{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f16c9d4c-66cb-4692-a61d-9aa86a8765d0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "importing modules\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "SLURM random seed indices not provided; using random seed = 0\n", "LOCAL RANK 0\n", "device: cuda\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", "\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", "# SDXL unCLIP requires code from https://github.com/Stability-AI/generative-models/tree/main\n", "sys.path.append('generative_models/')\n", "import sgm\n", "from generative_models.sgm.modules.encoders.modules import FrozenOpenCLIPImageEmbedder, FrozenCLIPEmbedder, FrozenOpenCLIPEmbedder2\n", "from generative_models.sgm.models.diffusion import DiffusionEngine\n", "from generative_models.sgm.util import append_dims\n", "from omegaconf import OmegaConf\n", "\n", "# tf32 data type is faster than standard float32\n", "torch.backends.cuda.matmul.allow_tf32 = True\n", "\n", "import utils\n", "\n", "# Can run a SLURM job array to train many models with different random seed values\n", "try:\n", " idx = int(os.environ[\"SLURM_ARRAY_TASK_ID\"])\n", " seeds = range(10)\n", " seed = seeds[idx]\n", " print(f\"using random seed {seed} in SLURM job array\")\n", "except:\n", " print(\"SLURM random seed indices not provided; using random seed = 0\")\n", " seed = 0\n", "\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 \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)\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": 2, "id": "69037852-cdbd-4eac-a720-3fca5dc48a61", "metadata": {}, "outputs": [], "source": [ "# n_runs = 16\n", "# remove_close_to_MST = False\n", "# remove_random_n = False\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", "# 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" ] }, { "cell_type": "code", "execution_count": 3, "id": "8f75b5bf-bd74-42db-9a82-d070591a5076", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model_name: sub-001_ses-01_bs24_MST_paul_MSTsplit\n" ] } ], "source": [ "if utils.is_interactive():\n", " sub = \"sub-001\"\n", " session = \"ses-01\"\n", " # if session == \"multi\":\n", " # ses_list = [\"ses-02\", \"ses-03\"]\n", " train_test_split = 'MST' # orig\n", " glmsingle_path = f\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/glmsingle_{session}\"\n", " model_name = f\"{sub}_{session}_bs24_MST_paul_{train_test_split}split\" # 'testing_MST' \n", " print(\"model_name:\", model_name)\n", " eval_dir = f\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/{model_name}\"\n", "\n" ] }, { "cell_type": "markdown", "id": "d8a3901c-60dd-4ae2-b0f5-8a55aa231908", "metadata": {}, "source": [ "# Model" ] }, { "cell_type": "code", "execution_count": 4, "id": "e52985b1-95ff-487b-8b2d-cc1ad1c190b8", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--data_path=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2 --model_name=sub-001_ses-01_bs24_MST_paul_MSTsplit --subj=1 --no-blurry_recon --use_prior --hidden_dim=1024 --n_blocks=4 --eval_dir=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/sub-001_ses-01_bs24_MST_paul_MSTsplit\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", " # 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", " --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": 5, "id": "49e5dae4-606d-4dc6-b420-df9e4c14737e", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "usage: ipykernel_launcher.py [-h] [--model_name MODEL_NAME]\n", " [--data_path DATA_PATH]\n", " [--subj {1,2,3,4,5,6,7,8}]\n", " [--blurry_recon | --no-blurry_recon]\n", " [--use_prior | --no-use_prior]\n", " [--clip_scale CLIP_SCALE] [--n_blocks N_BLOCKS]\n", " [--hidden_dim HIDDEN_DIM]\n", " [--new_test | --no-new_test] [--seq_len SEQ_LEN]\n", " [--seed SEED] [--glmsingle_path GLMSINGLE_PATH]\n", " [--remove_random_n | --no-remove_random_n]\n", "ipykernel_launcher.py: error: unrecognized arguments: --eval_dir=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/sub-001_ses-01_bs24_MST_paul_MSTsplit\n" ] }, { "ename": "SystemExit", "evalue": "2", "output_type": "error", "traceback": [ "An exception has occurred, use %tb to see the full traceback.\n", "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ri4541/.conda/envs/rt_mindEye2/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3585: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" ] } ], "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,\n", ")\n", "parser.add_argument(\n", " \"--remove_random_n\",action=argparse.BooleanOptionalAction,default=False,\n", ")\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": 13, "id": "f8524dbf-f81a-43a2-8f2f-9a1d615440db", "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_/train_image_indices.npy'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[13], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_random_n:\n\u001b[1;32m 3\u001b[0m imgs_to_remove \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00meval_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/imgs_to_remove.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m train_image_indices \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43meval_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/train_image_indices.npy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m test_image_indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00meval_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/test_image_indices.