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(1).ipynb @@ -0,0 +1,1928 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7d5f265e-407a-40bd-92fb-a652091fd7ea", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "importing modules\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/rt_mindEye2/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", + "no module 'xformers'. Processing without...\n", + "no module 'xformers'. Processing without...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LOCAL RANK 0\n", + "PID of this process = 37194\n", + "device: cuda\n", + "Distributed environment: DistributedType.NO\n", + "Num processes: 1\n", + "Process index: 0\n", + "Local process index: 0\n", + "Device: cuda\n", + "\n", + "Mixed precision type: fp16\n", + "\n", + "distributed = False num_devices = 1 local rank = 0 world size = 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/vector_quantize_pytorch/vector_quantize_pytorch.py:436: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " @autocast(enabled = False)\n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/vector_quantize_pytorch/vector_quantize_pytorch.py:621: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " @autocast(enabled = False)\n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/vector_quantize_pytorch/finite_scalar_quantization.py:143: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " @autocast(enabled = False)\n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/vector_quantize_pytorch/lookup_free_quantization.py:165: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " @autocast(enabled = False)\n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/accelerate/accelerator.py:439: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n" + ] + } + ], + "source": [ + "print('importing modules')\n", + "import os\n", + "import sys\n", + "import json\n", + "import argparse\n", + "import numpy as np\n", + "import math\n", + "from einops import rearrange\n", + "import time\n", + "import random\n", + "import string\n", + "import h5py\n", + "from tqdm import tqdm\n", + "import webdataset as wds\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import transforms\n", + "from accelerate import Accelerator, DeepSpeedPlugin\n", + "\n", + "from generative_models.sgm.modules.encoders.modules import FrozenOpenCLIPImageEmbedder\n", + "from models import GNet8_Encoder\n", + "\n", + "# tf32 data type is faster than standard float32\n", + "torch.backends.cuda.matmul.allow_tf32 = True\n", + "\n", + "# custom functions #\n", + "import utils\n", + "\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\") # ['no', 'fp8', 'fp16', 'bf16']\n", + "\n", + "print(\"PID of this process =\",os.getpid())\n", + "device = accelerator.device\n", + "print(\"device:\",device)\n", + "world_size = accelerator.state.num_processes\n", + "distributed = not accelerator.state.distributed_type == 'NO'\n", + "num_devices = torch.cuda.device_count()\n", + "if num_devices==0 or not distributed: num_devices = 1\n", + "num_workers = num_devices\n", + "print(accelerator.state)\n", + "\n", + "print(\"distributed =\",distributed, \"num_devices =\", num_devices, \"local rank =\", local_rank, \"world size =\", world_size)\n", + "print = accelerator.print # only print if local_rank=0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1b8e8d9e-2931-4546-a2ce-a7417bbe21f4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/open_clip/factory.py:128: 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(checkpoint_path, map_location=map_location)\n" + ] + } + ], + "source": [ + "# Load embedding model (last hidden layer)\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 = 256\n", + "# clip_emb_dim = 1664\n", + "\n", + "from utils import CLIPEncoder\n", + "\n", + "try:\n", + " print(clip_img_embedder)\n", + "except:\n", + " clip_img_embedder = CLIPEncoder(\n", + " model_name=\"ViT-H-14\",\n", + " pretrained=\"laion2b_s32b_b79k\",\n", + " precision=\"fp32\",\n", + " batch_size=20,\n", + " device=\"cuda\"\n", + " )\n", + "\n", + "clip_seq_dim = 1\n", + "clip_emb_dim = 1024\n", + "\n", + "\n", + "## Load embedding model (last layer)\n", + "# clip_img_embedder = FrozenOpenCLIPImageEmbedder(\n", + "# arch=\"ViT-bigG-14\",\n", + "# version=\"laion2b_s39b_b160k\",\n", + "# output_tokens=False,\n", + "# only_tokens=False,\n", + "# )\n", + "# clip_img_embedder.to(device)\n", + "# clip_seq_dim = 1\n", + "# clip_emb_dim = 1280" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1ffb659a-8154-4536-ab27-2d976da1bf4e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model_name: sdxl_turbo-MST\n", + "--model_name=sdxl_turbo-MST --data_path=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2 --all_recons_path=/home/ubuntu/real_time_mindEye2/evals/sdxl_turbo-MST/sdxl_turbo-MST_all_recons.pt --eval_dir=/home/ubuntu/real_time_mindEye2/evals/sdxl_turbo-MST\n" + ] + } + ], + "source": [ + "plot_all = False\n", + "compute_circular = False # for the circular tests looking at image similarity in clip space (without any brain data involved)\n", + "saving = True\n", + "\n", + "# if running this interactively, can specify jupyter_args here for argparser to use\n", + "if utils.is_interactive():\n", + " model_name = f\"sdxl_turbo-MST\"\n", + " eval_dir = f\"/home/ubuntu/real_time_mindEye2/evals/{model_name}\" # f\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/{model_name}\"\n", + " if (\"remove\" in model_name and \"random\" in model_name) or \"ses-04\" in model_name:\n", + " all_recons_path = f\"{eval_dir}/all_recons.pt\"\n", + " elif \"paul\" in model_name:\n", + " all_recons_path = f\"evals/{model_name}/{model_name}_all_recons.pt\"\n", + " else:\n", + " all_recons_path = f\"{eval_dir}/{model_name}_all_recons.pt\" \n", + "\n", + " data_path = \"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2\"\n", + " print(\"model_name:\", model_name)\n", + "\n", + " jupyter_args = f\"--model_name={model_name} --data_path={data_path} --all_recons_path={all_recons_path} --eval_dir={eval_dir}\"\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": 4, + "id": "fb8120cd-f226-4e2c-a6c5-3cd8ef6e9bc8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "parser = argparse.ArgumentParser(description=\"Model Training Configuration\")\n", + "parser.add_argument(\n", + " \"--model_name\", type=str, default=\"testing\",\n", + " help=\"name of model, used for ckpt saving and wandb logging (if enabled)\",\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", + " \"--all_recons_path\", type=str,\n", + " help=\"Path to where all_recons.pt is stored\",\n", + ")\n", + "\n", + "parser.add_argument(\n", + " \"--eval_dir\", type=str,\n", + " help=\"Path to where evaluations should be stored\",\n", + ")\n", + "\n", + "parser.add_argument(\n", + " \"--seed\",type=int,default=42,\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", + "# seed all random functions\n", + "utils.seed_everything(seed)" + ] + }, + { + "cell_type": "markdown", + "id": "95d66b33-b327-4895-a861-ecc6ccc51296", + "metadata": { + "tags": [] + }, + "source": [ + "# Evals" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "be66f9c9-f25a-48d9-9e9a-272ab33d20ed", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([248, 3, 256, 256])\n", + "all_recons_path: /home/ubuntu/real_time_mindEye2/evals/sdxl_turbo-MST/sdxl_turbo-MST_all_recons.pt\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_37194/2292911046.py:15: 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", + " all_images = torch.load(f\"{eval_dir}/{model_name}_all_images.pt\")\n", + "/tmp/ipykernel_37194/2292911046.py:16: 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", + " all_clipvoxels = torch.load(f\"{eval_dir}/{model_name}_all_clipvoxels.pt\")\n", + "/tmp/ipykernel_37194/2292911046.py:19: 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", + " all_unrefinedrecons = torch.load(f\"{eval_dir}/{model_name}_all_recons.pt\")\n", + "/tmp/ipykernel_37194/2292911046.py:23: 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", + " all_recons = torch.load(all_recons_path)\n" + ] + } + ], + "source": [ + "eval_captions = False\n", + "if (\"remove\" in model_name and \"random\" in model_name) or \"ses-04\" in model_name:\n", + " all_images = torch.load(f\"{eval_dir}/all_images.pt\")\n", + " all_clipvoxels = torch.load(f\"{eval_dir}/all_clipvoxels.pt\")\n", + " if eval_captions:\n", + " all_predcaptions = torch.load(f\"{eval_dir}/all_predcaptions.pt\")\n", + " all_unrefinedrecons = torch.load(f\"{eval_dir}/all_recons.pt\")\n", + "elif \"ses-01\" in model_name and \"paul\" in model_name:\n", + " all_images = torch.load(f\"evals/{model_name}/{model_name}_all_images.pt\")\n", + " all_clipvoxels = torch.load(f\"evals/{model_name}/{model_name}_all_clipvoxels.pt\")\n", + " if eval_captions:\n", + " all_predcaptions = torch.load(f\"evals/{model_name}/{model_name}_all_predcaptions.pt\")\n", + " all_unrefinedrecons = torch.load(f\"evals/{model_name}/{model_name}_all_recons.pt\")\n", + "else:\n", + " all_images = torch.load(f\"{eval_dir}/{model_name}_all_images.pt\") \n", + " all_clipvoxels = torch.load(f\"{eval_dir}/{model_name}_all_clipvoxels.pt\") \n", + " if eval_captions:\n", + " all_predcaptions = torch.load(f\"{eval_dir}/{model_name}_all_predcaptions.pt\") \n", + " all_unrefinedrecons = torch.load(f\"{eval_dir}/{model_name}_all_recons.pt\") \n", + "\n", + "print(all_images.shape)\n", + "print(\"all_recons_path:\", all_recons_path)\n", + "all_recons = torch.load(all_recons_path)\n", + "\n", + "# all_blurryrecons = torch.load(f\"{eval_dir}/all_blurryrecons.