+    "# ]\n",
+    "# unique_images[unique_images_pairs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "59bc3b21-e29d-4d2b-8223-cd704e3f058a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 3/1386 [00:00<01:00, 22.73it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1386/1386 [06:14<00:00,  3.70it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "images torch.Size([1386, 3, 224, 224])\n",
+      "MST_images 1386\n",
+      "MST_images==True 248\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import imageio.v2 as imageio\n",
+    "resize_transform = transforms.Resize((224, 224))\n",
+    "MST_images = []\n",
+    "images = None\n",
+    "for im_name in tqdm(image_idx):\n",
+    "    if sub == 'sub-001' and session == 'ses-01':\n",
+    "        image_file = f\"all_stimuli/rtmindeye_stimuli/{unique_images[im_name]}\"\n",
+    "    else:\n",
+    "        image_file = f\"{unique_images[im_name]}\"\n",
+    "    im = imageio.imread(image_file)\n",
+    "    im = torch.Tensor(im / 255).permute(2,0,1)\n",
+    "    im = resize_transform(im.unsqueeze(0))\n",
+    "    if images is None:\n",
+    "        images = im\n",
+    "    else:\n",
+    "        images = torch.vstack((images, im))\n",
+    "    if (sub == 'sub-001' and session == 'ses-04') or (sub == 'sub-003' and session == 'ses-01'):\n",
+    "        if ('w_' in image_file or 'paired_image_' in image_file or re.match(r'all_stimuli/rtmindeye_stimuli/\\d{1,2}_\\d{1,3}\\.png$', image_file) or re.match(r'all_stimuli/rtmindeye_stimuli/images/\\d{1,2}_\\d{1,3}\\.png$', image_file)):  \n",
+    "            MST_images.append(True)\n",
+    "        else:\n",
+    "            MST_images.append(False)\n",
+    "    else:   \n",
+    "        if (\"MST_pairs\" in image_file): # (\"_seed_\" not in unique_images[im_name]) and (unique_images[im_name] != \"blank.jpg\") \n",
+    "            MST_images.append(True)\n",
+    "        else:\n",
+    "            MST_images.append(False)\n",
+    "\n",
+    "print(\"images\", images.shape)\n",
+    "MST_images = np.array(MST_images)\n",
+    "print(\"MST_images\", len(MST_images))\n",
+    "if (sub == 'sub-001' and session == 'ses-04') or (sub == 'sub-003' and session == 'ses-01'):\n",
+    "    assert len(MST_images[MST_images==True]) == 100\n",
+    "print(\"MST_images==True\", len(MST_images[MST_images==True]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f440a02-dd8a-4a13-9c90-bd07253f6910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pairs = utils.find_paired_indices(image_idx)\n",
+    "pairs = sorted(pairs, key=lambda x: x[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c5f61515-d4fa-419b-b945-cdedc8f24669",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "vox (1386, 1, 1, 182468)\n",
+      "vox (1386, 182468)\n"
+     ]
+    }
+   ],
+   "source": [
+    "vox = None\n",
+    "needs_postprocessing = False\n",
+    "params = (session, ses_list, remove_close_to_MST, image_names, remove_random_n, vox_idx)\n",
+    "\n",
+    "if resample_post_glmsingle == True:\n",
+    "    glm_save_path_resampled = f\"{glmsingle_path}/vox_resampled.nii.gz\"\n",
+    "    if load_from_resampled_file == True:\n",
+    "        # resampling was done in this notebook so we can load from file\n",
+    "        vox = nib.load(glm_save_path_resampled)\n",
+    "    else:\n",
+    "        # do resampling here\n",
+    "        assert os.path.exists(ref_name) and os.path.exists(omat_name), \"need to generate the boldref and omat separately since we don't have access to the functional data here; either do so using flirt on the command line or copy over the glmsingle resampled outputs\"\n",
+    "        vox = load_preprocess_betas(orig_glmsingle_path, *params)\n",
+    "        vox = resample_betas(orig_glmsingle_path, sub, session, task_name, vox, glmsingle_path, glm_save_path_resampled, ref_name, omat_name)\n",
+    "    needs_postprocessing = True\n",
+    "\n",
+    "if vox is None:\n",
+    "    # either resampling was done in glmsingle or we aren't resampling \n",
+    "    vox = load_preprocess_betas(glmsingle_path, *params)\n",
+    "\n",
+    "if needs_postprocessing == True:\n",
+    "    vox = apply_mask(vox, avg_mask)\n",
+    "    vox = vox.reshape(-1, vox.shape[-1])  # flatten the 3D image into np array with shape (voxels, images)\n",
+    "    print(vox.shape)\n",
+    "\n",
+    "assert len(vox) == len(image_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a4675ba2-b27c-48db-893c-d81f978ba93b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01-02_task-C/sub-005_ses-01-02_task-C_brain.nii.gz\n",
+      "Mask dimensions: (2.0, 2.0, 2.0)\n",
+      "\n",
+      "Affine:\n",
+      "[[  2.           0.           0.         -76.29234314]\n",
+      " [  0.           2.           0.         -84.79180908]\n",
+      " [  0.           0.           2.         -62.80359268]\n",
+      " [  0.           0.           0.           1.        ]]\n",
+      "\n",
+      "There are 182468 voxels in the included brain mask\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nilearn.plotting import plot_roi, plot_anat, plot_epi\n",
+    "\n",
+    "mask_name = f'{glmsingle_path}/{sub}_{session_label}{task_name}_brain'\n",
+    "if resample_voxel_size:\n",
+    "    if resample_post_glmsingle is True:\n",
+    "        # use original mask directory\n",
+    "        mask_in_name = f'{orig_glmsingle_path}/{sub}_{session}{task_name}_brain.nii.gz'\n",
+    "        mask_out_name = mask_name + f\"_{mask_resampled_suffix}.nii.gz\"\n",
+    "        assert os.path.exists(mask_in_name)\n",
+    "        applyxfm(mask_in_name, ref_name, omat_name, resample_method, output=mask_out_name)\n",
+    "        apply_thresh(mask_out_name, 0.5, output=mask_out_name)  # binarize the mask since resampling can result in non- 0 or 1 values\n",
+    "    mask_name += f\"_{mask_resampled_suffix}\"\n",
+    "\n",
+    "mask_name += \".nii.gz\"\n",