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6\u001b[0m images \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor(np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00meval_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/images.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", "File \u001b[0;32m~/.conda/envs/rt_mindEye2/lib/python3.11/site-packages/numpy/lib/npyio.py:405\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[1;32m 403\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 405\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 406\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'evals/sub-001_ses-01_bs24_MST_paul_MSTsplit/sub-001_ses-01_bs24_MST_paul_MSTsplit_/train_image_indices.npy'" ] } ], "source": [ "# eval_dir = f\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/{model_name}\"\n", "if remove_random_n:\n", " imgs_to_remove = np.load(f\"{eval_dir}/imgs_to_remove.npy\")\n", "train_image_indices = np.load(f\"{eval_dir}/train_image_indices.npy\")\n", "test_image_indices = np.load(f\"{eval_dir}/test_image_indices.npy\")\n", "images = torch.Tensor(np.load(f\"{eval_dir}/images.npy\"))\n", "vox = torch.Tensor(np.load(f\"{eval_dir}/vox.npy\"))" ] }, { "cell_type": "code", "execution_count": 14, "id": "5f45262c-59be-4ad9-89bd-deb74094d74a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "torch.Size([1000, 3, 224, 224])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "images.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "64672583-9f00-46f5-8d4e-00e4c7068a1d", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded test dl for subj1!\n", "\n" ] } ], "source": [ "test_data = torch.utils.data.TensorDataset(torch.tensor(test_image_indices))\n", "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": 8, "id": "a3cbeea8-e95b-48d9-9bc2-91af260c93d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 300 300\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": 9, "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": 10, "id": "de0400d4-cbd6-4941-a0b2-1a4bc2ae97da", "metadata": {}, "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 = 256\n", "clip_emb_dim = 1664" ] }, { "cell_type": "code", "execution_count": 11, "id": "f0dcf6f1-a67b-4b39-90be-4b4a4a4f6716", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "param counts:\n", "2,667,520 total\n", "2,667,520 trainable\n", "param counts:\n", "2,667,520 total\n", "2,667,520 trainable\n", "param counts:\n", "453,360,280 total\n", "453,360,280 trainable\n", "param counts:\n", "456,027,800 total\n", "456,027,800 trainable\n", "param counts:\n", "259,865,216 total\n", "259,865,200 trainable\n", "param counts:\n", "715,893,016 total\n", "715,893,000 trainable\n" ] } ], "source": [ "class MindEyeModule(nn.Module):\n", " def __init__(self):\n", " super(MindEyeModule, self).__init__()\n", " def forward(self, x):\n", " return x\n", " \n", "model = MindEyeModule()\n", "\n", "class RidgeRegression(torch.nn.Module):\n", " # make sure to add weight_decay when initializing optimizer\n", " def __init__(self, input_sizes, out_features, seq_len=1): \n", " super(RidgeRegression, self).__init__()\n", " self.seq_len = seq_len\n", " self.out_features = out_features\n", " self.linears = torch.nn.ModuleList([\n", " torch.nn.Linear(input_size, out_features) for input_size in input_sizes\n", " ])\n", " def forward(self, x, subj_idx=0):\n", " out = torch.cat([self.linears[subj_idx](x[:,seq]).unsqueeze(1) for seq in range(self.seq_len)], dim=1)\n", " return out\n", " \n", "model.ridge = RidgeRegression(num_voxels_list, out_features=hidden_dim)\n", "utils.count_params(model.ridge)\n", "utils.count_params(model)\n", "\n", "from functools import partial\n", "from diffusers.models.vae import Decoder\n", "class BrainNetwork(nn.Module):\n", " def __init__(self, h=4096, in_dim=15724, out_dim=768, seq_len=1, n_blocks=n_blocks, drop=.15, \n", " clip_size=768):\n", " super().__init__()\n", " self.seq_len = seq_len\n", " self.h = h\n", " self.clip_size = clip_size\n", " \n", " self.mixer_blocks1 = nn.ModuleList([\n", " self.mixer_block1(h, drop) for _ in range(n_blocks)\n", " ])\n", " self.mixer_blocks2 = nn.ModuleList([\n", " self.mixer_block2(seq_len, drop) for _ in range(n_blocks)\n", " ])\n", " \n", " # Output linear layer\n", " self.backbone_linear = nn.Linear(h * seq_len, out_dim, bias=True) \n", " if clip_scale>0:\n", " self.clip_proj = self.projector(clip_size, clip_size, h=clip_size)\n", " \n", " def projector(self, in_dim, out_dim, h=2048):\n", " return nn.Sequential(\n", " nn.LayerNorm(in_dim),\n", " nn.GELU(),\n", " nn.Linear(in_dim, h),\n", " nn.LayerNorm(h),\n", " nn.GELU(),\n", " nn.Linear(h, h),\n", " nn.LayerNorm(h),\n", " nn.GELU(),\n", " nn.Linear(h, out_dim)\n", " )\n", " \n", " def mlp(self, in_dim, out_dim, drop):\n", " return nn.Sequential(\n", " nn.Linear(in_dim, out_dim),\n", " nn.GELU(),\n", " nn.Dropout(drop),\n", " nn.Linear(out_dim, out_dim),\n", " )\n", " \n", " def mixer_block1(self, h, drop):\n", " return nn.Sequential(\n", " nn.LayerNorm(h),\n", " self.mlp(h, h, drop), # Token mixing\n", " )\n", "\n", " def mixer_block2(self, seq_len, drop):\n", " return nn.Sequential(\n", " nn.LayerNorm(seq_len),\n", " self.mlp(seq_len, seq_len, drop) # Channel mixing\n", " )\n", " \n", " def forward(self, x):\n", " # make empty tensors\n", " c,b = torch.Tensor([0.]), torch.Tensor([[0.],[0.]])