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4b6c44f9-95ac-4bac-9fbf-d0b31f4f127f", + "metadata": {}, + "outputs": [], + "source": [ + "# if \"ses-01\" in model_name:\n", + "# paul_all_images = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_images.pt\").to('cpu')\n", + "# paul_all_clipvoxels = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_clipvoxels.pt\").to('cpu')\n", + "# paul_all_recons = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_recons.pt\").to('cpu')\n", + "# # paul_all_prior_out = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_prior_out.pt\").to('cpu')\n", + "# # all_images = torch.load(f\"{eval_dir}/all_images.pt\") \n", + "# print(paul_all_images.shape, all_images.shape)\n", + "# print(paul_all_clipvoxels.shape, all_clipvoxels.shape)\n", + "# print(torch.eq(paul_all_clipvoxels, all_clipvoxels))\n", + "# # assert torch.allclose(paul_all_images, all_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e8f29ac6-6561-4770-8837-8877488dce05", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# for i in range(100):\n", + "# # print(torch.allclose(paul_all_images[i], all_images[i]))\n", + "# pass" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a1382548-4b43-4101-a15b-e67b1225059c", + "metadata": {}, + "outputs": [], + "source": [ + "# num_images = paul_all_images.size(0)\n", + "# rows = 10 # Number of rows for the grid\n", + "# cols = 10 # Number of columns for the grid\n", + "\n", + "# fig, axes = plt.subplots(rows, cols * 2, figsize=(80, 40))\n", + "\n", + "# for i in range(num_images):\n", + "# row = i // cols\n", + "# col = (i % cols) * 2 # Adjust for side-by-side\n", + " \n", + "# # Plot correct image\n", + "# ax_correct = axes[row, col]\n", + "# ax_correct.imshow(paul_all_recons[i].permute(1, 2, 0).cpu().numpy())\n", + "# ax_correct.axis('off')\n", + "# ax_correct.set_title(f\"Correct {i}\")\n", + " \n", + "# # Plot modified image\n", + "# ax_modified = axes[row, col + 1]\n", + "# ax_modified.imshow(all_recons[i].permute(1, 2, 0).cpu().numpy())\n", + "# ax_modified.axis('off')\n", + "# ax_modified.set_title(f\"Modified {i}\")\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4cc551db-85c3-4696-a6fd-52b0092669eb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sdxl_turbo-MST_all_recons.pt\n", + "torch.Size([248, 3, 256, 256]) torch.Size([248, 3, 256, 256])\n" + ] + } + ], + "source": [ + "model_name_plus_suffix = all_recons_path.split('/')[-1]\n", + "print(model_name_plus_suffix)\n", + "print(all_images.shape, all_recons.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "22e933c4-1eed-48a7-a22f-84a4606ac253", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sdxl_turbo-MST (31, 2) torch.Size([62, 3, 256, 256]) torch.Size([62, 1, 1024])\n" + ] + } + ], + "source": [ + "if \"MST\" in model_name:\n", + " if (\"remove\" in model_name and \"random\" in model_name) or \"ses-04\" in model_name or \"rishab\" in model_name:\n", + " MST_ID = np.load(f\"{eval_dir}/MST_ID.npy\")\n", + " MST_pairmate_indices = np.load(f\"{eval_dir}/MST_pairmate_indices.npy\")\n", + " elif \"paul\" in model_name:\n", + " MST_ID = np.load(f\"evals/{model_name}/{model_name}_MST_ID.npy\")\n", + " MST_pairmate_indices = np.array(utils.find_paired_indices(torch.Tensor(MST_ID)))\n", + " # print(MST_pairmate_indices)\n", + " else:\n", + " MST_ID = np.load(f\"{eval_dir}/{model_name}_MST_ID.npy\") \n", + " MST_pairmate_indices = np.load(f\"{eval_dir}/MST_pairmate_indices.npy\") \n", + "\n", + " # pairs = utils.find_paired_indices(torch.Tensor(MST_ID))\n", + " # if \"close_to_MST\" in model_name or (\"remove\" in model_name and \"random\" in model_name) or \"ses-0\" in model_name:\n", + " # pairs = np.array(pairs[:-1]) # index out the placeholder\n", + " # pairs = np.array(pairs)\n", + " # if \"ses-0\" in model_name:\n", + " # if \"ses-01\" in model_name or \"ses-04\" in model_name:\n", + " # print(pairs.shape)\n", + " # assert pairs.shape == (49,2)\n", + " # else:\n", + " # assert pairs.shape == (50,3)\n", + " # else:\n", + " # assert pairs.shape == (100,3)\n", + " # print(pairs)\n", + " # repeats_in_test = torch.load(f\"{eval_dir}/repeats_in_test.pt\")\n", + " # test_image_indices = torch.load(f\"{eval_dir}/test_image_indices.pt\")\n", + " all_unique_images = all_images[MST_pairmate_indices.flatten()]\n", + " all_unique_clipvoxels = all_clipvoxels[MST_pairmate_indices.flatten()]\n", + "\n", + " print(model_name, MST_pairmate_indices.shape, all_unique_images.shape, all_unique_clipvoxels.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "880081e4-7567-4b1e-acb0-4af863018228", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# visualize all unique images\n", + "if plot_all:\n", + " # Plot all the MST images and pairmates\n", + " import textwrap\n", + " def wrap_title(title, wrap_width):\n", + " return \"\\n\".join(textwrap.wrap(title, wrap_width))\n", + "\n", + " size = int(np.ceil(MST_pairmate_indices.shape[0]/2)) # helps determine size of plot\n", + " fig, axes = plt.subplots(size, 4, figsize=(15, size*4))\n", + " jj=-1; kk=0;\n", + " for i, j in enumerate(all_unique_images):\n", + " jj+=1\n", + " axes[kk][jj].imshow(utils.torch_to_Image(j))\n", + " axes[kk][jj].axis('off')\n", + " if jj==3: \n", + " kk+=1; jj=-1\n", + "\n", + " fig.tight_layout()\n", + " # plt.savefig('figures/MST_2_pairmates_10-01')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "48c8772b-871a-4031-b013-d7159bf8b74a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# if plot_all:\n", + "# # create full grid of recon comparisons\n", + "# from PIL import Image\n", + "\n", + "# imsize = 150\n", + "# if all_images.shape[-1] != imsize:\n", + "# all_images = transforms.Resize((imsize,imsize))(all_images).float()\n", + "# if all_recons.shape[-1] != imsize:\n", + "# all_recons = transforms.Resize((imsize,imsize))(all_recons).float()\n", + "\n", + "# num_images = all_recons.shape[0]\n", + "# num_rows = (2 * num_images + 9) // 10\n", + "\n", + "# # Interleave tensors\n", + "# merged = torch.stack([val for pair in zip(all_images, all_recons) for val in pair], dim=0)\n", + "\n", + "# # Calculate grid size\n", + "# grid = torch.zeros((num_rows * 10, 3, all_recons.shape[-1], all_recons.shape[-1]))\n", + "\n", + "# # Populate the grid\n", + "# grid[:2*num_images] = merged\n", + "# grid_images = [transforms.functional.to_pil_image(grid[i]) for i in range(num_rows * 10)]\n", + "\n", + "# # Create the grid image\n", + "# grid_image = Image.new('RGB', (all_recons.shape[-1]*10, all_recons.shape[-1] * num_rows)) # 10 images wide\n", + "\n", + "# # Paste images into the grid\n", + "# for i, img in enumerate(grid_images):\n", + "# grid_image.paste(img, (all_recons.shape[-1] * (i % 10), all_recons.shape[-1] * (i // 10)))\n", + "# grid_image\n", + "# # grid_image.save(f\"{model_name_plus_suffix[:-3]}_1000recons.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f42009e9-f910-4f02-8db6-d46778aa6595", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "imsize = 256\n", + "if all_images.shape[-1] != imsize:\n", + " all_images = transforms.Resize((imsize,imsize))(all_images).float()\n", + "if all_recons.shape[-1] != imsize:\n", + " all_recons = transforms.Resize((imsize,imsize))(all_recons).float()\n", + "try:\n", + " if all_blurryrecons.shape[-1] != imsize:\n", + " all_blurryrecons = transforms.Resize((imsize,imsize))(all_blurryrecons).float()\n", + "except:\n", + " pass\n", + "\n", + "if \"enhanced\" in model_name_plus_suffix:\n", + " try:\n", + " all_recons = all_recons*.75 + all_blurryrecons*.25\n", + " print(\"weighted averaging to improve low-level evals\")\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "06e23174-a777-4dd1-8b73-6783587d8f9c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# visualize some images with recons and captions\n", + "if plot_all:\n", + " assert np.all(all_images.shape == all_recons.shape)\n", + " import textwrap\n", + " def wrap_title(title, wrap_width):\n", + " return \"\\n\".join(textwrap.wrap(title, wrap_width))\n", + "\n", + " fig, axes = plt.subplots(3, 4, figsize=(10, 8))\n", + " jj=-1; kk=0;\n", + " for j in np.array([0,1,2,3,4,5]):\n", + " jj+=1\n", + " # print(kk,jj)\n", + " axes[kk][jj].imshow(utils.torch_to_Image(all_images[j]))\n", + " axes[kk][jj].axis('off')\n", + " jj+=1\n", + " axes[kk][jj].imshow(utils.torch_to_Image(all_recons[j]))\n", + " axes[kk][jj].axis('off')\n", + " axes[kk][jj].set_title(wrap_title(str(all_predcaptions[[j]]),wrap_width=30), fontsize=8)\n", + " if jj==3: \n", + " kk+=1; jj=-1\n", + "\n", + " fig.tight_layout()\n", + " # plt.savefig('figures/recon_09-26')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5b4deb53-4d85-4292-92c5-bb59077523cf", + "metadata": {}, + "source": [ + "# Retrieval eval (chance = 