+    "print(mask_name)\n",
+    "avg_mask = nib.load(mask_name)\n",
+    "# mask info\n",
+    "dimsize=avg_mask.header.get_zooms()\n",
+    "affine_mat = avg_mask.affine\n",
+    "brain=avg_mask.get_fdata()\n",
+    "xyz=brain.shape #xyz dimensionality of brain mask and epi data\n",
+    "\n",
+    "print('Mask dimensions:', dimsize)\n",
+    "print('')\n",
+    "print('Affine:')\n",
+    "print(affine_mat)\n",
+    "print('')\n",
+    "print(f'There are {int(np.sum(brain))} voxels in the included brain mask\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8a5573cf-19b5-40e6-b21c-883e762f5f35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01-02_task-C/sub-005_ses-01-02_task-C_nsdgeneral.nii.gz\n",
+      "nsdgeneral path exists!\n"
+     ]
+    }
+   ],
+   "source": [
+    "nsdgeneral_path = f'{glmsingle_path}/{sub}_{session_label}{task_name}_nsdgeneral.nii.gz'  \n",
+    "print(nsdgeneral_path)\n",
+    "assert os.path.exists(nsdgeneral_path)\n",
+    "print(f\"nsdgeneral path exists!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b940e5dc-ac25-4f48-9764-6030cf18ff1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if resample_voxel_size:\n",
+    "    nsdgeneral_path = f'{glmsingle_path}/{sub}_task-{task}_nsdgeneral_resampled.nii.gz'  \n",
+    "    if resample_post_glmsingle:\n",
+    "        assert os.path.exists(orig_glmsingle_path)\n",
+    "        roi_in_path = f\"{orig_glmsingle_path}/{sub}_task-{task}_nsdgeneral.nii.gz\"  # the input file is the original nsdgeneral mask (without resampling), from the original glmsingle directory\n",
+    "        applyxfm(roi_in_path, ref_name, omat_name, resample_method, output=nsdgeneral_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d906312b-ea5d-418d-8326-e8b395c9a9c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading brain mask\n",
+      "Mask dimensions: (2.0, 2.0, 2.0)\n",
+      "\n",
+      "Affine:\n",
+      "[[  2.           0.           0.         -76.29234314]\n",
+      " [  0.           2.           0.         -84.79180908]\n",
+      " [  0.           0.           2.         -62.80359268]\n",
+      " [  0.           0.           0.           1.        ]]\n",
+      "\n",
+      "There are 180585 voxels in the included brain mask\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 730x350 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from nilearn.plotting import plot_roi\n",
+    "assert sub == 'sub-005' and session_label == 'ses-01-02'\n",
+    "print('loading brain mask')\n",
+    "avg_mask = nib.load(\"/home/ubuntu/3t_data/data/sub-005_final_brain.nii.gz\") # '/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_task-C/sub-005_final_brain.nii.gz'\n",
+    "final_mask = nib.load(\"/home/ubuntu/3t_data/data/sub-005_final_mask.nii.gz\") # '/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_task-C/sub-005_final_mask.nii.gz'\n",
+    "\n",
+    "# mask info\n",
+    "dimsize=avg_mask.header.get_zooms()\n",
+    "affine_mat = avg_mask.affine\n",
+    "brain=avg_mask.get_fdata()\n",
+    "xyz=brain.shape #xyz dimensionality of brain mask and epi data\n",
+    "\n",
+    "print('Mask dimensions:', dimsize)\n",
+    "print('')\n",
+    "print('Affine:')\n",
+    "print(affine_mat)\n",
+    "print('')\n",
+    "print(f'There are {int(np.sum(brain))} voxels in the included brain mask\\n')\n",
+    "\n",
+    "plot_roi(final_mask, bg_img=avg_mask)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "843dc2fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = f'/home/ubuntu/3t_data/data/union_mask_from_{session_label}.npy' # f'/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_task-C/union_mask_from_{session_label}.npy'\n",
+    "union_mask = np.load(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ae2862a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01-02_task-C'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glmsingle_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3b36fc1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ses_mask = []\n",
+    "ses_mask.append(nib.load(f'/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01_task-C/sub-005_ses-01_task-C_brain.nii.gz')) # f'/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_ses-01_task-C/sub-005_ses-01_task-C_brain.nii.gz'\n",
+    "ses_mask.append(nib.load(f'/home/ubuntu/glmsingle/glmsingle_sub-005_ses-02_task-C/sub-005_ses-02_task-C_brain.nii.gz')) # f'/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_ses-02_task-C/sub-005_ses-02_task-C_brain.nii.gz'\n",
+    "for m in ses_mask:\n",
+    "    assert np.all(m.affine == final_mask.affine)\n",
+    "    assert np.all(m.shape == final_mask.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e1f7cd2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "vox (693, 1, 1, 182242)\n",
+      "vox (693, 182242)\n",
+      "vox (693, 1, 1, 183159)\n",
+      "vox (693, 183159)\n",
+      "applied final brain mask\n",
+      "(1386, 19174)\n",
+      "applied union roi mask\n",
+      "(1386, 8627)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ses_vox = []\n",
+    "vox = None\n",
+    "needs_postprocessing = False\n",
+    "params = (session, ses_list, remove_close_to_MST, image_names, remove_random_n, vox_idx)\n",
+    "\n",
+    "if resample_post_glmsingle == True:\n",
+    "    glm_save_path_resampled = f\"{glmsingle_path}/vox_resampled.nii.gz\"\n",
+    "    if load_from_resampled_file == True:\n",
+    "        # resampling was done in this notebook so we can load from file\n",
+    "        vox = nib.load(glm_save_path_resampled)\n",
+    "    else:\n",
+    "        # do resampling here\n",
+    "        assert os.path.exists(ref_name) and os.path.exists(omat_name), \"need to generate the boldref and omat separately since we don't have access to the functional data here; either do so using flirt on the command line or copy over the glmsingle resampled outputs\"\n",