\n", " \n", " # Mixer blocks\n", " residual1 = x\n", " residual2 = x.permute(0,2,1)\n", " for block1, block2 in zip(self.mixer_blocks1,self.mixer_blocks2):\n", " x = block1(x) + residual1\n", " residual1 = x\n", " x = x.permute(0,2,1)\n", " \n", " x = block2(x) + residual2\n", " residual2 = x\n", " x = x.permute(0,2,1)\n", " \n", " x = x.reshape(x.size(0), -1)\n", " backbone = self.backbone_linear(x).reshape(len(x), -1, self.clip_size)\n", " if clip_scale>0:\n", " c = self.clip_proj(backbone)\n", " \n", " return backbone, c, b\n", "\n", "model.backbone = BrainNetwork(h=hidden_dim, in_dim=hidden_dim, seq_len=1, \n", " clip_size=clip_emb_dim, out_dim=clip_emb_dim*clip_seq_dim)\n", "utils.count_params(model.backbone)\n", "utils.count_params(model)\n", "\n", "if use_prior:\n", " from models import *\n", "\n", " # setup diffusion prior network\n", " out_dim = clip_emb_dim\n", " depth = 6\n", " dim_head = 52\n", " heads = clip_emb_dim//52 # heads * dim_head = clip_emb_dim\n", " timesteps = 100\n", "\n", " prior_network = VersatileDiffusionPriorNetwork(\n", " dim=out_dim,\n", " depth=depth,\n", " dim_head=dim_head,\n", " heads=heads,\n", " causal=False,\n", " num_tokens = clip_seq_dim,\n", " learned_query_mode=\"pos_emb\"\n", " )\n", "\n", " model.diffusion_prior = BrainDiffusionPrior(\n", " net=prior_network,\n", " image_embed_dim=out_dim,\n", " condition_on_text_encodings=False,\n", " timesteps=timesteps,\n", " cond_drop_prob=0.2,\n", " image_embed_scale=None,\n", " )\n", " \n", " utils.count_params(model.diffusion_prior)\n", " utils.count_params(model)" ] }, { "cell_type": "code", "execution_count": 24, "id": "6e720891-6575-4711-bc35-876527ed3b9d", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "---loading /scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/train_logs/sub-001_multi_bs24_MST_rishab_MSTsplit_remove_150_random_seed_1/last.pth ckpt---\n", "\n", "hidden_dim = 1024\n", "Saved model: ridge.linears.0.weight -> torch.Size([1024, 2604])\n", "Saved model: ridge.linears.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.0.0.weight -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.0.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.0.1.0.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.0.1.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.0.1.3.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.0.1.3.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.1.0.weight -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.1.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.1.1.0.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.1.1.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.1.1.3.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.1.1.3.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.2.0.weight -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.2.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.2.1.0.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.2.1.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.2.1.3.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.2.1.3.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.3.0.weight -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.3.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.3.1.0.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.3.1.0.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks1.3.1.3.weight -> torch.Size([1024, 1024])\n", "Saved model: backbone.mixer_blocks1.3.1.3.bias -> torch.Size([1024])\n", "Saved model: backbone.mixer_blocks2.0.0.weight -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.0.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.0.1.0.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.0.1.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.0.1.3.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.0.1.3.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.1.0.weight -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.1.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.1.1.0.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.1.1.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.1.1.3.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.1.1.3.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.2.0.weight -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.2.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.2.1.0.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.2.1.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.2.1.3.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.2.1.3.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.3.0.weight -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.3.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.3.1.0.