1/100)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2e49d57a-9b65-490c-a1e0-61bd05682171", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_37194/606217879.py:10: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with torch.cuda.amp.autocast(dtype=torch.float16):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "overall fwd percent_correct: 0.0968\n", + "overall bwd percent_correct: 0.0806\n" + ] + } + ], + "source": [ + "from scipy import stats\n", + "\n", + "all_fwd_acc = []\n", + "all_bwd_acc = []\n", + "\n", + "assert len(all_unique_images) == len(all_unique_clipvoxels) \n", + "\n", + "all_percent_correct_fwds, all_percent_correct_bwds = [], []\n", + "\n", + "with torch.cuda.amp.autocast(dtype=torch.float16):\n", + " all_unique_images_resized = transforms.Resize((224,224))(all_unique_images).float() # embedder requires this shape\n", + " all_emb = clip_img_embedder(all_unique_images_resized.to(device)).float() # CLIP-Image\n", + " all_emb_ = all_unique_clipvoxels # CLIP-Brain\n", + "\n", + " # flatten if necessary\n", + " all_emb = all_emb.reshape(len(all_emb),-1).to(device)\n", + " all_emb_ = all_emb_.reshape(len(all_emb_),-1).to(device)\n", + "\n", + " # l2norm \n", + " all_emb = nn.functional.normalize(all_emb,dim=-1)\n", + " all_emb_ = nn.functional.normalize(all_emb_,dim=-1)\n", + "\n", + " all_labels = torch.arange(len(all_emb)).to(device)\n", + " all_bwd_sim = utils.batchwise_cosine_similarity(all_emb,all_emb_) # clip, brain\n", + " all_fwd_sim = utils.batchwise_cosine_similarity(all_emb_,all_emb) # brain, clip\n", + "\n", + " # if \"ses-0\" not in model_name or \"ses-01\" in model_name or \"ses-04\" in model_name:\n", + " # assert len(all_fwd_sim) == 100\n", + " # assert len(all_bwd_sim) == 100\n", + " # else:\n", + " # assert len(all_fwd_sim) == 50\n", + " # assert len(all_bwd_sim) == 50\n", + " \n", + " all_percent_correct_fwds = utils.topk(all_fwd_sim, all_labels, k=1).item()\n", + " all_percent_correct_bwds = utils.topk(all_bwd_sim, all_labels, k=1).item()\n", + "\n", + "all_fwd_acc.append(all_percent_correct_fwds)\n", + "all_bwd_acc.append(all_percent_correct_bwds)\n", + "\n", + "all_fwd_sim = np.array(all_fwd_sim.cpu())\n", + "all_bwd_sim = np.array(all_bwd_sim.cpu())\n", + "\n", + "print(f\"overall fwd percent_correct: {all_fwd_acc[0]:.4f}\")\n", + "print(f\"overall bwd percent_correct: {all_bwd_acc[0]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1a4cda50-ef4a-43d0-9c2e-53ee8509482d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_37194/2295493801.py:23: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with torch.cuda.amp.autocast(dtype=torch.float16):\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "The size of tensor a (325) must match the size of tensor b (257) at non-singleton dimension 1", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 24\u001b[39m\n\u001b[32m 20\u001b[39m all_unique_clipvoxels_half = all_unique_clipvoxels[\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(all_unique_clipvoxels)/\u001b[32m2\u001b[39m):]\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.cuda.amp.autocast(dtype=torch.float16):\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m emb = \u001b[43mclip_img_embedder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_unique_images_half\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m.float() \u001b[38;5;66;03m# CLIP-Image\u001b[39;00m\n\u001b[32m 25\u001b[39m emb_ = all_unique_clipvoxels_half \u001b[38;5;66;03m# CLIP-Brain\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# flatten if necessary\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/real_time_mindEye2/utils.py:1151\u001b[39m, in \u001b[36mCLIPEncoder.__call__\u001b[39m\u001b[34m(self, image)\u001b[39m\n\u001b[32m 1150\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, image):\n\u001b[32m-> \u001b[39m\u001b[32m1151\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mencode_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m.unsqueeze(\u001b[32m1\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/real_time_mindEye2/utils.py:1142\u001b[39m, in \u001b[36mCLIPEncoder.encode_image\u001b[39m\u001b[34m(self, image, verbose)\u001b[39m\n\u001b[32m 1140\u001b[39m batch_images = torch.stack(batch_images)\n\u001b[32m 1141\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.no_grad():\n\u001b[32m-> \u001b[39m\u001b[32m1142\u001b[39m batch_features = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencode_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1143\u001b[39m \u001b[43m \u001b[49m\u001b[43mbatch_images\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1144\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1145\u001b[39m features.append(batch_features)\n\u001b[32m 1147\u001b[39m features = torch.cat(features, dim=\u001b[32m0\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rt_mindEye2/lib/python3.11/site-packages/open_clip/model.py:266\u001b[39m, in \u001b[36mCLIP.encode_image\u001b[39m\u001b[34m(self, image, normalize)\u001b[39m\n\u001b[32m 265\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mencode_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image, normalize: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m266\u001b[39m features = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvisual\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 267\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m F.normalize(features, dim=-\u001b[32m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m normalize \u001b[38;5;28;01melse\u001b[39;00m features\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rt_mindEye2/lib/python3.11/site-packages/torch/nn/modules/module.py:1736\u001b[39m, in \u001b[36mModule._wrapped_call_impl\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1734\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._compiled_call_impl(*args, **kwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[32m 1735\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1736\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rt_mindEye2/lib/python3.11/site-packages/torch/nn/modules/module.py:1747\u001b[39m, in \u001b[36mModule._call_impl\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1742\u001b[39m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[32m 1743\u001b[39m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[32m 1744\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m._backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._forward_pre_hooks\n\u001b[32m 1745\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[32m 1746\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[32m-> \u001b[39m\u001b[32m1747\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1749\u001b[39m result = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1750\u001b[39m called_always_called_hooks = \u001b[38;5;28mset\u001b[39m()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rt_mindEye2/lib/python3.11/site-packages/open_clip/transformer.py:510\u001b[39m, in \u001b[36mVisionTransformer.forward\u001b[39m\u001b[34m(self, x)\u001b[39m\n\u001b[32m 508\u001b[39m x = torch.cat([_expand_token(\u001b[38;5;28mself\u001b[39m.class_embedding, x.shape[\u001b[32m0\u001b[39m]).to(x.dtype), x], dim=\u001b[32m1\u001b[39m)\n\u001b[32m 509\u001b[39m \u001b[38;5;66;03m# shape = [*, grid ** 2 + 1, width]\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m510\u001b[39m x = \u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpositional_embedding\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 512\u001b[39m x = \u001b[38;5;28mself\u001b[39m.patch_dropout(x)\n\u001b[32m 513\u001b[39m x = \u001b[38;5;28mself\u001b[39m.ln_pre(x)\n", + "\u001b[31mRuntimeError\u001b[39m: The size of tensor a (325) must match the size of tensor b (257) at non-singleton dimension 1" + ] + } + ], + "source": [ + "if \"ses-0\" not in model_name:\n", + " from scipy import stats\n", + "\n", + " fwd_acc = []\n", + " bwd_acc = []\n", + " fwd_sim_halves = []\n", + " bwd_sim_halves = []\n", + "\n", + " assert len(all_unique_images) == len(all_unique_clipvoxels) \n", + "\n", + " for i in range(2): # since this is a 2-session model, we evaluate on the test set corresponding to each session and report both separately for better comparison to single-session models\n", + " percent_correct_fwds, percent_correct_bwds = [], []\n", + " # percent_correct_fwd, percent_correct_bwd = None, None\n", + "\n", + " if i==0: \n", + " all_unique_images_half = all_unique_images[:int(len(all_unique_images)/2)]\n", + " all_unique_clipvoxels_half = all_unique_clipvoxels[:int(len(all_unique_clipvoxels)/2)]\n", + " elif i==1:\n", + " all_unique_images_half = all_unique_images[int(len(all_unique_images)/2):]\n", + " all_unique_clipvoxels_half = all_unique_clipvoxels[int(len(all_unique_clipvoxels)/2):]\n", + "\n", + "\n", + " with torch.cuda.amp.autocast(dtype=torch.float16):\n", + " emb = clip_img_embedder(all_unique_images_half.to(device)).float() # CLIP-Image\n", + " emb_ = all_unique_clipvoxels_half # CLIP-Brain\n", + "\n", + " # flatten if necessary\n", + " emb = emb.reshape(len(emb),-1).to(device)\n", + " emb_ = emb_.reshape(len(emb_),-1).to(device)\n", + "\n", + " # l2norm \n", + " emb = nn.functional.normalize(emb,dim=-1)\n", + " emb_ = nn.functional.normalize(emb_,dim=-1)\n", + "\n", + " labels = torch.arange(len(emb)).to(device)\n", + " bwd_sim = utils.batchwise_cosine_similarity(emb,emb_) # clip, brain\n", + " fwd_sim = utils.batchwise_cosine_similarity(emb_,emb) # brain, clip\n", + "\n", + " assert len(fwd_sim) == 50\n", + " assert len(bwd_sim) == 50\n", + "\n", + " # percent_correct_fwds = np.append(percent_correct_fwds, utils.topk(fwd_sim, labels, k=1).item())\n", + " # percent_correct_bwds = np.append(percent_correct_bwds, utils.topk(bwd_sim, labels, k=1).item())\n", + " percent_correct_fwds = utils.topk(fwd_sim, labels, k=1).item()\n", + " percent_correct_bwds = utils.topk(bwd_sim, labels, k=1).item()\n", + "\n", + " # percent_correct_fwd = np.mean(percent_correct_fwds)\n", + " # fwd_sd = np.std(percent_correct_fwds) / np.sqrt(len(percent_correct_fwds))\n", + " # fwd_ci = stats.norm.interval(0.95, loc=percent_correct_fwd, scale=fwd_sd)\n", + "\n", + " # percent_correct_bwd = np.mean(percent_correct_bwds)\n", + " # bwd_sd = np.std(percent_correct_bwds) / np.sqrt(len(percent_correct_bwds))\n", + " # bwd_ci = stats.norm.interval(0.95, loc=percent_correct_bwd, scale=bwd_sd)\n", + "\n", + " fwd_acc.append(percent_correct_fwds)\n", + " bwd_acc.append(percent_correct_bwds)\n", + "\n", + " fwd_sim = np.array(fwd_sim.cpu())\n", + " bwd_sim = np.array(bwd_sim.cpu())\n", + " fwd_sim_halves.append(fwd_sim)\n", + " bwd_sim_halves.append(bwd_sim)\n", + "\n", + " print(f\"ses-02 fwd percent_correct: {fwd_acc[0]:.4f}; ses-03 fwd percent_correct: {fwd_acc[1]:.4f}\")\n", + " print(f\"ses-02 bwd percent_correct: {bwd_acc[0]:.4f}; ses-03 bwd percent_correct: {bwd_acc[1]:.4f} \")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "40d43a70-e1db-43e5-ae84-40eb1577cf01", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if compute_circular:\n", + " if \"ses-0\" not in model_name: # we're in a multisession model, assumed ses-02 and ses-03 for now\n", + " fwd_acc_circular = []\n", + " fwd_sim_halves_circular = []\n", + "\n", + " assert len(all_unique_images) == len(all_unique_clipvoxels) \n", + "\n", + " for i in range(2): # since this is a 2-session model, we evaluate on the test set corresponding to each session and report both separately for better comparison to single-session models\n", + " percent_correct_fwds_circular = []\n", + " # percent_correct_fwd_circular = None\n", + "\n", + " if i==0: \n", + " all_unique_images_half_circular = all_unique_images[:int(len(all_unique_images)/2)]\n", + " all_unique_clipvoxels_half_circular = all_unique_clipvoxels[:int(len(all_unique_clipvoxels)/2)]\n", + " elif i==1:\n", + " all_unique_images_half_circular = all_unique_images[int(len(all_unique_images)/2):]\n", + " all_unique_clipvoxels_half_circular = all_unique_clipvoxels[int(len(all_unique_clipvoxels)/2):]\n", + "\n", + " with torch.cuda.amp.autocast(dtype=torch.float16):\n", + " emb_circular = clip_img_embedder(all_unique_images_half_circular.to(device)).float() # CLIP-Image\n", + "\n", + " # flatten if necessary\n", + " emb_circular = emb_circular.reshape(len(emb_circular),-1).to(device)\n", + "\n", + " # l2norm \n", + " emb_circular = nn.functional.normalize(emb_circular,dim=-1)\n", + "\n", + " labels_circular = torch.arange(len(emb_circular)).to(device)\n", + " fwd_sim_circular = utils.batchwise_cosine_similarity(emb_circular,emb_circular) # clip, clip\n", + "\n", + "\n", + " if \"ses-0\" in model_name:\n", + " assert len(fwd_sim_circular) == 25\n", + " else:\n", + " assert len(fwd_sim_circular) == 50\n", + "\n", + " # percent_correct_fwds_circular = np.append(percent_correct_fwds_circular, utils.topk(fwd_sim_circular, labels_circular, k=1).item())\n", + " percent_correct_fwds_circular = utils.topk(fwd_sim_circular, labels_circular, k=1).item()\n", + "\n", + "\n", + " # percent_correct_fwd_circular = np.mean(percent_correct_fwds_circular)\n", + " # fwd_sd_circular = np.std(percent_correct_fwds_circular) / np.sqrt(len(percent_correct_fwds_circular))\n", + " # fwd_ci_circular = stats.norm.interval(0.95, loc=percent_correct_fwd_circular, scale=fwd_sd_circular)\n", + "\n", + " fwd_acc_circular.append(percent_correct_fwds_circular)\n", + "\n", + " fwd_sim_circular = np.array(fwd_sim_circular.cpu())\n", + " fwd_sim_halves_circular.append(fwd_sim_circular)\n", + "\n", + " print(f\"ses-02 fwd percent_correct: {fwd_acc_circular[0]:.4f}; ses-03 fwd percent_correct: {fwd_acc_circular[1]:.4f}\")\n", + " \n", + " else: # single session model\n", + " fwd_acc_circular = []\n", + "\n", + " assert len(all_unique_images) == len(all_unique_clipvoxels) \n", + "\n", + " percent_correct_fwds_circular = []\n", + " # percent_correct_fwd_circular = None\n", + "\n", + " with torch.cuda.amp.autocast(dtype=torch.float16):\n", + " emb_circular = clip_img_embedder(all_unique_images.to(device)).float() # CLIP-Image\n", + "\n", + " # flatten if necessary\n", + " emb_circular = emb_circular.reshape(len(emb_circular),-1).to(device)\n", + "\n", + " # l2norm \n", + " emb_circular = nn.functional.normalize(emb_circular,dim=-1)\n", + "\n", + " labels_circular = torch.arange(len(emb_circular)).to(device)\n", + " fwd_sim_circular = utils.batchwise_cosine_similarity(emb_circular,emb_circular) # clip, clip\n", + "\n", + "\n", + " if \"ses-01\" in model_name:\n", + " assert len(fwd_sim_circular) == 100\n", + " else:\n", + " assert len(fwd_sim_circular) == 50\n", + "\n", + " # percent_correct_fwds_circular = np.append(percent_correct_fwds_circular, utils.topk(fwd_sim_circular, labels_circular, k=1).item())\n", + " percent_correct_fwds_circular = utils.topk(fwd_sim_circular, labels_circular, k=1).item()\n", + "\n", + "\n", + " # percent_correct_fwd_circular = np.mean(percent_correct_fwds_circular)\n", + " # fwd_sd_circular = np.std(percent_correct_fwds_circular) / np.sqrt(len(percent_correct_fwds_circular))\n", + " # fwd_ci_circular = stats.norm.interval(0.95, loc=percent_correct_fwd_circular, scale=fwd_sd_circular)\n", + "\n", + " fwd_acc_circular.append(percent_correct_fwds_circular)\n", + "\n", + " fwd_sim_circular = np.array(fwd_sim_circular.cpu())\n", + "\n", + " print(f\"session fwd percent_correct (circular): {fwd_acc_circular[0]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d086c99a-c712-4ac7-b625-a0656c9ee886", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if compute_circular:\n", + " # print(utils.topk(torch.Tensor(fwd_sim_halves[1]).to(device), labels, k=1).item())\n", + " # ses02_top1 = torch.argsort(torch.Tensor(fwd_sim_halves[0]).to(device),axis=1)[:,-1] == labels # from utils.topk()\n", + " # ses03_top1 = torch.argsort(torch.Tensor(fwd_sim_halves[1]).to(device),axis=1)[:,-1] == labels\n", + " # top1_results = torch.cat((ses02_top1, ses03_top1))\n", + " # incorrect_idx = torch.argwhere(top1_results == False)[:,0]\n", + " # print(incorrect_idx)\n", + "\n", + " # confirm that the lure is behind the target 80% of the time in CLIP last hidden layer embeddings\n", + "\n", + " use_fwd_sim = False\n", + " use_first_half = False\n", + "\n", + " all_top_sims = [] # len(fwd_sim_halves[0]); for each image, contains the similarity to the top-n choices until it gets the correct answer. If len 1, top-1 is correct\n", + " all_pairmate_sims = [] # len(fwd_sim_halves[0]); the similarity of each image to its pairmate \n", + " all_chose_lures = [] # len(fwd_sim_halves[0]); True for each top-n choice if the lure was predicted to be more similar to the target \n", + " if \"ses-0\" not in model_name:\n", + " sim_halves = fwd_sim_halves if use_fwd_sim else bwd_sim_halves\n", + " sim_halves = sim_halves[0] if use_first_half else sim_halves[1]\n", + " else:\n", + " sim_halves = all_fwd_sim if use_fwd_sim else all_bwd_sim\n", + " for i, img in enumerate(sim_halves):\n", + " if i%2==0:\n", + " idx_to_pairmate = 1\n", + " elif i%2==1:\n", + " idx_to_pairmate = -1\n", + "\n", + " order = img.argsort()[::-1]\n", + " # print(order)\n", + " top_sim = []\n", + " chose_lure = []\n", + " for idx in order:\n", + " sim = img[idx]\n", + " pairmate_sim = img[i+idx_to_pairmate]\n", + " top_sim.append(sim) \n", + " chose_lure.append((idx, sim <= pairmate_sim))\n", + " # print(i, idx, img[idx], img[i+idx_to_pairmate])\n", + " if idx == i:\n", + " break\n", + "\n", + " all_top_sims.append(top_sim)\n", + " all_pairmate_sims.append(pairmate_sim)\n", + " all_chose_lures.append(chose_lure)\n", + "\n", + " # print(all_top_sims)\n", + " # print()\n", + " # print(all_pairmate_sims)\n", + " # print()\n", + " # print(all_chose_lures)\n", + "\n", + " where_chose_pairmate = []\n", + " for idx, i in enumerate(all_chose_lures):\n", + " for value in i:\n", + " # print(value[1])\n", + " if value[1] == True:\n", + " # print(idx, i, end='\\n')\n", + " where_chose_pairmate.append(idx)\n", + " break\n", + "\n", + " # where_chose_pairmate # trials where the pairmate was chosen ahead of the target" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a5c4405c-b3b7-42fe-b622-f4cb81500464", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# top-n predictions using CLIP brain embeddings\n", + "\n", + "if plot_all:\n", + " use_fwd_sim = True\n", + " top_n = 10 # how many of the top n images to display\n", + " print(\"Given Brain embedding, find correct Image embedding\")\n", + " fig, ax = plt.subplots(nrows=len(all_unique_images), ncols=top_n+1, figsize=(top_n*2,len(all_unique_images)*2))\n", + " for trial in range(len(all_unique_images)):\n", + " ax[trial, 0].imshow(utils.torch_to_Image(all_unique_images[trial]))\n", + " ax[trial, 0].set_title(\"original\\nimage\")\n", + " ax[trial, 0].axis(\"off\")\n", + " for attempt in range(top_n):\n", + " if trial < 50:\n", + " if \"ses-0\" not in model_name:\n", + " sim_half = fwd_sim_halves[0] if use_fwd_sim else bwd_sim_halves[0]\n", + " unique_imgs_to_plot = all_unique_images[:int(len(all_unique_images)/2)]\n", + " # unique_clipvoxels_to_plot = all_unique_clipvoxels[:int(len(all_unique_clipvoxels)/2)]\n", + " else:\n", + " sim_half = all_fwd_sim if use_fwd_sim else all_bwd_sim\n", + " unique_imgs_to_plot = all_unique_images\n", + " # unique_clipvoxels_to_plot = all_unique_clipvoxels\n", + " which = np.flip(np.argsort(sim_half[trial]))[attempt]\n", + "\n", + " elif trial >= 50:\n", + " if \"ses-0\" not in model_name:\n", + " sim_halves = fwd_sim_halves[1] if use_fwd_sim else bwd_sim_halves[1]\n", + " unique_imgs_to_plot = all_unique_images[int(len(all_unique_images)/2):]\n", + " # unique_clipvoxels_to_plot = all_unique_clipvoxels[int(len(all_unique_clipvoxels)/2):]\n", + " else:\n", + " sim_halves = all_fwd_sim if use_fwd_sim else all_bwd_sim\n", + " unique_imgs_to_plot = all_unique_images\n", + " # unique_clipvoxels_to_plot = all_unique_clipvoxels\n", + " which = np.flip(np.argsort(sim_half[trial-50]))[attempt]\n", + "\n", + " ax[trial, attempt+1].imshow(utils.torch_to_Image(unique_imgs_to_plot[which]))\n", + " ax[trial, attempt+1].set_title(f\"Top {attempt+1}\")\n", + " ax[trial, attempt+1].axis(\"off\")\n", + " fig.tight_layout()\n", + " # plt.savefig('figures/retrieval_top10')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "05df72ce-3cdb-4ad2-a790-0835a41fb0f6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# similarity of each unique MST image to all others using CLIP image embeddings only (top-1 is guaranteed to be correct)\n", + "# uses last hidden layer (which may not match as well as the last layer to human semantic judgments)\n", + "if plot_all and compute_circular:\n", + " print(\"Given Brain embedding, find correct Image embedding\")\n", + " top_n = 10 # how many of the top n images to display\n", + " fig, ax = plt.subplots(nrows=len(all_unique_images), ncols=top_n+1, figsize=(top_n*2,len(all_unique_images)*2))\n", + " for trial in range(len(all_unique_images)):\n", + " ax[trial, 0].imshow(utils.torch_to_Image(all_unique_images[trial]))\n", + " ax[trial, 0].set_title(\"original\\nimage\")\n", + " ax[trial, 0].axis(\"off\")\n", + " for attempt in range(10):\n", + " if trial < 50:\n", + " if \"ses-0\" not in model_name:\n", + " sim_half_circular = fwd_sim_halves_circular[0]\n", + " unique_imgs_to_plot_circular = all_unique_images[:int(len(all_unique_images)/2)]\n", + " else:\n", + " sim_half_circular = fwd_sim_circular\n", + " unique_imgs_to_plot_circular = all_unique_images\n", + " which_circular = np.flip(np.argsort(sim_half_circular[trial]))[attempt]\n", + "\n", + " elif trial >= 50:\n", + " if \"ses-0\" not in model_name:\n", + " sim_halves_circular = fwd_sim_halves_circular[1]\n", + " unique_imgs_to_plot_circular = all_unique_images[int(len(all_unique_images)/2):]\n", + " else:\n", + " sim_halves_circular = all_fwd_sim_circular\n", + " unique_imgs_to_plot_circular = all_unique_images\n", + " which_circular = np.flip(np.argsort(sim_half_circular[trial-50]))[attempt]\n", + "\n", + " ax[trial, attempt+1].imshow(utils.torch_to_Image(unique_imgs_to_plot_circular[which_circular]))\n", + " ax[trial, attempt+1].set_title(f\"Top {attempt+1}\")\n", + " ax[trial, attempt+1].axis(\"off\")\n", + " fig.tight_layout()\n", + " # plt.savefig('figures/circular_top10')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0d404dec-5336-45cf-8932-833e895a9ebe", + "metadata": { + "tags": [] + }, + "source": [ + "## MST Paired Retrieval (chance = 50%)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "06667f7d-a356-4c30-8e1b-06ebd7ff1752", + "metadata": {}, + "outputs": [], + "source": [ + "if compute_circular:\n", + " all_top_sims_circular = [] # len(fwd_sim_halves[0]); for each image, contains the similarity to the top-n choices until it gets the correct answer. If len 1, top-1 is correct\n", + " all_pairmate_sims_circular = [] # len(fwd_sim_halves[0]); the similarity of each image to its pairmate \n", + " all_chose_lures_circular = [] # len(fwd_sim_halves[0]); True for each top-n choice if the lure was predicted to be more similar to the target \n", + "\n", + " if \"ses-0\" not in model_name:\n", + " first_half = True\n", + " sim_halves_circular = fwd_sim_halves_circular\n", + " sim_halves_circular = sim_halves_circular[0] if use_first_half else sim_halves_circular[1]\n", + " else:\n", + " sim_halves_circular = all_fwd_sim_circular\n", + " for i, img in enumerate(sim_halves_circular):\n", + " if i%2==0:\n", + " idx_to_pairmate = 1\n", + " elif i%2==1:\n", + " idx_to_pairmate = -1\n", + "\n", + " order_circular = img.argsort()[::-1]\n", + " # print(order)\n", + " top_sim_circular = []\n", + " chose_lure_circular = []\n", + " for idx in order_circular:\n", + " sim_circular = img[idx]\n", + " pairmate_sim_circular = img[i+idx_to_pairmate]\n", + " top_sim_circular.append(sim_circular) \n", + " chose_lure_circular.append((idx, sim_circular <= pairmate_sim_circular))\n", + " # print(i, idx, img[idx], img[i+idx_to_pairmate])\n", + " if idx == i:\n", + " break\n", + "\n", + " all_top_sims_circular.append(top_sim_circular)\n", + " all_pairmate_sims_circular.append(pairmate_sim_circular)\n", + " all_chose_lures_circular.append(chose_lure_circular)\n", + "\n", + " bot_half = (all_pairmate_sims_circular < np.median(all_pairmate_sims_circular))[::2] # every other one since the sims are symmetric\n", + " top_half = (all_pairmate_sims_circular > np.median(all_pairmate_sims_circular))[::2]\n", + "\n", + " binary_acc = []\n", + " for i,(a,b) in enumerate(tqdm(MST_pairmate_indices,total=len(MST_pairmate_indices))):\n", + " # print(i,a,b)\n", + " with torch.no_grad():\n", + " with torch.cuda.amp.autocast():\n", + " emb_a = nn.functional.normalize(clip_img_embedder(all_images[[a]].to(device)).float().flatten(1),dim=-1)\n", + " emb_b = nn.functional.normalize(clip_img_embedder(all_images[[b]].to(device)).float().flatten(1),dim=-1)\n", + " emb_v = nn.functional.normalize(all_clipvoxels[[a]].flatten(1),dim=-1).to(device)\n", + "\n", + " a_sim = utils.pairwise_cosine_similarity(emb_v, emb_a).item()\n", + " b_sim = utils.pairwise_cosine_similarity(emb_v, emb_b).item()\n", + "\n", + " binary_acc.append(a_sim > b_sim)\n", + "\n", + " with torch.no_grad():\n", + " with torch.cuda.amp.autocast():\n", + " emb_a = nn.functional.normalize(clip_img_embedder(all_images[[a]].to(device)).float().flatten(1),dim=-1)\n", + " emb_b = nn.functional.normalize(clip_img_embedder(all_images[[b]].to(device)).float().flatten(1),dim=-1)\n", + " emb_v = nn.functional.normalize(all_clipvoxels[[b]].flatten(1),dim=-1).to(device)\n", + "\n", + " a_sim = utils.pairwise_cosine_similarity(emb_v, emb_a).item()\n", + " b_sim = utils.pairwise_cosine_similarity(emb_v, emb_b).item()\n", + "\n", + " binary_acc.append(a_sim < b_sim)\n", + "\n", + " assert len(binary_acc) == 50\n", + " mst_score = np.mean(binary_acc)\n", + " print(f\"session score: {np.mean(binary_acc):.4f} ± {np.std(binary_acc):.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3468021c-660d-4205-8e5d-d75f6f3c2881", + "metadata": {}, + "outputs": [], + "source": [ + "# test = np.sort(MST_pairmate_indices, axis=1)\n", + "# test" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a032c4cc-4cf9-4b33-ac02-0e569f7757be", + "metadata": {}, + "outputs": [], + "source": [ + "# model_name" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "130bb4d9-b7f1-40a4-a3e2-7e3699c69c49", + "metadata": {}, + "outputs": [], + "source": [ + "# paul_all_images = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_images.pt\").to('cpu')\n", + "# paul_all_clipvoxels = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_clipvoxels.pt\").to('cpu')\n", + "# paul_all_recons = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_recons.pt\").to('cpu')\n", + "# paul_all_prior_out = torch.load(f\"evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_all_prior_out.pt\").to('cpu')\n", + "# print(paul_all_images.shape, all_images.shape)\n", + "# # assert torch.eq(paul_all_images, all_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "698ffc2c-5653-49d3-9d16-38a32f9e7f6b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# print(paul_all_images.shape, all_images.shape)\n", + "# torch.eq(paul_all_images, all_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "23f26846-3170-4c91-a8e3-f98322e90dc0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assuming ses-02+ses-03 multisession model\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/15 [00:00 \u001b[39m\u001b[32m36\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(binary_acc) == \u001b[32m50\u001b[39m \u001b[38;5;66;03m# don't want to average across both sessions; make sure it resets\u001b[39;00m\n\u001b[32m 37\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mses-0\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhalf+\u001b[32m2\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp.mean(binary_acc)\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m ± \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp.std(binary_acc)\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 38\u001b[39m mst_score.append((np.mean(binary_acc),np.std(binary_acc)))\n", + "\u001b[31mAssertionError\u001b[39m: " + ] + } + ], + "source": [ + "all_images_resized = transforms.Resize((224,224))(all_images).float()\n", + "if \"MST\" in model_name:\n", + " if \"ses-0\" not in model_name:\n", + " print('assuming ses-02+ses-03 multisession model')\n", + " mst_score = []\n", + " for half in range(2):\n", + " binary_acc = []\n", + " if half==0:\n", + " MST_pairmate_indices_half = MST_pairmate_indices[:int(len(MST_pairmate_indices)/2)]\n", + " elif half==1:\n", + " MST_pairmate_indices_half = MST_pairmate_indices[int(len(MST_pairmate_indices)/2):]\n", + " for i,(a,b) in enumerate(tqdm(MST_pairmate_indices_half,total=len(MST_pairmate_indices_half))):\n", + " # print(i,a,b)\n", + " with torch.no_grad():\n", + " with torch.cuda.amp.autocast():\n", + " emb_a = nn.functional.normalize(clip_img_embedder(all_images_resized[[a]].to(device)).float().flatten(1),dim=-1)\n", + " emb_b = nn.functional.normalize(clip_img_embedder(all_images_resized[[b]].to(device)).float().flatten(1),dim=-1)\n", + " emb_v = nn.functional.normalize(all_clipvoxels[[a]].flatten(1),dim=-1).to(device)\n", + "\n", + " a_sim = utils.pairwise_cosine_similarity(emb_v, emb_a).item()\n", + " b_sim = utils.pairwise_cosine_similarity(emb_v, emb_b).item()\n", + "\n", + " binary_acc.append(a_sim > b_sim)\n", + "\n", + " with torch.no_grad():\n", + " with torch.cuda.amp.autocast():\n", + " emb_a = nn.functional.normalize(clip_img_embedder(all_images_resized[[a]].to(device)).float().flatten(1),dim=-1)\n", + " emb_b = nn.functional.normalize(clip_img_embedder(all_images_resized[[b]].to(device)).float().flatten(1),dim=-1)\n", + " emb_v = nn.functional.normalize(all_clipvoxels[[b]].flatten(1),dim=-1).to(device)\n", + "\n", + " a_sim = utils.pairwise_cosine_similarity(emb_v, emb_a).item()\n", + " b_sim = utils.pairwise_cosine_similarity(emb_v, emb_b).item()\n", + "\n", + " binary_acc.append(a_sim < b_sim)\n", + "\n", + " assert len(binary_acc) == 50 # don't want to average across both sessions; make sure it resets\n", + " print(f\"ses-0{half+2} score: {np.mean(binary_acc):.4f} ± {np.std(binary_acc):.4f}\")\n", + " mst_score.append((np.mean(binary_acc),np.std(binary_acc)))\n", + "\n", + " # print(mst_score)\n", + " else:\n", + " print('assuming single session')\n", + " binary_acc = []\n", + " for i,(a,b) in enumerate(tqdm(MST_pairmate_indices,total=len(MST_pairmate_indices))):\n", + " # print(i,a,b)\n", + " with torch.no_grad():\n", + " with torch.cuda.amp.autocast():\n", + " emb_a = nn.functional.normalize(clip_img_embedder(all_images_resized[[a]].to(device)).float().flatten(1),dim=-1)\n", + " emb_b = nn.functional.normalize(clip_img_embedder(all_images_resized[[b]].to(device)).float().flatten(1),dim=-1)\n", + " emb_v = nn.functional.normalize(all_clipvoxels[[a]].flatten(1),dim=-1).to(device)\n", + "\n", + " a_sim = utils.pairwise_cosine_similarity(emb_v, emb_a).item()\n", + " b_sim = utils.pairwise_cosine_similarity(emb_v, emb_b).item()\n", + "\n", + " binary_acc.append(a_sim > b_sim)\n", + "\n", + " with torch.no_grad():\n", + " with torch.cuda.amp.autocast():\n", + " emb_a = nn.functional.normalize(clip_img_embedder(all_images_resized[[a]].to(device)).float().flatten(1),dim=-1)\n", + " emb_b = nn.functional.normalize(clip_img_embedder(all_images_resized[[b]].to(device)).float().flatten(1),dim=-1)\n", + " emb_v = nn.functional.normalize(all_clipvoxels[[b]].flatten(1),dim=-1).to(device)\n", + "\n", + " a_sim = utils.pairwise_cosine_similarity(emb_v, emb_a).item()\n", + " b_sim = utils.pairwise_cosine_similarity(emb_v, emb_b).item()\n", + "\n", + " binary_acc.append(a_sim < b_sim)\n", + " \n", + " # assert len(binary_acc) == 50\n", + " mst_score = np.mean(binary_acc)\n", + " print(f\"session score: {np.mean(binary_acc):.4f} ± {np.std(binary_acc):.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "0a26e124-2444-434d-a399-d03c2c90cc08", + "metadata": {}, + "source": [ + "## 2-way identification" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3e1778ff-5d6a-4087-b59f-0f44b9e0eada", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names\n", + "\n", + "@torch.no_grad()\n", + "def two_way_identification(all_recons, all_images, model, preprocess, feature_layer=None, return_avg=True):\n", + " preds = model(torch.stack([preprocess(recon) for recon in all_recons], dim=0).to(device))\n", + " reals = model(torch.stack([preprocess(indiv) for indiv in all_images], dim=0).to(device))\n", + " if feature_layer is None:\n", + " preds = preds.float().flatten(1).cpu().numpy()\n", + " reals = reals.float().flatten(1).cpu().numpy()\n", + " else:\n", + " preds = preds[feature_layer].float().flatten(1).cpu().numpy()\n", + " reals = reals[feature_layer].float().flatten(1).cpu().numpy()\n", + "\n", + " r = np.corrcoef(reals, preds)\n", + " r = r[:len(all_images), len(all_images):]\n", + " congruents = np.diag(r)\n", + "\n", + " success = r < congruents\n", + " success_cnt = np.sum(success, 0)\n", + "\n", + " if return_avg:\n", + " perf = np.mean(success_cnt) / (len(all_images)-1)\n", + " return perf\n", + " else:\n", + " return success_cnt, len(all_images)-1\n", + " \n", + "all_recons=all_recons.to(device)\n", + "all_images=all_images.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "df6be966-52ef-4cf6-8078-8d2d9617564b", + "metadata": {}, + "source": [ + "## PixCorr" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2e17ea38-a254-4e90-a910-711734fdd8eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([248, 541875])\n", + "torch.Size([248, 541875])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:01<00:00, 142.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.08980458347640023\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "preprocess = transforms.Compose([\n", + " transforms.Resize(425, interpolation=transforms.InterpolationMode.BILINEAR),\n", + "])\n", + "\n", + "# Flatten images while keeping the batch dimension\n", + "all_images_flattened = preprocess(all_images).reshape(len(all_images), -1).cpu()\n", + "all_recons_flattened = preprocess(all_recons).view(len(all_recons), -1).cpu()\n", + "\n", + "print(all_images_flattened.shape)\n", + "print(all_recons_flattened.shape)\n", + "\n", + "corr_stack = []\n", + "\n", + "corrsum = 0\n", + "for i in tqdm(range(len(all_images))):\n", + " corrcoef = np.corrcoef(all_images_flattened[i], all_recons_flattened[i])[0][1]\n", + " if np.isnan(corrcoef):\n", + " print(\"WARNING: CORRCOEF WAS NAN\")\n", + " corrcoef = 0\n", + " corrsum += corrcoef\n", + " corr_stack.append(corrcoef)\n", + "corrmean = corrsum / len(all_images)\n", + "\n", + "pixcorr = corrmean\n", + "print(pixcorr)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7e2cd891-db44-475d-a8c6-ab345eaa58f8", + "metadata": {}, + "outputs": [], + "source": [ + "# print(all_images.shape)\n", + "# print(all_images_flattened.shape)\n", + "# print(all_recons.shape)\n", + "# print(all_recons_flattened.shape)\n", + "# len(all_images)" + ] + }, + { + "cell_type": "markdown", + "id": "7a556d5b-33a2-44aa-b48d-4b168316bbdd", + "metadata": { + "tags": [] + }, + "source": [ + "## SSIM" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "2326fc4c-1248-4d0f-9176-218c6460f285", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converted, now calculating ssim...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:02<00:00, 85.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.42943209222424794\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# see https://github.com/zijin-gu/meshconv-decoding/issues/3\n", + "from skimage.color import rgb2gray\n", + "from skimage.metrics import structural_similarity as ssim\n", + "\n", + "preprocess = transforms.Compose([\n", + " transforms.Resize(425, interpolation=transforms.InterpolationMode.BILINEAR), \n", + "])\n", + "\n", + "# convert image to grayscale with rgb2grey\n", + "img_gray = rgb2gray(preprocess(all_images).permute((0,2,3,1)).cpu())\n", + "recon_gray = rgb2gray(preprocess(all_recons).permute((0,2,3,1)).cpu())\n", + "print(\"converted, now calculating ssim...\")\n", + "\n", + "ssim_score=[]\n", + "for im,rec in tqdm(zip(img_gray,recon_gray),total=len(all_images)):\n", + " ssim_score.append(ssim(rec, im, multichannel=True, gaussian_weights=True, sigma=1.5, use_sample_covariance=False, data_range=1.0))\n", + "\n", + "ssim = np.mean(ssim_score)\n", + "print(ssim)" + ] + }, + { + "cell_type": "markdown", + "id": "35138520-ec00-48a6-90dc-249a32a783d2", + "metadata": {}, + "source": [ + "## AlexNet" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3b45cc6c-ab80-43e2-b446-c8fcb4fc54e4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /home/ubuntu/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n", + "100%|██████████| 233M/233M [00:01<00:00, 237MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---early, AlexNet(2)---\n", + "2-way Percent Correct: 0.5751\n", + "\n", + "---mid, AlexNet(5)---\n", + "2-way Percent Correct: 0.5790\n" + ] + } + ], + "source": [ + "from torchvision.models import alexnet, AlexNet_Weights\n", + "alex_weights = AlexNet_Weights.IMAGENET1K_V1\n", + "\n", + "alex_model = create_feature_extractor(alexnet(weights=alex_weights), return_nodes=['features.4','features.11']).to(device)\n", + "alex_model.eval().requires_grad_(False).to(device)\n", + "\n", + "# see alex_weights.transforms()\n", + "preprocess = transforms.Compose([\n", + " transforms.Resize(256, interpolation=transforms.InterpolationMode.BILINEAR),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + "])\n", + "\n", + "layer = 'early, AlexNet(2)'\n", + "print(f\"\\n---{layer}---\")\n", + "all_per_correct = two_way_identification(all_recons, all_images, \n", + " alex_model, preprocess, 'features.4')\n", + "alexnet2 = np.mean(all_per_correct)\n", + "print(f\"2-way Percent Correct: {alexnet2:.4f}\")\n", + "\n", + "layer = 'mid, AlexNet(5)'\n", + "print(f\"\\n---{layer}---\")\n", + "all_per_correct = two_way_identification(all_recons, all_images, \n", + " alex_model, preprocess, 'features.11')\n", + "alexnet5 = np.mean(all_per_correct)\n", + "print(f\"2-way Percent Correct: {alexnet5:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c296bab2-d106-469e-b997-b32d21a2cf01", + "metadata": {}, + "source": [ + "## InceptionV3" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5a9c1b2b-af2a-476d-a1ac-32ee915ac2ec", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /home/ubuntu/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n", + "100%|██████████| 104M/104M [00:00<00:00, 187MB/s] \n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/torchvision/models/feature_extraction.py:174: UserWarning: NOTE: The nodes obtained by tracing the model in eval mode are a subsequence of those obtained in train mode. When choosing nodes for feature extraction, you may need to specify output nodes for train and eval mode separately.\n", + " warnings.warn(msg + suggestion_msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2-way Percent Correct: 0.5571\n" + ] + } + ], + "source": [ + "from torchvision.models import inception_v3, Inception_V3_Weights\n", + "weights = Inception_V3_Weights.DEFAULT\n", + "inception_model = create_feature_extractor(inception_v3(weights=weights), \n", + " return_nodes=['avgpool']).to(device)\n", + "inception_model.eval().requires_grad_(False).to(device)\n", + "\n", + "# see weights.transforms()\n", + "preprocess = transforms.Compose([\n", + " transforms.Resize(342, interpolation=transforms.InterpolationMode.BILINEAR),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + "])\n", + "\n", + "all_per_correct = two_way_identification(all_recons, all_images,\n", + " inception_model, preprocess, 'avgpool')\n", + " \n", + "inception = np.mean(all_per_correct)\n", + "print(f\"2-way Percent Correct: {inception:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d7a25f7f-8298-4413-b512-8a1173413e07", + "metadata": {}, + "source": [ + "## CLIP" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "6afbf7ce-8793-4988-a328-a632acd88aa9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████| 890M/890M [00:26<00:00, 35.1MiB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2-way Percent Correct: 0.5629\n" + ] + } + ], + "source": [ + "import clip\n", + "clip_model, preprocess = clip.load(\"ViT-L/14\", device=device)\n", + "\n", + "preprocess = transforms.Compose([\n", + " transforms.Resize(224, interpolation=transforms.InterpolationMode.BILINEAR),\n", + " transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],\n", + " std=[0.26862954, 0.26130258, 0.27577711]),\n", + "])\n", + "\n", + "all_per_correct = two_way_identification(all_recons, all_images,\n", + " clip_model.encode_image, preprocess, None) # final layer\n", + "clip_ = np.mean(all_per_correct)\n", + "print(f\"2-way Percent Correct: {clip_:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e4fed9f8-ef1a-4c6d-a83f-2a934b6e87fd", + "metadata": {}, + "source": [ + "## Efficient Net" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "14143c0f-1b32-43ef-98d8-8ed458df4551", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth\" to /home/ubuntu/.cache/torch/hub/checkpoints/efficientnet_b1-c27df63c.pth\n", + "100%|██████████| 30.1M/30.1M [00:00<00:00, 133MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance: 0.9132417713371813\n" + ] + } + ], + "source": [ + "import scipy as sp\n", + "from torchvision.models import efficientnet_b1, EfficientNet_B1_Weights\n", + "weights = EfficientNet_B1_Weights.DEFAULT\n", + "eff_model = create_feature_extractor(efficientnet_b1(weights=weights), \n", + " return_nodes=['avgpool'])\n", + "eff_model.eval().requires_grad_(False).to(device)\n", + "\n", + "# see weights.transforms()\n", + "preprocess = transforms.Compose([\n", + " transforms.Resize(255, interpolation=transforms.InterpolationMode.BILINEAR),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + "])\n", + "\n", + "gt = eff_model(preprocess(all_images))['avgpool']\n", + "gt = gt.reshape(len(gt),-1).cpu().numpy()\n", + "fake = eff_model(preprocess(all_recons))['avgpool']\n", + "fake = fake.reshape(len(fake),-1).cpu().numpy()\n", + "\n", + "effnet_nomean = np.array([sp.spatial.distance.correlation(gt[i],fake[i]) for i in range(len(gt))])\n", + "effnet = effnet_nomean.mean()\n", + "print(\"Distance:\",effnet)" + ] + }, + { + "cell_type": "markdown", + "id": "405f669d-cab7-4c75-90cd-651283f65a9e", + "metadata": {}, + "source": [ + "## SwAV" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4c60b0c4-79fe-4cff-95e9-99733c821e67", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://github.com/facebookresearch/swav/zipball/main\" to /home/ubuntu/.cache/torch/hub/main.zip\n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/ubuntu/rt_mindEye2/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar\" to /home/ubuntu/.cache/torch/hub/checkpoints/swav_800ep_pretrain.pth.tar\n", + "100%|██████████| 108M/108M [00:00<00:00, 180MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance: 0.599512959880879\n" + ] + } + ], + "source": [ + "swav_model = torch.hub.load('facebookresearch/swav:main', 'resnet50')\n", + "swav_model = create_feature_extractor(swav_model, \n", + " return_nodes=['avgpool'])\n", + "swav_model.eval().requires_grad_(False).to(device)\n", + "\n", + "preprocess = transforms.Compose([\n", + " transforms.Resize(224, interpolation=transforms.InterpolationMode.BILINEAR),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + "])\n", + "\n", + "gt = swav_model(preprocess(all_images))['avgpool']\n", + "gt = gt.reshape(len(gt),-1).cpu().numpy()\n", + "fake = swav_model(preprocess(all_recons))['avgpool']\n", + "fake = fake.reshape(len(fake),-1).cpu().numpy()\n", + "\n", + "swav_nomean = np.array([sp.spatial.distance.correlation(gt[i],fake[i]) for i in range(len(gt))])\n", + "swav = swav_nomean.mean()\n", + "print(\"Distance:\",swav)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "8ebeeff0-49ab-4bf5-9e12-dd45eb7ff71f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Metric Value\n", + " alexnet2 0.575095\n", + " alexnet5 0.579013\n", + " inception 0.557088\n", + " clip_ 0.562949\n", + " effnet 0.913242\n", + " swav 0.599513\n", + " pixcorr 0.089805\n", + " ssim 0.429432\n", + "percent_correct_fwd 0.096774\n", + "percent_correct_bwd 0.080645\n", + " mst_score []\n", + "Saved final evals!\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Define metric names\n", + "metric_names = [\n", + " \"alexnet2\", \"alexnet5\", \"inception\", \"clip_\", \"effnet\", \"swav\", \"pixcorr\", \"ssim\",\n", + " \"percent_correct_fwd\", \"percent_correct_bwd\", \"mst_score\"\n", + "]\n", + "\n", + "# Define values depending on session\n", + "if \"ses-0\" not in model_name and False:\n", + " values = [alexnet2, alexnet5, inception, clip_, effnet, swav, pixcorr, ssim, fwd_acc, bwd_acc, mst_score]\n", + "else:\n", + " values = [alexnet2, alexnet5, inception, clip_, effnet, swav, pixcorr, ssim, all_fwd_acc[0], all_bwd_acc[0], mst_score]\n", + "\n", + "# Create the DataFrame\n", + "df = pd.DataFrame({\n", + " \"Metric\": metric_names,\n", + " \"Value\": values\n", + "})\n", + "\n", + "# Optional formatting\n", + "# pd.options.display.float_format = '{:.2%}'.format\n", + "\n", + "# Print the DataFrame nicely\n", + "print(df.to_string(index=False))\n", + "\n", + "# Save to file if needed\n", + "final_evals_path = f\"{eval_dir}/final_evals\"\n", + "if saving:\n", + " df.to_csv(final_evals_path, index=False)\n", + " print('Saved final evals!')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "8dd57b7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([62, 1, 1024])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_unique_clipvoxels.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "0446fb2a-fd3f-451f-b9b2-38aa95a2be8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metric,Value\n", + "alexnet2,0.5750946846023247\n", + "alexnet5,0.5790126681467939\n", + "inception,0.5570882852292021\n", + "clip_,0.5629489356144705\n", + "effnet,0.9132417713371813\n", + "swav,0.599512959880879\n", + "pixcorr,0.08980458347640023\n", + "ssim,0.42943209222424794\n", + "percent_correct_fwd,0.09677419066429138\n", + "percent_correct_bwd,0.08064515888690948\n", + "mst_score,[]\n" + ] + } + ], + "source": [ + "with open(final_evals_path, 'r') as f:\n", + " for line in f:\n", + " print(line, end='')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd74e27c-6d79-4d49-bcc9-f6b85fadc752", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rt_mindEye2", + "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 +} diff --git a/recon_inference-multisession_union_mask_sdxl_turbo.ipynb b/recon_inference-multisession_union_mask_sdxl_turbo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..26b5f1a43d499a9a019da86a5e2f4dcad03bf183 --- /dev/null +++ b/recon_inference-multisession_union_mask_sdxl_turbo.ipynb @@ -0,0 +1,5716 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "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": 5, + "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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/rt_sdxlturbo/lib/python3.11/site-packages/accelerate/accelerator.py:439: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " self.scaler = torch.cuda.amp.GradScaler(**kwargs)\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": 6, + "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": 7, + "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": 8, + "id": "e52985b1-95ff-487b-8b2d-cc1ad1c190b8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model_name: sdxl_turbo-MST\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=sdxl_turbo-MST --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 = \"sdxl_turbo-MST\"\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": 9, + "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": 10, + "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": 11, + "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": 12, + "id": "59bc3b21-e29d-4d2b-8223-cd704e3f058a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1%| | 7/1386 [00:00<00:32, 42.96it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1386/1386 [04:36<00:00, 5.02it/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": 13, + "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": 14, + "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": 15, + "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": 16, + "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": 17, + "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": 18, + "id": "144cc582", + "metadata": {}, + "outputs": [], + "source": [ + "# take all paths exept last dir\n", + "base_glm_single_path = glmsingle_path.split('/')[:-1]\n", + "base_glm_single_path = '/'.join(base_glm_single_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": [ + "
" + ] + }, + "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(f'{base_glm_single_path}/glmsingle_sub-005_task-C/sub-005_final_brain.nii.gz')\n", + "final_mask = nib.load(f'{base_glm_single_path}/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": 20, + "id": "843dc2fd", + "metadata": {}, + "outputs": [], + "source": [ + "path = f'{base_glm_single_path}/glmsingle_sub-005_task-C/union_mask_from_ses-01-02.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": 21, + "id": "ae2862a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/ubuntu/glmsingle/glmsingle_sub-005_ses-01-02_task-C'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glmsingle_path" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "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": 23, + "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": 24, + "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": 25, + "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": 26, + "id": "a292cfad-83f4-4bf8-994e-da2c871c0a6c", + "metadata": {}, + "outputs": [], + "source": [ + "# test_image_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "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": 28, + "id": "43703336", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of train mean from ses-01: (8627,)\n", + "shape of train std from ses-01: (8627,)\n", + "shape of train mean from ses-02: (8627,)\n", + "shape of train std from ses-02: (8627,)\n", + "voxels have been zscored\n", + "ses-01: -0.11676669 1.0246168\n", + "ses-02: -0.02013384 0.98079187\n", + "vox (1386, 8627)\n" + ] + } + ], + "source": [ + "ses_split = vox[train_image_indices].shape[0] // 2\n", + "\n", + "train_mean_s1 = np.mean(vox[train_image_indices][:ses_split], axis=0)\n", + "train_std_s1 = np.std(vox[train_image_indices][:ses_split], axis=0)\n", + "train_mean_s2 = np.mean(vox[train_image_indices][ses_split:], axis=0)\n", + "train_std_s2 = np.std(vox[train_image_indices][ses_split:], axis=0)\n", + "\n", + "print('shape of train mean from ses-01:', train_mean_s1.shape)\n", + "print('shape of train std from ses-01:', train_std_s1.shape)\n", + "print('shape of train mean from ses-02:', train_mean_s2.shape)\n", + "print('shape of train std from ses-02:', train_std_s2.shape)\n", + "\n", + "\n", + "vox[:ses_split] = utils.zscore(vox[:ses_split],train_mean=train_mean_s1,train_std=train_std_s1)\n", + "vox[ses_split:] = utils.zscore(vox[ses_split:],train_mean=train_mean_s2,train_std=train_std_s2)\n", + "\n", + "print(\"voxels have been zscored\")\n", + "print(\"ses-01:\", vox[:ses_split,0].mean(), vox[:ses_split,0].std())\n", + "print(\"ses-02:\", vox[ses_split:,0].mean(), vox[ses_split:,0].std())\n", + "print(\"vox\", vox.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "b4b12e50", + "metadata": {}, + "outputs": [], + "source": [ + "# vox[:ses_split] = utils.zscore(vox[:ses_split],train_mean=train_mean_s1,train_std=train_std_s1)\n", + "# vox[ses_split:] = utils.zscore(vox[ses_split:],train_mean=train_mean_s2,train_std=train_std_s2)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "5528d877-b662-41f7-8982-3f31051871f6", + "metadata": {}, + "outputs": [], + "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": 31, + "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": 32, + "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": 33, + "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": 34, + "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": 46, + "id": "788c3a6a", + "metadata": {}, + "outputs": [], + "source": [ + "test_voxels = torch.tensor(test_voxels)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "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": 48, + "id": "56b606a4-7302-4ac5-b89d-bbe4fcb00d11", + "metadata": {}, + "outputs": [], + "source": [ + "import utils" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "e452b5b2-47d9-4271-b9fc-ea331fbac1bc", + "metadata": {}, + "outputs": [ + { + "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": 50, + "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": 51, + "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": 52, + "id": "41b4a640", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---loading /home/ubuntu/real_time_mindEye2/train_logs/sdxl_turbo-MST/last.pth ckpt---\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_34077/184505764.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": 56, + "id": "8062afe3", + "metadata": {}, + "outputs": [], + "source": [ + "eval_dir = f\"/home/ubuntu/real_time_mindEye2/evals/{model_name}\" # \"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals\"\n", + "os.makedirs(eval_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "a1b1e759", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0430a04de0584baf9c73068a45cae2c7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading pipeline components...: 0%| | 0/7 [00:000:\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": 59, + "id": "be402221", + "metadata": {}, + "outputs": [], + "source": [ + "# torch.tensor(np.array(samples).transpose(2,0,1)).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "e9e24f37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([248, 3, 256, 256]), tensor(1.), tensor(6.1275e-05))" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_recons.shape, all_recons.max(), all_recons.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "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": 62, + "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": 63, + "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": 64, + "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": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_images.shape, all_recons.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "9a5d42c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.float32" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_recons.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "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": 67, + "id": "e1a9856d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(y.cpu()[200])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "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", + "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" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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 +}