+    "        vox = load_preprocess_betas(orig_glmsingle_path, *params)\n",
+    "        vox = resample_betas(orig_glmsingle_path, sub, session, task_name, vox, glmsingle_path, glm_save_path_resampled, ref_name, omat_name)\n",
+    "    needs_postprocessing = True\n",
+    "\n",
+    "if vox is None:\n",
+    "    # either resampling was done in glmsingle or we aren't resampling \n",
+    "    ses_vox.append(load_preprocess_betas(f'/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01_task-C', *params)) # '/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_ses-01_task-C'\n",
+    "    ses_vox.append(load_preprocess_betas(f'/home/ubuntu/glmsingle/glmsingle_sub-005_ses-02_task-C', *params)) # '/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_sub-005_ses-02_task-C'\n",
+    "    for i, v in enumerate(ses_vox):\n",
+    "        v = nilearn.masking.unmask(v, ses_mask[i])\n",
+    "        ses_vox[i] = nilearn.masking.apply_mask(v, final_mask)\n",
+    "    vox = np.concatenate(ses_vox)\n",
+    "    print(\"applied final brain mask\")\n",
+    "    print(vox.shape)\n",
+    "    vox = vox[:, union_mask]\n",
+    "    print(\"applied union roi mask\")\n",
+    "    print(vox.shape)\n",
+    "    \n",
+    "    \n",
+    "if needs_postprocessing == True:\n",
+    "    vox = apply_mask(vox, avg_mask)\n",
+    "    vox = vox.reshape(-1, vox.shape[-1])  # flatten the 3D image into np array with shape (voxels, images)\n",
+    "    print(vox.shape)\n",
+    "\n",
+    "assert len(vox) == len(image_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "26802a5b-7bc8-4d47-b8e0-1dfa557fc6ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pairs_homog = np.array([[p[0], p[1]] for p in pairs])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "735dfc27-a9bd-4a22-ac3f-a1f44515293e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1138 248\n"
+     ]
+    }
+   ],
+   "source": [
+    "utils.seed_everything(seed)\n",
+    "\n",
+    "# add_repeats = 48\n",
+    "# imageTrain = np.arange(len(images))\n",
+    "# train_image_indices = np.array([item for item in imageTrain if item not in pairs.flatten()])\n",
+    "# train_image_indices = np.sort(np.append(train_image_indices, np.array(pairs[:add_repeats].flatten())))\n",
+    "\n",
+    "# # check that there's no repeat indices in training data\n",
+    "# assert len(sorted(np.append(np.array([item for item in imageTrain if item not in pairs.flatten()]), np.array(pairs[:add_repeats].flatten())))) == len(set(sorted(np.append(np.array([item for item in imageTrain if item not in pairs.flatten()]), np.array(pairs[:add_repeats].flatten())))))\n",
+    "\n",
+    "# test_image_indices = pairs[add_repeats:]\n",
+    "# print(len(train_image_indices), len(test_image_indices))\n",
+    "\n",
+    "if train_test_split == 'orig':\n",
+    "    # train = all images except images that were repeated\n",
+    "    # test = average of the same-image presentations\n",
+    "    imageTrain = np.arange(len(images))\n",
+    "    train_image_indices = np.array([item for item in imageTrain if item not in pairs.flatten()])\n",
+    "    test_image_indices = pairs\n",
+    "    print(len(train_image_indices), len(test_image_indices))\n",
+    "elif train_test_split == 'MST':\n",
+    "    # non-MST images are the train split\n",
+    "    # MST images are the test split\n",
+    "    train_image_indices = np.where(MST_images==False)[0]\n",
+    "    test_image_indices = np.where(MST_images==True)[0]\n",
+    "    print(len(train_image_indices), len(test_image_indices))\n",
+    "    # for i in test_image_indices:\n",
+    "    #     assert i in pairs  # all MST images have pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a292cfad-83f4-4bf8-994e-da2c871c0a6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test_image_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b81220cd-c11d-4a2a-8755-53b70d90cfe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# repeats_in_test = []\n",
+    "# for p in pairs:\n",
+    "#     group = []\n",
+    "#     for item in p:\n",
+    "#         curr = np.where(test_image_indices == item)\n",
+    "#         if curr[0].size > 0:\n",
+    "#             group.append(curr[0][0])\n",
+    "#     # print(np.array(group))\n",
+    "#     if len(group) > 0:\n",
+    "#         repeats_in_test.append(np.array(group))\n",
+    "#     # if p[0] in test_image_indices:\n",
+    "#     #     repeats_in_test.append(p)\n",
+    "        \n",
+    "# repeats_in_test = np.array(repeats_in_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5528d877-b662-41f7-8982-3f31051871f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "voxels have been zscored\n",
+      "-0.08859582 0.99191713\n",
+      "vox (1386, 8627)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_mean = np.mean(vox[train_image_indices],axis=0)\n",
+    "train_std = np.std(vox[train_image_indices],axis=0)\n",
+    "\n",
+    "vox = utils.zscore(vox,train_mean=train_mean,train_std=train_std)\n",
+    "print(\"voxels have been zscored\")\n",
+    "print(vox[:,0].mean(), vox[:,0].std())\n",
+    "print(\"vox\", vox.shape)\n",
+    "\n",
+    "images = torch.Tensor(images)\n",
+    "vox = torch.Tensor(vox)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1eb5d464-7ffa-419a-a6b4-d0108f8e196a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = torch.utils.data.TensorDataset(torch.tensor(test_image_indices))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8a3901c-60dd-4ae2-b0f5-8a55aa231908",
+   "metadata": {},
+   "source": [
+    "# Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "64672583-9f00-46f5-8d4e-00e4c7068a1d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded test dl for subj1!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "subj_list = [subj]\n",
+    "subj = subj_list[0]\n",
+    "test_dl = torch.utils.data.DataLoader(test_data, batch_size=len(test_data), shuffle=False, drop_last=True, pin_memory=True)\n",
+    "print(f\"Loaded test dl for subj{subj}!\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a3cbeea8-e95b-48d9-9bc2-91af260c93d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 248 248\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_voxels, test_images = None, None\n",
+    "for test_i, behav in enumerate(test_dl):\n",
+    "    behav = behav[0]\n",
+    "\n",
+    "    if behav.ndim>1:\n",
+    "        test_image = images[behav[:,0].long().cpu()].to(device)\n",
+    "        test_vox = vox[behav.long().cpu()].mean(1)\n",
+    "    else:\n",
+    "        test_image = images[behav.long().cpu()].to(device)\n",
+    "        test_vox = vox[behav.long().cpu()]\n",
+    "    \n",
+    "    if test_voxels is None:\n",
+    "        test_voxels = test_vox\n",
+    "        test_images = test_image\n",
+    "    else:\n",
+    "        test_voxels = torch.vstack((test_voxels, test_vox))\n",
+    "        test_images = torch.vstack((test_images, test_image))\n",
+    "\n",
+    "print(test_i, len(test_voxels), len(test_images))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a3ae7a06-7135-4073-b315-59579e35e2a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_voxels_list = []\n",
+    "num_voxels_list.append(test_voxels.shape[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "de0400d4-cbd6-4941-a0b2-1a4bc2ae97da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# ## USING OpenCLIP ViT-bigG ###\n",
+    "# sys.path.append('generative_models/')\n",
+    "# import sgm\n",
+    "# from generative_models.sgm.modules.encoders.modules import FrozenOpenCLIPImageEmbedder\n",
+    "\n",
+    "# try:\n",
+    "#     print(clip_img_embedder)\n",
+    "# except:\n",
+    "#     clip_img_embedder = FrozenOpenCLIPImageEmbedder(\n",
+    "#         arch=\"ViT-bigG-14\",\n",
+    "#         version=\"laion2b_s39b_b160k\",\n",
+    "#         output_tokens=True,\n",
+    "#         only_tokens=True,\n",
+    "#     )\n",
+    "#     clip_img_embedder.to(device)\n",
+    "clip_seq_dim = 1\n",
+    "clip_emb_dim = 1024"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "56b606a4-7302-4ac5-b89d-bbe4fcb00d11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e452b5b2-47d9-4271-b9fc-ea331fbac1bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/rt_sdxlturbo/lib/python3.11/site-packages/clip/clip.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
+      "  from pkg_resources import packaging\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MindEyeModule()\n",
+      "param counts:\n",
+      "8,835,072 total\n",
+      "8,835,072 trainable\n",
+      "param counts:\n",
+      "8,835,072 total\n",
+      "8,835,072 trainable\n",
+      "param counts:\n",
+      "12,609,560 total\n",
+      "12,609,560 trainable\n",
+      "param counts:\n",
+      "21,444,632 total\n",
+      "21,444,632 trainable\n",
+      "param counts:\n",
+      "97,743,072 total\n",
+      "97,743,056 trainable\n",
+      "param counts:\n",
+      "119,187,704 total\n",
+      "119,187,688 trainable\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = utils.prepare_model_and_training(\n",
+    "    num_voxels_list=num_voxels_list,\n",
+    "    n_blocks=n_blocks,\n",
+    "    hidden_dim=hidden_dim,\n",
+    "    clip_emb_dim=clip_emb_dim,\n",
+    "    clip_seq_dim=clip_seq_dim,\n",
+    "    use_prior=use_prior,\n",
+    "    clip_scale=clip_scale\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f726f617-39f5-49e2-8d0c-d11d27d01c30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # prep unCLIP\n",
+    "# config = OmegaConf.load(\"/scratch/gpfs/ri4541/MindEyeV2/src/generative_models/configs/unclip6.yaml\")\n",
+    "# config = OmegaConf.to_container(config, resolve=True)\n",
+    "# unclip_params = config[\"model\"][\"params\"]\n",
+    "# network_config = unclip_params[\"network_config\"]\n",
+    "# denoiser_config = unclip_params[\"denoiser_config\"]\n",
+    "# first_stage_config = unclip_params[\"first_stage_config\"]\n",
+    "# conditioner_config = unclip_params[\"conditioner_config\"]\n",
+    "# sampler_config = unclip_params[\"sampler_config\"]\n",
+    "# scale_factor = unclip_params[\"scale_factor\"]\n",
+    "# disable_first_stage_autocast = unclip_params[\"disable_first_stage_autocast\"]\n",
+    "# offset_noise_level = unclip_params[\"loss_fn_config\"][\"params\"][\"offset_noise_level\"]\n",
+    "\n",
+    "# first_stage_config['target'] = 'sgm.models.autoencoder.AutoencoderKL'\n",
+    "# sampler_config['params']['num_steps'] = 38\n",
+    "\n",
+    "# diffusion_engine = DiffusionEngine(network_config=network_config,\n",
+    "#                        denoiser_config=denoiser_config,\n",
+    "#                        first_stage_config=first_stage_config,\n",
+    "#                        conditioner_config=conditioner_config,\n",
+    "#                        sampler_config=sampler_config,\n",
+    "#                        scale_factor=scale_factor,\n",
+    "#                        disable_first_stage_autocast=disable_first_stage_autocast)\n",
+    "# # set to inference\n",
+    "# diffusion_engine.eval().requires_grad_(False)\n",
+    "# diffusion_engine.to(device)\n",
+    "\n",
+    "# ckpt_path = '/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/unclip6_epoch0_step110000.ckpt' \n",
+    "# ckpt = torch.load(ckpt_path, map_location='cpu')\n",
+    "# diffusion_engine.load_state_dict(ckpt['state_dict'])\n",
+    "\n",
+    "# batch={\"jpg\": torch.randn(1,3,1,1).to(device), # jpg doesnt get used, it's just a placeholder\n",
+    "#       \"original_size_as_tuple\": torch.ones(1, 2).to(device) * 768,\n",
+    "#       \"crop_coords_top_left\": torch.zeros(1, 2).to(device)}\n",
+    "# out = diffusion_engine.conditioner(batch)\n",
+    "# vector_suffix = out[\"vector\"].to(device)\n",
+    "# print(\"vector_suffix\", vector_suffix.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "68abd440-7e6b-4023-9dc8-05b1b5c0baa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generate_captions = False\n",
+    "\n",
+    "if generate_captions:\n",
+    "    # setup text caption networks\n",
+    "    from transformers import AutoProcessor, AutoModelForCausalLM\n",
+    "    from modeling_git import GitForCausalLMClipEmb\n",
+    "    # processor = AutoProcessor.from_pretrained(\"microsoft/git-large-coco\")\n",
+    "    # clip_text_model = GitForCausalLMClipEmb.from_pretrained(\"microsoft/git-large-coco\")\n",
+    "    processor = AutoProcessor.from_pretrained(\"microsoft/git-large-coco\") # \"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2\"\n",
+    "    clip_text_model = GitForCausalLMClipEmb.from_pretrained(\"microsoft/git-large-coco\") # \"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2\"\n",
+    "\n",
+    "    clip_text_model.to(device) # if you get OOM running this script, you can switch this to cpu and lower minibatch_size to 4\n",
+    "    clip_text_model.eval().requires_grad_(False)\n",
+    "    clip_text_seq_dim = 257\n",
+    "    clip_text_emb_dim = 1024\n",
+    "\n",
+    "    class CLIPConverter(torch.nn.Module):\n",
+    "        def __init__(self):\n",
+    "            super(CLIPConverter, self).__init__()\n",
+    "            self.linear1 = nn.Linear(clip_seq_dim, clip_text_seq_dim)\n",
+    "            self.linear2 = nn.Linear(clip_emb_dim, clip_text_emb_dim)\n",
+    "        def forward(self, x):\n",
+    "            x = x.permute(0,2,1)\n",
+    "            x = self.linear1(x)\n",
+    "            x = self.linear2(x.permute(0,2,1))\n",
+    "            return x\n",
+    "            \n",
+    "    clip_convert = CLIPConverter()\n",
+    "    state_dict = torch.load(\"/home/ubuntu/rt_mindEye/bigG_to_L_epoch8.pth\", map_location='cpu')['model_state_dict'] # \"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/bigG_to_L_epoch8.pth\"\n",
+    "    clip_convert.load_state_dict(state_dict, strict=True)\n",
+    "    clip_convert.to(device) # if you get OOM running this script, you can switch this to cpu and lower minibatch_size to 4\n",
+    "    del state_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "41b4a640",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "---loading /home/ubuntu/real_time_mindEye2/train_logs/testing_MST_ViT-H_1024_scalf/last.pth ckpt---\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_74786/184505764.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  checkpoint = torch.load(outdir+f'/{tag}.pth', map_location='cpu')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ckpt loaded!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load pretrained model ckpt\n",
+    "tag='last'\n",
+    "outdir = os.path.abspath(f'/home/ubuntu/real_time_mindEye2/train_logs/{model_name}') # f'/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/train_logs/{model_name}'\n",
+    "print(f\"\\n---loading {outdir}/{tag}.pth ckpt---\\n\")\n",
+    "checkpoint = torch.load(outdir+f'/{tag}.pth', map_location='cpu')\n",
+    "state_dict = checkpoint['model_state_dict']\n",
+    "model.load_state_dict(state_dict, strict=True)\n",
+    "del checkpoint\n",
+    "print(\"ckpt loaded!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "8062afe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eval_dir = f\"/home/ubuntu/real_time_mindEye2/evals/{model_name}\" # \"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "a1b1e759",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02dbc558b1cc4244b600bc03dd740610",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from diffusers import DiffusionPipeline, AutoPipelineForText2Image\n",
+    "from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\n",
+    "\n",
+    "class SDXL_Reconstructor:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        num_inference_steps=4,\n",
+    "        device=\"cuda\",\n",
+    "        cache_dir=\"/srv/eeg_reconstruction/shared/cache/\",\n",
+    "    ):\n",
+    "        self.num_inference_steps = num_inference_steps\n",
+    "        self.dtype = torch.float16\n",
+    "        self.device = device\n",
+    "\n",
+    "        image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n",
+    "            \"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\", cache_dir=cache_dir\n",
+    "        )\n",
+    "        feature_extractor = CLIPImageProcessor(size=224, crop_size=224)\n",
+    "\n",
+    "        pipe = AutoPipelineForText2Image.from_pretrained(\n",
+    "            \"stabilityai/sdxl-turbo\",\n",
+    "            variant=\"fp16\",\n",
+    "            cache_dir=cache_dir,\n",
+    "            image_encoder=image_encoder,\n",
+    "            feature_extractor=feature_extractor,\n",
+    "        )\n",
+    "        pipe.to(device)\n",
+    "        # load ip adapter\n",
+    "        pipe.load_ip_adapter(\n",
+    "            \"h94/IP-Adapter\",\n",
+    "            subfolder=\"sdxl_models\",\n",
+    "            weight_name=\"ip-adapter_sdxl_vit-h.safetensors\",\n",
+    "            torch_dtype=torch.float16,\n",
+    "            cache_dir=cache_dir,\n",
+    "        )\n",
+    "        # set ip_adapter scale (default is 1)\n",
+    "        pipe.set_ip_adapter_scale(1)\n",
+    "        self.pipe = pipe\n",
+    "        self.pipe.vae = self.pipe.vae.to(torch.float32)\n",
+    "\n",
+    "    def encode_image(self, image):\n",
+    "        if isinstance(image, torch.Tensor):\n",
+    "            # Handle the case where 'image' is a tensor\n",
+    "            if image.ndim == 3:\n",
+    "                image = [\n",
+    "                    transforms.ToPILImage()(image.cpu())\n",
+    "                ]  # Convert single tensor to PIL Image\n",
+    "            elif image.ndim == 4:\n",
+    "                image = [\n",
+    "                    transforms.ToPILImage()(img.cpu()) for img in image\n",
+    "                ]  # Convert batch of tensors to list of PIL Images\n",
+    "            else:\n",
+    "                raise ValueError(\n",
+    "                    \"Unsupported tensor shape. Expected 3 or 4 dimensions.\"\n",
+    "                )\n",
+    "        elif isinstance(image, str):\n",
+    "            # Handle the case where 'image' is a string (assumed to be a file path)\n",
+    "            image = [\n",
+    "                Image.open(image).convert(\"RGB\")\n",
+    "            ]  # Open image and convert to RGB\n",
+    "        elif isinstance(image, list) and all(\n",
+    "            isinstance(img, str) for img in image\n",
+    "        ):\n",
+    "            # Handle the case where 'image' is a list of strings (image file paths)\n",
+    "            image = [\n",
+    "                Image.open(path).convert(\"RGB\") for path in image\n",
+    "            ]  # Open each image and convert to RGB\n",
+    "        elif isinstance(image, Image.Image):\n",
+    "            # Handle the case where 'image' is a PIL Image\n",
+    "            image = [image.convert(\"RGB\")]  # Ensure image is in RGB format\n",
+    "        elif isinstance(image, list) and all(\n",
+    "            isinstance(img, Image.Image) for img in image\n",
+    "        ):\n",
+    "            # Handle the case where 'image' is a list of PIL Images\n",
+    "            image = [\n",
+    "                img.convert(\"RGB\") for img in image\n",
+    "            ]  # Ensure all images are in RGB format\n",
+    "        else:\n",
+    "            raise ValueError(\n",
+    "                \"Unsupported image format. Please provide a tensor, image\"\n",
+    "                \" path, list of image paths, a PIL Image, or a list of PIL\"\n",
+    "                \" Images.\"\n",
+    "            )\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            # Now 'image' is a list of PIL Images\n",
+    "            clip_image, _ = self.pipe.encode_image(\n",
+    "                image=image, device=self.device, num_images_per_prompt=1\n",
+    "            )\n",
+    "        return clip_image\n",
+    "\n",
+    "    def encode_text(self, text, cropped=False):\n",
+    "        with torch.no_grad():\n",
+    "            clip_text, _, _, _ = self.pipe.encode_prompt(text)\n",
+    "            if cropped:\n",
+    "                clip_text = clip_text[:, :, :768]\n",
+    "        return clip_text\n",
+    "\n",
+    "    def encode_latent(self, image):\n",
+    "        if not isinstance(image, PIL.Image.Image):\n",
+    "            raise ValueError(\n",
+    "                \"`image` has to be of type PIL.Image.Image but is\"\n",
+    "                f\" {type(image)}\"\n",
+    "            )\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            image = self.pipe.image_processor.preprocess(image).to(\n",
+    "                \"cuda\", torch.float32\n",
+    "            )\n",
+    "            latents = self.pipe.vae.encode(image).latent_dist.sample(None)\n",
+    "            latents = latents * self.pipe.vae.config.scaling_factor\n",
+    "        return latents\n",
+    "\n",
+    "    def reconstruct(\n",
+    "        self,\n",
+    "        image=None,\n",
+    "        latent=None,\n",
+    "        c_i=None,\n",
+    "        c_t=None,\n",
+    "        n_samples=1,\n",
+    "        textstrength=0.0,\n",
+    "        strength=1.0,\n",
+    "        cropped=False,\n",
+    "    ):\n",
+    "        with torch.no_grad():\n",
+    "            if c_i is not None and c_t is not None:\n",
+    "                c_i = [\n",
+    "                    c_i.unsqueeze(0).to(device=self.device, dtype=self.dtype)\n",
+    "                ]\n",
+    "                if cropped:\n",
+    "                    c_t = c_t.reshape((-1, 77, 768))\n",
+    "                    c_t = F.pad(c_t, (0, 1280))\n",
+    "                else:\n",
+    "                    c_t = c_t.reshape((-1, 77, 2048))\n",
+    "                prompt = None\n",
+    "                pooled_prompt_embeds = torch.zeros((1, 1280))\n",
+    "            elif c_i is not None:\n",
+    "                c_i = [\n",
+    "                    c_i.unsqueeze(0).to(device=self.device, dtype=self.dtype)\n",
+    "                ]\n",
+    "                prompt = \"\"\n",
+    "                pooled_prompt_embeds = None\n",
+    "                textstrength = 0.0\n",
+    "            elif c_t is not None:\n",
+    "                c_i = [torch.zeros((1, 1, 1024))]\n",
+    "                if cropped:\n",
+    "                    c_t = c_t.reshape((-1, 77, 768))\n",
+    "                    c_t = F.pad(c_t, (0, 1280))\n",
+    "                else:\n",
+    "                    c_t = c_t.reshape((-1, 77, 2048))\n",
+    "                prompt = None\n",
+    "                pooled_prompt_embeds = torch.zeros((1, 1280))\n",
+    "                textstrength = 1.0\n",
+    "            else:\n",
+    "                strength = 0.0\n",
+    "            if image is None and latent is None:\n",
+    "                strength = 1.0\n",
+    "\n",
+    "            if latent is not None:\n",
+    "                latent = latent.reshape((-1, 4, 62, 62)).to(\n",
+    "                    self.device, torch.float32\n",
+    "                )\n",
+    "                latent = latent / self.pipe.vae.config.scaling_factor\n",
+    "                image = self.pipe.vae.decode(latent, return_dict=False)[0]\n",
+    "                images = self.pipe.image_processor.postprocess(\n",
+    "                    image, output_type=\"pil\"\n",
+    "                )\n",
+    "\n",
+    "            if strength != 0.0:\n",
+    "                self.pipe.set_ip_adapter_scale(1 - textstrength)\n",
+    "                images = self.pipe(\n",
+    "                    image=image,\n",
+    "                    strength=strength,\n",
+    "                    prompt=prompt,\n",
+    "                    prompt_embeds=c_t,\n",
+    "                    pooled_prompt_embeds=pooled_prompt_embeds,\n",
+    "                    ip_adapter_image_embeds=c_i,\n",
+    "                    num_inference_steps=self.num_inference_steps,\n",
+    "                    guidance_scale=0.0,\n",
+    "                    generator=None,\n",
+    "                    num_images_per_prompt=n_samples,\n",
+    "                ).images\n",
+    "\n",
+    "        if len(images) == 1:\n",
+    "            return images[0]\n",
+    "        else:\n",
+    "            return images\n",
+    "\n",
+    "device = 'cuda'\n",
+    "generator = SDXL_Reconstructor(device=device, cache_dir='.cache')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c6a706a3-d151-4643-bb34-7d08aa7361c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/8 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a926203ca7c84c51a74e0198b32b1561",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sampling loop time step:   0%|          | 0/19 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "093b7606661e4eab8bbbf7f9cdd4be61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecdc55df108a44b4a88e0b2aeb5e6a7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "360f19d9b7ab4a4fb1e8f5dc16f050ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
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+    },
+    {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
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+    },
+    {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/4 [00:00<?, ?it/s]"
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+    },
+    {
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 88%|████████▊ | 7/8 [04:31<00:30, 30.62s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "testing_MST_ViT-H_1024_scalf\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "sampling loop time step:   0%|          | 0/19 [00:00<?, ?it/s]"
+      ]
+     },
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+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 8/8 [04:49<00:00, 36.14s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "testing_MST_ViT-H_1024_scalf\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved testing_MST_ViT-H_1024_scalf outputs!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get all reconstructions\n",
+    "model.to(device)\n",
+    "model.eval().requires_grad_(False)\n",
+    "\n",
+    "all_blurryrecons = None\n",
+    "all_images = None\n",
+    "all_recons = None\n",
+    "all_predcaptions = []\n",
+    "all_clipvoxels = None\n",
+    "all_prior_out = None\n",
+    "all_backbones = None\n",
+    "\n",
+    "minibatch_size = 32\n",
+    "num_samples_per_image = 1\n",
+    "plotting = False\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    for batch in tqdm(range(0,len(test_images),minibatch_size)):\n",
+    "        start_time = time.time() \n",
+    "\n",
+    "        image = test_images[batch:batch+minibatch_size]\n",
+    "        voxel = test_voxels[batch:batch+minibatch_size].unsqueeze(1).to(device)\n",
+    "\n",
+    "        # Save ground truth images\n",
+    "        if all_images is None:\n",
+    "            all_images = image\n",
+    "        else:\n",
+    "            all_images = torch.vstack((all_images, image))\n",
+    "        \n",
+    "        voxel_ridge = model.ridge(voxel,0)\n",
+    "        backbone, clip_voxels, blurry_image_enc_ = model.backbone(voxel_ridge)\n",
+    "                \n",
+    "        # Save retrieval submodule outputs\n",
+    "        if clip_scale>0:\n",
+    "            if all_clipvoxels is None:\n",
+    "                all_clipvoxels = clip_voxels.cpu()\n",
+    "            else:\n",
+    "                all_clipvoxels = torch.vstack((all_clipvoxels, clip_voxels.cpu()))\n",
+    "                \n",
+    "        # Feed voxels through OpenCLIP-bigG diffusion prior\n",
+    "        prior_out = model.diffusion_prior.p_sample_loop(backbone.shape, \n",
+    "                        text_cond = dict(text_embed = backbone), \n",
+    "                        cond_scale = 1., timesteps = 20).cpu()\n",
+    "        \n",
+    "        if all_prior_out is None:\n",
+    "            all_prior_out = prior_out\n",
+    "        else:\n",
+    "            all_prior_out = torch.vstack((all_prior_out, prior_out))\n",
+    "\n",
+    "        if generate_captions:\n",
+    "            pred_caption_emb = clip_convert(prior_out.to(device).float())\n",
+    "            generated_ids = clip_text_model.generate(pixel_values=pred_caption_emb, max_length=20)\n",
+    "            generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
+    "            all_predcaptions = np.hstack((all_predcaptions, generated_caption))\n",
+    "            print(generated_caption)\n",
+    "            \n",
+    "        # Feed diffusion prior outputs through unCLIP\n",
+    "        if plotting:\n",
+    "            jj=-1\n",
+    "            fig, axes = plt.subplots(1, len(voxel)+2, figsize=(20, 8))\n",
+    "\n",
+    "        for i in range(len(voxel)):\n",
+    "            # samples = utils.unclip_recon(prior_out[[i]],\n",
+    "            #                  diffusion_engine,\n",
+    "            #                  vector_suffix,\n",
+    "            #                  num_samples=num_samples_per_image)\n",
+    "   \n",
+    "            samples = generator.reconstruct(\n",
+    "                            c_i=prior_out[[i]], # eeg_test_features[stimuli].unsqueeze(0),\n",
+    "                            n_samples=num_samples_per_image,\n",
+    "                        )\n",
+    "            if isinstance(samples, list) :\n",
+    "                samples = torch.cat([torch.tensor(np.array(sample).transpose(2,0,1)).unsqueeze(0) for sample in samples])\n",
+    "                samples = samples.unsqueeze(0)\n",
+    "\n",
+    "            else:\n",
+    "                samples = torch.tensor(np.array(samples).transpose(2,0,1)).unsqueeze(0)\n",
+    "            \n",
+    "            samples = samples/255\n",
+    "            if all_recons is None:\n",
+    "                all_recons = samples.cpu()\n",
+    "            else:\n",
+    "                all_recons = torch.vstack((all_recons, samples.cpu()))\n",
+    "                \n",
+    "            if plotting:  \n",
+    "                jj+=1\n",
+    "                axes[jj].imshow(utils.torch_to_Image(image[i]))\n",
+    "                axes[jj].axis('off')\n",
+    "                jj+=1\n",
+    "                axes[jj].imshow(utils.torch_to_Image(samples.cpu()[0]))\n",
+    "                axes[jj].axis('off')\n",
+    "        \n",
+    "        plt.show()\n",
+    "\n",
+    "        print(model_name)\n",
+    "        # err # dont actually want to run the whole thing with plotting=True\n",
+    "\n",
+    "# resize outputs before saving\n",
+    "imsize = 256\n",
+    "all_images = transforms.Resize((imsize,imsize))(all_images).float()\n",
+    "all_recons = transforms.Resize((imsize,imsize))(all_recons).float()\n",
+    "if blurry_recon: \n",
+    "    all_blurryrecons = transforms.Resize((imsize,imsize))(all_blurryrecons).float()\n",
+    "        \n",
+    "## Saving ##\n",
+    "if not os.path.exists(eval_dir):\n",
+    "    os.mkdir(eval_dir)\n",
+    "\n",
+    "if \"MST\" in model_name:\n",
+    "    np.save(f\"{eval_dir}/{model_name}_MST_ID.npy\", MST_ID)\n",
+    "torch.save(all_images.cpu(),f\"{eval_dir}/{model_name}_all_images.pt\")\n",
+    "\n",
+    "# repeats_in_test = []\n",
+    "# for p in pairs:\n",
+    "#     if p[0] in test_image_indices:\n",
+    "#         repeats_in_test.append(p)\n",
+    "        \n",
+    "# repeats_in_test = np.array(repeats_in_test)\n",
+    "\n",
+    "# torch.save(test_image_indices, f\"{eval_dir}/{model_name}_test_image_indices.pt\")\n",
+    "# torch.save(repeats_in_test, f\"{eval_dir}/{model_name}_repeats_in_test.pt\")\n",
+    "torch.save(all_recons,f\"{eval_dir}/{model_name}_all_recons.pt\")\n",
+    "if clip_scale>0:\n",
+    "    torch.save(all_clipvoxels,f\"{eval_dir}/{model_name}_all_clipvoxels.pt\")\n",
+    "torch.save(all_prior_out,f\"{eval_dir}/{model_name}_all_prior_out.pt\")\n",
+    "if generate_captions:\n",
+    "    torch.save(all_predcaptions,f\"{eval_dir}/{model_name}_all_predcaptions.pt\")\n",
+    "print(f\"saved {model_name} outputs!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "be402221",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# torch.tensor(np.array(samples).transpose(2,0,1)).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e9e24f37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([248, 3, 256, 256]), tensor(1.), tensor(0.))"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_recons.shape, all_recons.max(), all_recons.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "96ea1761",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# c_immm = all_images[5]\n",
+    "\n",
+    "# plt.imshow(c_immm.to('cpu').permute(1,2,0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "5bcd5050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if \"MST\" in model_name:\n",
+    "#     np.save(f\"{eval_dir}/{model_name}_MST_ID.npy\", MST_ID)\n",
+    "# torch.save(all_images.cpu(),f\"{eval_dir}/{model_name}_all_images.pt\")\n",
+    "\n",
+    "# # repeats_in_test = []\n",
+    "# # for p in pairs:\n",
+    "# #     if p[0] in test_image_indices:\n",
+    "# #         repeats_in_test.append(p)\n",
+    "        \n",
+    "# # repeats_in_test = np.array(repeats_in_test)\n",
+    "\n",
+    "# # torch.save(test_image_indices, f\"{eval_dir}/{model_name}_test_image_indices.pt\")\n",
+    "# # torch.save(repeats_in_test, f\"{eval_dir}/{model_name}_repeats_in_test.pt\")\n",
+    "# torch.save(all_recons,f\"{eval_dir}/{model_name}_all_recons.pt\")\n",
+    "# if clip_scale>0:\n",
+    "#     torch.save(all_clipvoxels,f\"{eval_dir}/{model_name}_all_clipvoxels.pt\")\n",
+    "# torch.save(all_prior_out,f\"{eval_dir}/{model_name}_all_prior_out.pt\")\n",
+    "# torch.save(all_predcaptions,f\"{eval_dir}/{model_name}_all_predcaptions.pt\")\n",
+    "# print(f\"saved {model_name} outputs!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "73b243d7-6552-4fc8-bef7-d5ad03b17cb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if \"MST\" in model_name:\n",
+    "#     np.save(f\"{eval_dir}/{model_name}_MST_ID.npy\", MST_ID)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6c6856c3-9205-48f5-bfb2-7e0099f429a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([248, 3, 256, 256]), torch.Size([248, 3, 256, 256]))"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_images.shape, all_recons.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "9a5d42c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_recons.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f9a7162f-ca3b-4b14-9676-3037094994c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.permute(all_images, (0,2,3,1))\n",
+    "y = torch.permute(all_recons, (0,2,3,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e1a9856d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f046ec0c8d0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(y.cpu()[200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7fa41429-ab6a-4aa6-96b9-5c963016b33a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
+      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5, 2, figsize=(8, 8))\n",
+    "for row, _ in enumerate(ax):\n",
+    "    ax[row][0].imshow(x.cpu()[row])\n",
+    "    ax[row][1].imshow(y.cpu()[row])\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44793f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rt_sdxlturbo",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}