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.3.1.0.bias -> torch.Size([1])\n", "Saved model: backbone.mixer_blocks2.3.1.3.weight -> torch.Size([1, 1])\n", "Saved model: backbone.mixer_blocks2.3.1.3.bias -> torch.Size([1])\n", "Saved model: backbone.backbone_linear.weight -> torch.Size([425984, 1024])\n", "Saved model: backbone.backbone_linear.bias -> torch.Size([425984])\n", "Saved model: backbone.clip_proj.0.weight -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.0.bias -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.2.weight -> torch.Size([1664, 1664])\n", "Saved model: backbone.clip_proj.2.bias -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.3.weight -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.3.bias -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.5.weight -> torch.Size([1664, 1664])\n", "Saved model: backbone.clip_proj.5.bias -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.6.weight -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.6.bias -> torch.Size([1664])\n", "Saved model: backbone.clip_proj.8.weight -> torch.Size([1664, 1664])\n", "Saved model: backbone.clip_proj.8.bias -> torch.Size([1664])\n", "Saved model: diffusion_prior.noise_scheduler.betas -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.alphas_cumprod -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.alphas_cumprod_prev -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.sqrt_alphas_cumprod -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.sqrt_one_minus_alphas_cumprod -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.log_one_minus_alphas_cumprod -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.sqrt_recip_alphas_cumprod -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.sqrt_recipm1_alphas_cumprod -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.posterior_variance -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.posterior_log_variance_clipped -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.posterior_mean_coef1 -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.posterior_mean_coef2 -> torch.Size([100])\n", "Saved model: diffusion_prior.noise_scheduler.p2_loss_weight -> torch.Size([100])\n", "Saved model: diffusion_prior.net.learned_query -> torch.Size([256, 1664])\n", "Saved model: diffusion_prior.net.null_brain_embeds -> torch.Size([256, 1664])\n", "Saved model: diffusion_prior.net.null_image_embed -> torch.Size([256, 1664])\n", "Saved model: diffusion_prior.net.to_time_embeds.0.1.net.0.0.weight -> torch.Size([3328, 1664])\n", "Saved model: diffusion_prior.net.to_time_embeds.0.1.net.0.0.bias -> torch.Size([3328])\n", "Saved model: diffusion_prior.net.to_time_embeds.0.1.net.1.0.weight -> torch.Size([3328, 3328])\n", "Saved model: diffusion_prior.net.to_time_embeds.0.1.net.1.0.bias -> torch.Size([3328])\n", "Saved model: diffusion_prior.net.to_time_embeds.0.1.net.2.weight -> torch.Size([1664, 3328])\n", "Saved model: diffusion_prior.net.to_time_embeds.0.1.net.2.bias -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.rel_pos_bias.relative_attention_bias.weight -> torch.Size([32, 32])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.null_kv -> torch.Size([2, 52])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.norm.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.to_q.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.to_kv.weight -> torch.Size([104, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.rotary_emb.freqs -> torch.Size([16])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.0.to_out.1.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.1.0.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.1.1.weight -> torch.Size([13312, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.0.1.5.weight -> torch.Size([1664, 6656])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.null_kv -> torch.Size([2, 52])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.norm.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.to_q.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.to_kv.weight -> torch.Size([104, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.rotary_emb.freqs -> torch.Size([16])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.0.to_out.1.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.1.0.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.1.1.weight -> torch.Size([13312, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.1.1.5.weight -> torch.Size([1664, 6656])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.null_kv -> torch.Size([2, 52])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.norm.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.to_q.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.to_kv.weight -> torch.Size([104, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.rotary_emb.freqs -> torch.Size([16])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.2.0.to_out.1.g -> torch.Size([1664])\n", "Saved model: 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torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.3.1.0.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.3.1.1.weight -> torch.Size([13312, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.3.1.5.weight -> torch.Size([1664, 6656])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.null_kv -> torch.Size([2, 52])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.norm.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.to_q.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.to_kv.weight -> torch.Size([104, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.rotary_emb.freqs -> torch.Size([16])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.0.to_out.1.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.1.0.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.1.1.weight -> torch.Size([13312, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.4.1.5.weight -> torch.Size([1664, 6656])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.null_kv -> torch.Size([2, 52])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.norm.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.to_q.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.to_kv.weight -> torch.Size([104, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.rotary_emb.freqs -> torch.Size([16])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.0.to_out.1.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.1.0.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.1.1.weight -> torch.Size([13312, 1664])\n", "Saved model: diffusion_prior.net.causal_transformer.layers.5.1.5.weight -> torch.Size([1664, 6656])\n", "Saved model: diffusion_prior.net.causal_transformer.norm.g -> torch.Size([1664])\n", "Saved model: diffusion_prior.net.causal_transformer.project_out.weight -> torch.Size([1664, 1664])\n", "Current model: ridge.linears.0.weight -> torch.Size([1024, 2604])\n", "Current model: ridge.linears.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.0.0.weight -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.0.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.0.1.0.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.0.1.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.0.1.3.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.0.1.3.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.1.0.weight -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.1.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.1.1.0.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.1.1.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.1.1.3.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.1.1.3.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.2.0.weight -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.2.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.2.1.0.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.2.1.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.2.1.3.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.2.1.3.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.3.0.weight -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.3.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.3.1.0.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.3.1.0.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks1.3.1.3.weight -> torch.Size([1024, 1024])\n", "Current model: backbone.mixer_blocks1.3.1.3.bias -> torch.Size([1024])\n", "Current model: backbone.mixer_blocks2.0.0.weight -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.0.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.0.1.0.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.0.1.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.0.1.3.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.0.1.3.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.1.0.weight -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.1.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.1.1.0.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.1.1.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.1.1.3.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.1.1.3.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.2.0.weight -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.2.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.2.1.0.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.2.1.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.2.1.3.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.2.1.3.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.3.0.weight -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.3.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.3.1.0.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.3.1.0.bias -> torch.Size([1])\n", "Current model: backbone.mixer_blocks2.3.1.3.weight -> torch.Size([1, 1])\n", "Current model: backbone.mixer_blocks2.3.1.3.bias -> torch.Size([1])\n", "Current model: backbone.backbone_linear.weight -> torch.Size([425984, 1024])\n", "Current model: backbone.backbone_linear.bias -> torch.Size([425984])\n", "Current model: backbone.clip_proj.0.weight -> torch.Size([1664])\n", "Current model: backbone.clip_proj.0.bias -> torch.Size([1664])\n", "Current model: backbone.clip_proj.2.weight -> torch.Size([1664, 1664])\n", "Current model: backbone.clip_proj.2.bias -> torch.Size([1664])\n", "Current model: backbone.clip_proj.3.weight -> torch.Size([1664])\n", "Current model: backbone.clip_proj.3.bias -> torch.Size([1664])\n", "Current model: backbone.clip_proj.5.weight -> torch.Size([1664, 1664])\n", "Current model: backbone.clip_proj.5.bias -> torch.Size([1664])\n", "Current model: backbone.clip_proj.6.weight -> torch.Size([1664])\n", "Current model: backbone.clip_proj.6.bias -> torch.Size([1664])\n", "Current model: backbone.clip_proj.8.weight -> torch.Size([1664, 1664])\n", "Current model: backbone.clip_proj.8.bias -> torch.Size([1664])\n", "Current model: diffusion_prior.noise_scheduler.betas -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.alphas_cumprod -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.alphas_cumprod_prev -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.sqrt_alphas_cumprod -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.sqrt_one_minus_alphas_cumprod -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.log_one_minus_alphas_cumprod -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.sqrt_recip_alphas_cumprod -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.sqrt_recipm1_alphas_cumprod -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.posterior_variance -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.posterior_log_variance_clipped -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.posterior_mean_coef1 -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.posterior_mean_coef2 -> torch.Size([100])\n", "Current model: diffusion_prior.noise_scheduler.p2_loss_weight -> torch.Size([100])\n", "Current model: diffusion_prior.net.learned_query -> torch.Size([256, 1664])\n", "Current model: diffusion_prior.net.null_brain_embeds -> torch.Size([256, 1664])\n", "Current model: diffusion_prior.net.null_image_embed -> torch.Size([256, 1664])\n", "Current model: diffusion_prior.net.to_time_embeds.0.1.net.0.0.weight -> torch.Size([3328, 1664])\n", "Current model: diffusion_prior.net.to_time_embeds.0.1.net.0.0.bias -> torch.Size([3328])\n", "Current model: diffusion_prior.net.to_time_embeds.0.1.net.1.0.weight -> torch.Size([3328, 3328])\n", "Current model: diffusion_prior.net.to_time_embeds.0.1.net.1.0.bias -> torch.Size([3328])\n", "Current model: diffusion_prior.net.to_time_embeds.0.1.net.2.weight -> torch.Size([1664, 3328])\n", "Current model: diffusion_prior.net.to_time_embeds.0.1.net.2.bias -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.rel_pos_bias.relative_attention_bias.weight -> torch.Size([32, 32])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.null_kv -> torch.Size([2, 52])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.to_q.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.to_kv.weight -> torch.Size([104, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.rotary_emb.freqs -> torch.Size([16])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.0.to_out.1.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.1.0.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.1.1.weight -> torch.Size([13312, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.0.1.5.weight -> torch.Size([1664, 6656])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.null_kv -> torch.Size([2, 52])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.to_q.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.to_kv.weight -> torch.Size([104, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.rotary_emb.freqs -> torch.Size([16])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.0.to_out.1.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.1.0.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.1.1.weight -> torch.Size([13312, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.1.1.5.weight -> torch.Size([1664, 6656])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.null_kv -> torch.Size([2, 52])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.to_q.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.to_kv.weight -> torch.Size([104, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.rotary_emb.freqs -> torch.Size([16])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.0.to_out.1.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.1.0.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.1.1.weight -> torch.Size([13312, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.2.1.5.weight -> torch.Size([1664, 6656])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.null_kv -> torch.Size([2, 52])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.to_q.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.to_kv.weight -> torch.Size([104, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.rotary_emb.freqs -> torch.Size([16])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.0.to_out.1.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.1.0.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.1.1.weight -> torch.Size([13312, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.3.1.5.weight -> torch.Size([1664, 6656])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.null_kv -> torch.Size([2, 52])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.to_q.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.to_kv.weight -> torch.Size([104, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.rotary_emb.freqs -> torch.Size([16])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.0.to_out.1.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.1.0.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.1.1.weight -> torch.Size([13312, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.4.1.5.weight -> torch.Size([1664, 6656])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.null_kv -> torch.Size([2, 52])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.to_q.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.to_kv.weight -> torch.Size([104, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.rotary_emb.freqs -> torch.Size([16])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.to_out.0.weight -> torch.Size([1664, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.0.to_out.1.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.1.0.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.1.1.weight -> torch.Size([13312, 1664])\n", "Current model: diffusion_prior.net.causal_transformer.layers.5.1.5.weight -> torch.Size([1664, 6656])\n", "Current model: diffusion_prior.net.causal_transformer.norm.g -> torch.Size([1664])\n", "Current model: diffusion_prior.net.causal_transformer.project_out.weight -> torch.Size([1664, 1664])\n", "ckpt loaded!\n" ] } ], "source": [ "# Load pretrained model ckpt\n", "tag='last'\n", "outdir = os.path.abspath(f'/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/train_logs/{model_name}')\n", "print(f\"\\n---loading {outdir}/{tag}.pth ckpt---\\n\")\n", "print(f\"hidden_dim = {hidden_dim}\")\n", "checkpoint = torch.load(outdir+f'/{tag}.pth', map_location='cpu')\n", "state_dict = checkpoint['model_state_dict']\n", "\n", "# Print the keys (layer names) and parameter shapes of saved+current model, use for debugging in case model_state_dict errors\n", "# for key, value in state_dict.items():\n", "# print(f\"Saved model: {key} -> {value.shape}\")\n", "# curr_state_dict = model.state_dict()\n", "# for key, value in curr_state_dict.items():\n", "# print(f\"Current model: {key} -> {value.shape}\")\n", "\n", "model.load_state_dict(state_dict, strict=True)\n", "del checkpoint\n", "print(\"ckpt loaded!\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "f726f617-39f5-49e2-8d0c-d11d27d01c30", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [1664, 1664] now.\n", "WARNING:sgm.modules.attention:SpatialTransformer: Found context dims [1664] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [1664, 1664] now.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Initialized embedder #0: FrozenOpenCLIPImageEmbedder with 1909889025 params. Trainable: False\n", "Initialized embedder #1: ConcatTimestepEmbedderND with 0 params. Trainable: False\n", "Initialized embedder #2: ConcatTimestepEmbedderND with 0 params. Trainable: False\n", "vector_suffix torch.Size([1, 1024])\n" ] } ], "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": 13, "id": "68abd440-7e6b-4023-9dc8-05b1b5c0baa9", "metadata": {}, "outputs": [], "source": [ "# 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(\"/scratch/gpfs/ri4541/real_time_mindEye2/coco\")\n", "clip_text_model = GitForCausalLMClipEmb.from_pretrained(\"/scratch/gpfs/ri4541/real_time_mindEye2/coco\")\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(\"bigG_to_L_epoch8.pth\", map_location='cpu')['model_state_dict']\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": 14, "id": "c6a706a3-d151-4643-bb34-7d08aa7361c8", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/10 [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", " 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, 12, figsize=(10, 4))\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", " 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}/MST_ID.npy\", MST_ID)\n", "torch.save(all_images.cpu(),f\"{eval_dir}/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}/test_image_indices.pt\")\n", "# torch.save(repeats_in_test, f\"{eval_dir}/repeats_in_test.pt\")\n", "torch.save(all_recons,f\"{eval_dir}/all_recons.pt\")\n", "if clip_scale>0:\n", " torch.save(all_clipvoxels,f\"{eval_dir}/all_clipvoxels.pt\")\n", "torch.save(all_prior_out,f\"{eval_dir}/all_prior_out.pt\")\n", "torch.save(all_predcaptions,f\"{eval_dir}/all_predcaptions.pt\")\n", "print(f\"saved {model_name} outputs!\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "73b243d7-6552-4fc8-bef7-d5ad03b17cb2", "metadata": {}, "outputs": [], "source": [ "# # 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", "# eval_dir = f\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/{model_name}\"\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\"/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/evals/{model_name}/{model_name}_MST_ID.npy\", MST_ID)\n", "# torch.save(all_images.cpu(),f\"{eval_dir}/all_images.pt\")\n", "# torch.save(all_recons,f\"{eval_dir}/all_recons.pt\")\n", "# if clip_scale>0:\n", "# torch.save(all_clipvoxels,f\"{eval_dir}/all_clipvoxels.pt\")\n", "# torch.save(all_prior_out,f\"{eval_dir}/all_prior_out.pt\")\n", "# torch.save(all_predcaptions,f\"{eval_dir}/all_predcaptions.pt\")\n", "# print(f\"saved {model_name} outputs!\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "6c6856c3-9205-48f5-bfb2-7e0099f429a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([300, 3, 256, 256])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_images.shape" ] }, { "cell_type": "code", "execution_count": 18, "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": 19, "id": "7fa41429-ab6a-4aa6-96b9-5c963016b33a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "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": "d553a7b3-9bdf-44b3-a0bf-398cf5cf402b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "rt_mindEye2 [~/.conda/envs/rt_mindEye2/]", "language": "python", "name": "conda_rt_mindeye2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }