{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f4d95fac-ac1d-473c-ab96-650f76e6aaf5", "metadata": { "tags": [] }, "outputs": [], "source": [ "# # Code to convert this notebook to .py if you want to run it via command line or with Slurm\n", "#from subprocess import call\n", "#command = \"jupyter nbconvert Train_MLPMixer-Copy1.ipynb --to python\"\n", "#call(command,shell=True)" ] }, { "cell_type": "markdown", "id": "b0f0f4f3", "metadata": {}, "source": [ "# Import packages & functions" ] }, { "cell_type": "code", "execution_count": 2, "id": "5bad764b-45c1-45ce-a716-8d055e09821a", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2023-11-03 11:37:15,856] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import 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", "\n", "import webdataset as wds\n", "import gc\n", "\n", "import matplotlib.pyplot as plt\n", "import torch\n", "import torch.nn as nn\n", "from torchvision import transforms\n", "\n", "from accelerate import Accelerator, DeepSpeedPlugin\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" ] }, { "cell_type": "code", "execution_count": 3, "id": "cc5d2e32-6027-4a19-bef4-5ca068db35bb", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LOCAL RANK 0\n", "Setting batch_size to 128\n", "[2023-11-03 11:37:18,062] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented\n", "[2023-11-03 11:37:18,062] [INFO] [comm.py:594:init_distributed] cdb=None\n", "[2023-11-03 11:37:18,063] [INFO] [comm.py:625:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl\n" ] } ], "source": [ "### 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", "num_devices = torch.cuda.device_count()\n", "if num_devices==0: num_devices = 1\n", "\n", "# ## UNCOMMENT BELOW SECTION AND COMMENT OUT DEEPSPEED SECTION TO AVOID USING DEEPSPEED ###\n", "# accelerator = Accelerator(split_batches=False, mixed_precision=\"fp16\")\n", "# global_batch_size = batch_size = 128\n", "# data_type = torch.float16 # change depending on your mixed_precision\n", "\n", "### DEEPSPEED INITIALIZATION ###\n", "if num_devices <= 1 and utils.is_interactive():\n", " global_batch_size = batch_size = 128\n", " print(f\"Setting batch_size to {batch_size}\")\n", " # can emulate a distributed environment for deepspeed to work in jupyter notebook\n", " os.environ[\"MASTER_ADDR\"] = \"localhost\"\n", " os.environ[\"MASTER_PORT\"] = str(np.random.randint(10000)+9000)\n", " os.environ[\"RANK\"] = \"0\"\n", " os.environ[\"LOCAL_RANK\"] = \"0\"\n", " os.environ[\"WORLD_SIZE\"] = \"1\"\n", " os.environ[\"GLOBAL_BATCH_SIZE\"] = str(global_batch_size) # set this to your batch size!\n", "else:\n", " global_batch_size = os.environ[\"GLOBAL_BATCH_SIZE\"] \n", " batch_size = int(os.environ[\"GLOBAL_BATCH_SIZE\"]) // num_devices\n", "\n", "# alter the deepspeed config according to your global and local batch size\n", "if local_rank == 0:\n", " with open('deepspeed_config_stage2.json', 'r') as file:\n", " config = json.load(file)\n", " config['train_batch_size'] = int(os.environ[\"GLOBAL_BATCH_SIZE\"])\n", " config['train_micro_batch_size_per_gpu'] = batch_size\n", " config['bf16'] = {'enabled': False}\n", " config['fp16'] = {'enabled': True}\n", " with open('deepspeed_config_stage2.json', 'w') as file:\n", " json.dump(config, file)\n", "else:\n", " # give some time for the local_rank=0 gpu to prep new deepspeed config file\n", " time.sleep(10)\n", "deepspeed_plugin = DeepSpeedPlugin(\"deepspeed_config_stage2_cpuoffload.json\")\n", "accelerator = Accelerator(split_batches=False, deepspeed_plugin=deepspeed_plugin)" ] }, { "cell_type": "code", "execution_count": 4, "id": "b767ab6f-d4a9-47a5-b3bf-f56bf6760c0c", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PID of this process = 418110\n", "device: cuda:0\n", "Distributed environment: DEEPSPEED Backend: nccl\n", "Num processes: 1\n", "Process index: 0\n", "Local process index: 0\n", "Device: cuda:0\n", "\n", "Mixed precision type: fp16\n", "ds_config: {'bf16': {'enabled': False}, 'fp16': {'enabled': True}, 'zero_optimization': {'stage': 2, 'contiguous_gradients': True, 'stage3_gather_16bit_weights_on_model_save': True, 'stage3_max_live_parameters': 1000000000.0, 'stage3_max_reuse_distance': 1000000000.0, 'stage3_prefetch_bucket_size': 10000000.0, 'stage3_param_persistence_threshold': 100000.0, 'reduce_bucket_size': 10000000.0, 'sub_group_size': 1000000000.0, 'offload_optimizer': {'device': 'cpu', 'nvme_path': '/scratch', 'pin_memory': True}, 'offload_param': {'device': 'none', 'nvme_path': '/scratch', 'buffer_size': 4000000000.0, 'pin_memory': True}}, 'aio': {'block_size': 26214400, 'queue_depth': 32, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}, 'gradient_accumulation_steps': 1, 'gradient_clipping': 1.0, 'steps_per_print': inf, 'train_batch_size': 256, 'train_micro_batch_size_per_gpu': 32, 'wall_clock_breakdown': False, 'zero_allow_untested_optimizer': True}\n", "\n", "distributed = True num_devices = 1 local rank = 0 world size = 1 data_type = torch.float16\n" ] } ], "source": [ "print(\"PID of this process =\",os.getpid())\n", "device = accelerator.device\n", "print(\"device:\",device)\n", "num_workers = num_devices\n", "print(accelerator.state)\n", "world_size = accelerator.state.num_processes\n", "distributed = not accelerator.state.distributed_type == 'NO'\n", "\n", "# set data_type to match your mixed precision (automatically set based on deepspeed config)\n", "if accelerator.mixed_precision == \"bf16\":\n", " data_type = torch.bfloat16\n", "elif accelerator.mixed_precision == \"fp16\":\n", " data_type = torch.float16\n", "else:\n", " data_type = torch.float32\n", "\n", "print(\"distributed =\",distributed, \"num_devices =\", num_devices, \"local rank =\", local_rank, \"world size =\", world_size, \"data_type =\", data_type)\n", "print = accelerator.print # only print if local_rank=0" ] }, { "cell_type": "markdown", "id": "9018b82b-c054-4463-9527-4b0c2a75bda6", "metadata": { "tags": [] }, "source": [ "# Configurations" ] }, { "cell_type": "code", "execution_count": 5, "id": "2b61fec7-72a0-4b67-86da-1375f1d9fbd3", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model_name: CUIEjK9AY8_interactive\n", "['--data_path=/fsx/proj-fmri/shared/mindeyev2_dataset', '--model_name=CUIEjK9AY8_interactive', '--subj=1', '--batch_size=128', '--no-blurry_recon', '--no-depth_recon', '--hidden_dim=4096', '--clip_scale=1.', '--blur_scale=100.', '--depth_scale=100.', '--max_lr=3e-4', '--mixup_pct=.66', '--num_epochs=12', '--ckpt_interval=999', '--no-use_image_aug', '--no-ckpt_saving']\n" ] } ], "source": [ "# if running this interactively, can specify jupyter_args here for argparser to use\n", "if utils.is_interactive():\n", " # create random model_name\n", " model_name = ''.join(random.choices(string.ascii_letters + string.digits, k=10))\n", " model_name = model_name + \"_interactive\"\n", " print(\"model_name:\", model_name)\n", "\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=/fsx/proj-fmri/shared/mindeyev2_dataset \\\n", " --model_name={model_name} \\\n", " --subj=1 --batch_size={batch_size} --no-blurry_recon --no-depth_recon --hidden_dim=4096 \\\n", " --clip_scale=1. --blur_scale=100. --depth_scale=100. \\\n", " --max_lr=3e-4 --mixup_pct=.66 --num_epochs=12 --ckpt_interval=999 --no-use_image_aug --no-ckpt_saving\"\n", "\n", " jupyter_args = jupyter_args.split()\n", " print(jupyter_args)\n", " \n", " from IPython.display import clear_output # function to clear print outputs in cell\n", " %load_ext autoreload \n", " # this allows you to change functions in models.py or utils.py and have this notebook automatically update with your revisions\n", " %autoreload 2 " ] }, { "cell_type": "code", "execution_count": 6, "id": "2028bdf0-2f41-46d9-b6e7-86b870dbf16c", "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=\"/fsx/proj-fmri/shared/natural-scenes-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=[1,2,5,7],\n", ")\n", "parser.add_argument(\n", " \"--batch_size\", type=int, default=32,\n", " help=\"Batch size can be increased by 10x if only training v2c and not diffusion diffuser\",\n", ")\n", "parser.add_argument(\n", " \"--wandb_log\",action=argparse.BooleanOptionalAction,default=False,\n", " help=\"whether to log to wandb\",\n", ")\n", "parser.add_argument(\n", " \"--resume_from_ckpt\",action=argparse.BooleanOptionalAction,default=False,\n", " help=\"if not using wandb and want to resume from a ckpt\",\n", ")\n", "parser.add_argument(\n", " \"--wandb_project\",type=str,default=\"stability\",\n", " help=\"wandb project name\",\n", ")\n", "parser.add_argument(\n", " \"--mixup_pct\",type=float,default=.33,\n", " help=\"proportion of way through training when to switch from BiMixCo to SoftCLIP\",\n", ")\n", "parser.add_argument(\n", " \"--blurry_recon\",action=argparse.BooleanOptionalAction,default=True,\n", " help=\"whether to output blurry reconstructions\",\n", ")\n", "parser.add_argument(\n", " \"--depth_recon\",action=argparse.BooleanOptionalAction,default=True,\n", " help=\"whether to output depth reconstructions\",\n", ")\n", "parser.add_argument(\n", " \"--blur_scale\",type=float,default=100.,\n", " help=\"multiply loss from blurry recons by this number\",\n", ")\n", "parser.add_argument(\n", " \"--depth_scale\",type=float,default=100.,\n", " help=\"multiply loss from depth recons by this number\",\n", ")\n", "parser.add_argument(\n", " \"--clip_scale\",type=float,default=1.,\n", " help=\"multiply contrastive loss by this number\",\n", ")\n", "parser.add_argument(\n", " \"--use_image_aug\",action=argparse.BooleanOptionalAction,default=True,\n", " help=\"whether to use image augmentation\",\n", ")\n", "parser.add_argument(\n", " \"--num_epochs\",type=int,default=120,\n", " help=\"number of epochs of training\",\n", ")\n", "parser.add_argument(\n", " \"--hidden_dim\",type=int,default=4096,\n", ")\n", "parser.add_argument(\n", " \"--lr_scheduler_type\",type=str,default='cycle',choices=['cycle','linear'],\n", ")\n", "parser.add_argument(\n", " \"--ckpt_saving\",action=argparse.BooleanOptionalAction,default=True,\n", ")\n", "parser.add_argument(\n", " \"--ckpt_interval\",type=int,default=5,\n", " help=\"save backup ckpt and reconstruct every x epochs\",\n", ")\n", "parser.add_argument(\n", " \"--seed\",type=int,default=42,\n", ")\n", "parser.add_argument(\n", " \"--max_lr\",type=float,default=3e-4,\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)" ] }, { "cell_type": "code", "execution_count": 7, "id": "60cd7f2c-37fd-426b-a0c6-633e51bc4c4d", "metadata": { "tags": [] }, "outputs": [], "source": [ "outdir = os.path.abspath(f'../train_logs/{model_name}')\n", "if not os.path.exists(outdir) and ckpt_saving:\n", " os.makedirs(outdir,exist_ok=True)\n", "if use_image_aug:\n", " import kornia\n", " from kornia.augmentation.container import AugmentationSequential\n", " img_augment = AugmentationSequential(\n", " kornia.augmentation.RandomResizedCrop((224,224), (0.6,1), p=0.3),\n", " kornia.augmentation.Resize((224, 224)),\n", " kornia.augmentation.RandomHorizontalFlip(p=0.3),\n", " kornia.augmentation.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.3),\n", " kornia.augmentation.RandomGrayscale(p=0.3),\n", " same_on_batch=False,\n", " data_keys=[\"input\"],\n", " )" ] }, { "cell_type": "markdown", "id": "42d13c25-1369-4c49-81d4-83d713586096", "metadata": { "tags": [] }, "source": [ "# Prep data, models, and dataloaders" ] }, { "cell_type": "markdown", "id": "1c023f24-5233-4a15-a2f5-78487b3a8546", "metadata": {}, "source": [ "## Dataloader" ] }, { "cell_type": "code", "execution_count": 8, "id": "81084834-035f-4465-ad59-59e6b806a2f5", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/fsx/proj-fmri/shared/mindeyev2_dataset/wds/subj01/train/{0..36}.tar\n", "/fsx/proj-fmri/shared/mindeyev2_dataset/wds/subj01/test/0.tar\n" ] } ], "source": [ "if subj==1:\n", " num_train = 24958\n", " num_test = 2770\n", "test_batch_size = num_test\n", "\n", "def my_split_by_node(urls): return urls\n", " \n", "train_url = f\"{data_path}/wds/subj0{subj}/train/\" + \"{0..36}.tar\"\n", "# train_url = f\"{data_path}/wds/subj0{subj}/train/\" + \"{0..1}.tar\"\n", "print(train_url)\n", "\n", "train_data = wds.WebDataset(train_url,resampled=False,nodesplitter=my_split_by_node)\\\n", " .shuffle(750, initial=1500, rng=random.Random(42))\\\n", " .decode(\"torch\")\\\n", " .rename(behav=\"behav.npy\", past_behav=\"past_behav.npy\", future_behav=\"future_behav.npy\", olds_behav=\"olds_behav.npy\")\\\n", " .to_tuple(*[\"behav\", \"past_behav\", \"future_behav\", \"olds_behav\"])\n", "train_dl = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=False, drop_last=True, pin_memory=True)\n", "\n", "test_url = f\"{data_path}/wds/subj0{subj}/test/\" + \"0.tar\"\n", "print(test_url)\n", "\n", "test_data = wds.WebDataset(test_url,resampled=False,nodesplitter=my_split_by_node)\\\n", " .shuffle(750, initial=1500, rng=random.Random(42))\\\n", " .decode(\"torch\")\\\n", " .rename(behav=\"behav.npy\", past_behav=\"past_behav.npy\", future_behav=\"future_behav.npy\", olds_behav=\"olds_behav.npy\")\\\n", " .to_tuple(*[\"behav\", \"past_behav\", \"future_behav\", \"olds_behav\"])\n", "test_dl = torch.utils.data.DataLoader(test_data, batch_size=test_batch_size, shuffle=False, drop_last=True, pin_memory=True)" ] }, { "cell_type": "markdown", "id": "203b060a-2dd2-4c35-929b-c576be82eb52", "metadata": {}, "source": [ "### check dataloaders are working" ] }, { "cell_type": "code", "execution_count": 9, "id": "e7a9c68c-c3c9-4080-bd99-067c4486dc37", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 2770 2770\n", "---\n", "\n", "194 24960 24960\n" ] } ], "source": [ "test_vox_indices = []\n", "test_73k_images = []\n", "for test_i, (behav, past_behav, future_behav, old_behav) in enumerate(test_dl):\n", " test_vox_indices = np.append(test_vox_indices, behav[:,0,5].cpu().numpy())\n", " test_73k_images = np.append(test_73k_images, behav[:,0,0].cpu().numpy())\n", "test_vox_indices = test_vox_indices.astype(np.int16)\n", "print(test_i, (test_i+1) * test_batch_size, len(test_vox_indices))\n", "print(\"---\\n\")\n", "\n", "train_vox_indices = []\n", "train_73k_images = []\n", "for train_i, (behav, past_behav, future_behav, old_behav) in enumerate(train_dl):\n", " train_vox_indices = np.append(train_vox_indices, behav[:,0,5].long().cpu().numpy())\n", " train_73k_images = np.append(train_73k_images, behav[:,0,0].cpu().numpy())\n", "train_vox_indices = train_vox_indices.astype(np.int16)\n", "print(train_i, (train_i+1) * batch_size, len(train_vox_indices))" ] }, { "cell_type": "markdown", "id": "45fad12c-f9fb-4408-8fd4-9bca324ad634", "metadata": {}, "source": [ "## Load data and images" ] }, { "cell_type": "code", "execution_count": 10, "id": "039dd330-7339-4f88-8f00-45f95e47baa0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subj01 betas loaded into memory\n", "voxels torch.Size([27750, 15724])\n", "images torch.Size([73000, 3, 224, 224])\n" ] } ], "source": [ "# load betas\n", "f = h5py.File(f'{data_path}/betas_all_subj0{subj}.hdf5', 'r')\n", "# f = h5py.File(f'{data_path}/betas_subj0{subj}_thresholded_wholebrain.hdf5', 'r')\n", "\n", "voxels = f['betas'][:]\n", "print(f\"subj0{subj} betas loaded into memory\")\n", "voxels = torch.Tensor(voxels).to(\"cpu\").to(data_type)\n", "print(\"voxels\", voxels.shape)\n", "num_voxels = voxels.shape[-1]\n", "\n", "# load orig images\n", "f = h5py.File(f'{data_path}/coco_images_224_float16.hdf5', 'r')\n", "images = f['images'][:]\n", "images = torch.Tensor(images).to(\"cpu\").to(data_type)\n", "print(\"images\", images.shape)" ] }, { "cell_type": "markdown", "id": "10ec4517-dbdf-4ece-98f6-4714d5de4e15", "metadata": {}, "source": [ "## Load models" ] }, { "cell_type": "markdown", "id": "48d6160e-1ee8-4da7-a755-9dbb452a6fa5", "metadata": {}, "source": [ "### CLIP image embeddings model" ] }, { "cell_type": "code", "execution_count": 11, "id": "b0420dc0-199e-4c1a-857d-b1747058b467", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ViT-L/14 cuda:0\n" ] } ], "source": [ "from models import Clipper\n", "clip_model = Clipper(\"ViT-L/14\", device=torch.device(f\"cuda:{local_rank}\"), hidden_state=True, norm_embs=True)\n", "clip_seq_dim = 257\n", "clip_emb_dim = 768 #1024\n", "# hidden_dim = 4096\n", "seq_len = 1 #2 #32 " ] }, { "cell_type": "markdown", "id": "5b79bd38-6990-4504-8d45-4a68d57d8885", "metadata": {}, "source": [ "### SD VAE" ] }, { "cell_type": "code", "execution_count": 12, "id": "01baff79-8114-482b-b115-6f05aa8ad691", "metadata": { "tags": [] }, "outputs": [], "source": [ "# if blurry_recon:\n", "# from diffusers import AutoencoderKL\n", "# autoenc = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, cache_dir=\"/fsx/proj-fmri/shared/cache\")\n", "# # autoenc.load_state_dict(torch.load('../train_logs/sdxl_vae_normed/best.pth')[\"model_state_dict\"])\n", "# autoenc.eval()\n", "# autoenc.requires_grad_(False)\n", "# autoenc.to(device)\n", "# utils.count_params(autoenc)\n", "\n", "if blurry_recon:# or depth_recon:\n", " from diffusers import VQModel\n", " autoenc = VQModel.from_pretrained(\"/fsx/proj-fmri/shared/cache/models--microsoft--vq-diffusion-ithq/snapshots/3f796fb49ee559370dc638dea1d8116af131d993/vqvae\", torch_dtype=data_type)\n", " autoenc.eval()\n", " autoenc.requires_grad_(False)\n", " autoenc.to(device)\n", " utils.count_params(autoenc)" ] }, { "cell_type": "markdown", "id": "120c8eee-9834-437d-bb60-b38faef50138", "metadata": {}, "source": [ "#### downsampled images" ] }, { "cell_type": "code", "execution_count": 13, "id": "6d1ba8dd-64c2-4ac9-947e-725b7f2e3e50", "metadata": { "tags": [] }, "outputs": [], "source": [ "if blurry_recon:\n", " if utils.is_interactive(): display(utils.torch_to_Image(images[[30]]))\n", "\n", " input_batch = images[[30]].to(device)\n", " print(input_batch.shape)\n", "\n", " downsampled_image = nn.functional.interpolate(input_batch, size=(8, 8), mode='bilinear', align_corners=False)\n", " re_upsampled_image = nn.functional.interpolate(downsampled_image, size=(128, 128), mode='nearest')\n", " re_upsampled_enc = autoenc.encode(2*re_upsampled_image-1).latents * 0.18215\n", " print(re_upsampled_enc.shape)\n", " \n", " if utils.is_interactive(): display(utils.torch_to_Image((autoenc.decode(re_upsampled_enc/0.18215).sample / 2 + 0.5).clamp(0,1)))" ] }, { "cell_type": "markdown", "id": "6390a3a8-2bef-4e81-9b82-e154d26a1e1d", "metadata": {}, "source": [ "#### MiDaS depth" ] }, { "cell_type": "code", "execution_count": 14, "id": "f35573e2-95bf-463d-8937-68ad4c2c3c20", "metadata": { "tags": [] }, "outputs": [], "source": [ "if depth_recon:\n", " from controlnet_aux.midas import MidasDetector\n", " \n", " midas_depth = MidasDetector.from_pretrained(\n", " \"valhalla/t2iadapter-aux-models\", filename=\"dpt_large_384.pt\", model_type=\"dpt_large\", cache_dir=\"/fsx/proj-fmri/shared/cache\").to(device)\n", " midas_depth.model.eval()\n", " midas_depth.model.requires_grad_(False)\n", " midas_depth.model.to(device)\n", " pass" ] }, { "cell_type": "code", "execution_count": 15, "id": "ba3f9207-b98e-45da-baa6-5cfcfb2ae958", "metadata": { "tags": [] }, "outputs": [], "source": [ "if depth_recon:\n", " if utils.is_interactive(): display(utils.torch_to_Image(images[[30]]))\n", "\n", " input_batch = images[[30,31]].float().to(device)\n", " print(input_batch.shape)\n", " \n", " midas_emb = midas_depth.model(input_batch).unsqueeze(1)\n", " print(midas_emb.shape)\n", "\n", " prediction = utils.resize(midas_emb, 32) #/30).clamp(0,1).half() # 30 is roughly prediction.max()\n", " print(prediction.shape)\n", " \n", " prediction = (prediction / prediction.view(prediction.shape[0], -1).max(dim=1)[0].view(-1, 1, 1, 1).expand_as(prediction)).half()\n", " midas_emb_size = prediction.flatten(1).shape[1]\n", " print(\"midas_emb\", prediction.shape, prediction.min(), prediction.max())\n", " print(\"midas_emb_size\", midas_emb_size)\n", " \n", " if utils.is_interactive(): display(utils.torch_to_Image(utils.resize(prediction, 224))) \n", "\n", " if blurry_recon:\n", " prediction = utils.resize(midas_emb, 128).half().repeat(1,3,1,1)\n", " prediction = (prediction / prediction.view(prediction.shape[0], -1).max(dim=1)[0].view(-1, 1, 1, 1).expand_as(prediction)).half()\n", " prediction_enc = autoenc.encode(2*prediction-1).latents * 0.18215\n", " print(\"vae midas_emb\", prediction_enc.shape, prediction_enc.min(), prediction_enc.max())\n", " \n", " if utils.is_interactive(): display(utils.torch_to_Image((autoenc.decode(prediction_enc/0.18215).sample / 2 + 0.5).clamp(0,1)))" ] }, { "cell_type": "markdown", "id": "260e5e4a-f697-4b2c-88fc-01f6a54886c0", "metadata": {}, "source": [ "### MindEye modules" ] }, { "cell_type": "code", "execution_count": 16, "id": "c44c271b-173f-472e-b059-a2eda0f4c4c5", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "MindEyeModule()" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "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", "model" ] }, { "cell_type": "code", "execution_count": 17, "id": "038a5d61-4769-40b9-a004-f4e7b5b38bb0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "param counts:\n", "66,506,752 total\n", "66,506,752 trainable\n", "param counts:\n", "66,506,752 total\n", "66,506,752 trainable\n", "torch.Size([2, 1, 15724]) torch.Size([2, 1, 4096])\n" ] } ], "source": [ "time_embedding_dim = 512\n", "\n", "class RidgeRegression(torch.nn.Module):\n", " # make sure to add weight_decay when initializing optimizer\n", " def __init__(self, input_size, out_features): \n", " super(RidgeRegression, self).__init__()\n", " self.out_features = out_features\n", " self.linear = torch.nn.Linear(input_size, out_features)\n", " def forward(self, x):\n", " return self.linear(x)\n", " \n", "model.ridge = RidgeRegression(voxels.shape[1] + time_embedding_dim, out_features=hidden_dim)\n", "utils.count_params(model.ridge)\n", "utils.count_params(model)\n", "\n", "b = torch.randn((2,1,voxels.shape[1]))\n", "time_emb_test = torch.randn((2,1,time_embedding_dim))\n", "print(b.shape, model.ridge(torch.cat((b,time_emb_test),dim=-1)).shape)" ] }, { "cell_type": "code", "execution_count": 59, "id": "ee763515-4853-4c5b-9265-ec5aa1bde971", "metadata": { "tags": [] }, "outputs": [], "source": [ "num_past_voxels = 15\n", "seq_len = 1 + 1" ] }, { "cell_type": "code", "execution_count": 73, "id": "3602c333-d029-465c-8fb4-c3ccffdba6fd", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "param counts:\n", "824,409,280 total\n", "824,409,280 trainable\n", "param counts:\n", "890,923,712 total\n", "890,923,712 trainable\n", "b.shape torch.Size([256, 4, 1024])\n", "torch.Size([256, 257, 768]) torch.Size([1]) torch.Size([1])\n" ] } ], "source": [ "class BrainNetwork(nn.Module):\n", " def __init__(self, out_dim=768, in_dim=15724, seq_len=2, h=4096, n_blocks=4, drop=.15, clip_size=768):\n", " super().__init__()\n", " self.seq_len = seq_len\n", " self.h = h\n", " self.clip_size = clip_size\n", " \n", " # Initial linear layer to match the input dimensions to hidden dimensions\n", " # self.lin0 = nn.Linear(in_dim, seq_len * h)\n", " \n", " # Mixer Blocks\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.clin1 = nn.Linear(h * seq_len, out_dim, bias=True)\n", "\n", " # low-rank matrices\n", " # self.rank = 500\n", " # self.U = nn.Parameter(torch.randn(self.rank, out_dim))\n", " # self.V = nn.Parameter(torch.randn(h * seq_len, self.rank))\n", " # self.S = nn.Parameter(torch.randn(out_dim))\n", "\n", " self.clip_proj = nn.Sequential(\n", " nn.LayerNorm(clip_size),\n", " nn.GELU(),\n", " nn.Linear(clip_size, 2048),\n", " nn.LayerNorm(2048),\n", " nn.GELU(),\n", " nn.Linear(2048, 2048),\n", " nn.LayerNorm(2048),\n", " nn.GELU(),\n", " nn.Linear(2048, clip_size)\n", " )\n", "\n", " if blurry_recon:\n", " # self.blin1 = nn.Sequential(\n", " # nn.Linear(out_dim, 4096, bias=True),\n", " # nn.LayerNorm(4096),\n", " # nn.GELU(),\n", " # nn.Linear(4096, 4096))\n", " self.blin1 = nn.Linear(h*seq_len, 4096)\n", " self.bgroupnorm = nn.GroupNorm(1, 256)\n", " self.bupsampler = Decoder(\n", " in_channels=256,\n", " out_channels=128,\n", " up_block_types=[\"UpDecoderBlock2D\",\"UpDecoderBlock2D\",\"UpDecoderBlock2D\"],\n", " block_out_channels=[32, 64, 128],\n", " layers_per_block=1,\n", " )\n", "\n", " if depth_recon:\n", " # self.dlin1 = nn.Sequential(\n", " # nn.Linear(h, midas_emb_size),\n", " # nn.Sigmoid(),\n", " # )\n", " self.dlin1 = nn.Linear(h*seq_len, 4096)\n", " self.dgroupnorm = nn.GroupNorm(1, 256)\n", " self.dupsampler = Decoder(\n", " in_channels=256,\n", " out_channels=1,#128,\n", " up_block_types=[\"UpDecoderBlock2D\",\"UpDecoderBlock2D\",\"UpDecoderBlock2D\",\"UpDecoderBlock2D\"],\n", " block_out_channels=[32, 64, 128, 256],\n", " layers_per_block=1,\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 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 forward(self, x):\n", " # make empty tensors for blur and depth outputs\n", " b,d = torch.Tensor([0.]), torch.Tensor([0.])\n", " \n", " # Initial linear layer\n", " # x = self.lin0(x)\n", " \n", " # Reshape to seq_len by dim\n", " # x = x.reshape(-1, self.seq_len, self.h)\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", " # Flatten\n", " x = x.reshape(x.size(0), -1)\n", " \n", " c = self.clin1(x)\n", "\n", " # low rank linear to out dim cuts # params by nearly half compared to full linear mapping\n", " # c = (x @ (self.V/100) @ (self.U/100)) + self.S\n", " \n", " c = self.clip_proj(c.reshape(len(c), -1, self.clip_size))\n", "\n", " if blurry_recon:\n", " b = self.blin1(x)\n", " b = b.reshape(len(b), 256, 4, 4)\n", " b = self.bgroupnorm(b)\n", " b = self.bupsampler(b)\n", " \n", " if depth_recon:\n", " d = self.dlin1(x)#.reshape(len(x), 1, 32, 32)\n", " d = d.reshape(len(d), 256, 4, 4)\n", " d = self.dgroupnorm(d)\n", " d = self.dupsampler(d)\n", " \n", " return c, b, d\n", "\n", "\n", "class TimeEmbedding(nn.Module):\n", " def __init__(self, embedding_time_dim=512, num_past_voxels=15):\n", " super().__init__()\n", " self.embedding_time = nn.Embedding(num_past_voxels, embedding_time_dim)\n", " self.num_past_voxels = num_past_voxels\n", " self.embedding_time_dim = embedding_time_dim\n", "\n", " def forward(self, time):\n", " # time is (batch_size,)\n", " time = time.long()\n", " time = self.embedding_time(time)\n", " return time # (batch_size, embedding_time_dim)\n", " \n", "\n", "#model.memory_encoder = MemoryEncoder(in_dim=voxels.shape[1], out_dim=4096, num_past_voxels=15, embedding_time_dim=512)\n", "model.time_embedding = TimeEmbedding(embedding_time_dim=512, num_past_voxels=15)\n", "\n", "model.backbone = BrainNetwork(h=1024, in_dim=1024, seq_len=4, 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", "\n", "# test that the model works on some fake data\n", "b = torch.randn((256,4,1024))\n", "print(\"b.shape\",b.shape)\n", "with torch.no_grad():\n", " clip_, blur_, depth_ = model.backbone(b)\n", "print(clip_.shape, blur_.shape, depth_.shape)\n" ] }, { "cell_type": "code", "execution_count": 70, "id": "fa7aa4ab-64af-45d4-8ed8-259043c16c29", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b.shape torch.Size([256, 2, 4096])\n", "torch.Size([256, 257, 768]) torch.Size([1]) torch.Size([1])\n" ] } ], "source": [ "\n", "voxel_ridge = torch.randn(512,4096)\n", "voxel_ridge = voxel_ridge.view(int(voxel_ridge.shape[0]/seq_len), seq_len, hidden_dim)\n", "print(\"b.shape\",voxel_ridge.shape)\n", "with torch.no_grad():\n", " clip_, blur_, depth_ = model.backbone(voxel_ridge)\n", "print(clip_.shape, blur_.shape, depth_.shape)" ] }, { "cell_type": "code", "execution_count": 64, "id": "e14d0482-dc42-43b9-9ce1-953c32f2c9c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total_steps 2339\n", "\n", "Done with model preparations!\n", "param counts:\n", "1,825,253,952 total\n", "1,825,253,952 trainable\n" ] } ], "source": [ "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n", "opt_grouped_parameters = [\n", " {'params': [p for n, p in model.ridge.named_parameters()], 'weight_decay': 1e-2},\n", " {'params': [p for n, p in model.backbone.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 1e-2},\n", " {'params': [p for n, p in model.backbone.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},\n", "]\n", "\n", "optimizer = torch.optim.AdamW(opt_grouped_parameters, lr=max_lr)\n", "\n", "if lr_scheduler_type == 'linear':\n", " lr_scheduler = torch.optim.lr_scheduler.LinearLR(\n", " optimizer,\n", " total_iters=int(np.floor(num_epochs*(num_train/num_devices/batch_size))),\n", " last_epoch=-1\n", " )\n", "elif lr_scheduler_type == 'cycle':\n", " total_steps=int(np.floor(num_epochs*(num_train/num_devices/batch_size)))\n", " print(\"total_steps\", total_steps)\n", " lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(\n", " optimizer, \n", " max_lr=max_lr,\n", " total_steps=total_steps,\n", " final_div_factor=1000,\n", " last_epoch=-1, pct_start=2/num_epochs\n", " )\n", " \n", "def save_ckpt(tag): \n", " ckpt_path = outdir+f'/{tag}.pth'\n", " print(f'saving {ckpt_path}',flush=True)\n", " unwrapped_model = accelerator.unwrap_model(model)\n", " try:\n", " torch.save({\n", " 'epoch': epoch,\n", " 'model_state_dict': unwrapped_model.state_dict(),\n", " 'optimizer_state_dict': optimizer.state_dict(),\n", " 'lr_scheduler': lr_scheduler.state_dict(),\n", " 'train_losses': losses,\n", " 'test_losses': test_losses,\n", " 'lrs': lrs,\n", " }, ckpt_path)\n", " except:\n", " print(\"Couldn't save... moving on to prevent crashing.\")\n", " del unwrapped_model\n", " \n", "print(\"\\nDone with model preparations!\")\n", "utils.count_params(model)" ] }, { "cell_type": "code", "execution_count": 49, "id": "5d600d8d-6a0d-4584-840e-c89521ff6364", "metadata": {}, "outputs": [], "source": [ "seq_len = 4" ] }, { "cell_type": "code", "execution_count": 57, "id": "7f8eef14-4806-4d11-af12-a36e4acad4df", "metadata": { "tags": [] }, "outputs": [], "source": [ "voxel_ridge = torch.randn(512,4096)\n", "voxel_ridge = voxel_ridge.view(int(voxel_ridge.shape[0]/seq_len), seq_len, hidden_dim)" ] }, { "cell_type": "code", "execution_count": 58, "id": "c578b861-2eb8-4b1c-8b2a-61268b9e9bef", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "torch.Size([128, 4, 4096])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voxel_ridge.shape" ] }, { "cell_type": "code", "execution_count": 55, "id": "3c937d01-eea5-4bad-906a-36d07b3c30a4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([0., 1., 2., 3.])\n", "torch.Size([128, 15, 17])\n", "tensor([ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n", " 1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., -1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n", " 0., 0.], dtype=torch.float64)\n", "torch.Size([128, 3, 15724])\n", "tensor([ True, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, True, False, False, False, False, True, False,\n", " False, False, False, False, True, True, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, True, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, True, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, True, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, True, False, False, False])\n", "tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", " [ 0.3796, 2.2207, 1.7715, ..., 3.0859, 1.5938, 0.6274],\n", " [ 0.3513, 1.1621, 0.0634, ..., 0.7300, 0.2314, 0.1820]],\n", "\n", " [[ 0.6987, -1.0283, 0.8228, ..., -0.0037, -1.2510, -0.5884],\n", " [-0.6133, 0.8877, 1.2207, ..., 0.7803, 1.0908, 0.8496],\n", " [ 0.2534, -0.0533, 0.3386, ..., -0.4102, -0.5864, 0.3982]],\n", "\n", " [[ 0.2139, -0.6875, -0.3225, ..., 0.8423, -0.5718, -0.0623],\n", " [-0.3418, -1.8799, -1.8477, ..., -1.2676, -2.4707, -1.8398],\n", " [-0.3765, 1.1113, -1.2715, ..., 0.5498, 0.0262, -0.4839]],\n", "\n", " ...,\n", "\n", " [[-0.9604, 0.2109, -1.0596, ..., 0.2092, -0.7017, -0.7466],\n", " [ 0.8960, -1.1387, -1.4111, ..., -0.3269, -0.2957, -0.5659],\n", " [ 2.4707, 0.8105, 0.7910, ..., 0.6099, 1.0049, 0.3572]],\n", "\n", " [[ 0.8110, -0.8374, -0.8813, ..., 0.2411, 0.5176, 1.0039],\n", " [-0.0218, -0.6675, 0.0044, ..., 0.2050, 0.2045, 0.7485],\n", " [-0.3379, -1.2100, -0.0176, ..., -0.4167, -0.3860, -0.1342]],\n", "\n", " [[-0.2690, 0.8086, 0.2878, ..., 1.0840, 0.8159, -0.7021],\n", " [ 0.3433, 0.2678, -0.9961, ..., -0.2805, -1.2490, -0.7988],\n", " [ 0.0687, 0.5952, 0.0823, ..., -0.0443, 0.6401, -0.4092]]],\n", " dtype=torch.float16)\n", "torch.Size([128, 15, 17])\n", "tensor([ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n", " 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., -1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.], dtype=torch.float64)\n", "torch.Size([128, 3, 15724])\n", "tensor([False, False, False, False, True, False, False, False, False, False,\n", " True, False, False, False, False, False, False, True, False, False,\n", " True, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, True, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, True, False,\n", " False, False, False, False, False, False, False, False, False, True,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, True, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False])\n", "tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", " [ 0.3796, 2.2207, 1.7715, ..., 3.0859, 1.5938, 0.6274],\n", " [ 0.3513, 1.1621, 0.0634, ..., 0.7300, 0.2314, 0.1820]],\n", "\n", " [[ 0.6987, -1.0283, 0.8228, ..., -0.0037, -1.2510, -0.5884],\n", " [-0.6133, 0.8877, 1.2207, ..., 0.7803, 1.0908, 0.8496],\n", " [ 0.2534, -0.0533, 0.3386, ..., -0.4102, -0.5864, 0.3982]],\n", "\n", " [[ 0.2139, -0.6875, -0.3225, ..., 0.8423, -0.5718, -0.0623],\n", " [-0.3418, -1.8799, -1.8477, ..., -1.2676, -2.4707, -1.8398],\n", " [-0.3765, 1.1113, -1.2715, ..., 0.5498, 0.0262, -0.4839]],\n", "\n", " ...,\n", "\n", " [[-0.9604, 0.2109, -1.0596, ..., 0.2092, -0.7017, -0.7466],\n", " [ 0.8960, -1.1387, -1.4111, ..., -0.3269, -0.2957, -0.5659],\n", " [ 2.4707, 0.8105, 0.7910, ..., 0.6099, 1.0049, 0.3572]],\n", "\n", " [[ 0.8110, -0.8374, -0.8813, ..., 0.2411, 0.5176, 1.0039],\n", " [-0.0218, -0.6675, 0.0044, ..., 0.2050, 0.2045, 0.7485],\n", " [-0.3379, -1.2100, -0.0176, ..., -0.4167, -0.3860, -0.1342]],\n", "\n", " [[-0.2690, 0.8086, 0.2878, ..., 1.0840, 0.8159, -0.7021],\n", " [ 0.3433, 0.2678, -0.9961, ..., -0.2805, -1.2490, -0.7988],\n", " [ 0.0687, 0.5952, 0.0823, ..., -0.0443, 0.6401, -0.4092]]],\n", " dtype=torch.float16)\n", "torch.Size([128, 15, 17])\n", "tensor([ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., -1., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n", " 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n", " 1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 1., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n", " 0., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0.], dtype=torch.float64)\n", "torch.Size([128, 3, 15724])\n", "tensor([False, False, False, True, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, True, False, False, False, False, False, False, True,\n", " False, False, False, False, False, False, True, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, True,\n", " False, False, False, False, True, False, False, False, False, False,\n", " True, False, False, False, True, False, True, False, False, True,\n", " True, False, False, False, False, False, False, False, False, False,\n", " False, False, False, True, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False])\n", "tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", " [ 0.3796, 2.2207, 1.7715, ..., 3.0859, 1.5938, 0.6274],\n", " [ 0.3513, 1.1621, 0.0634, ..., 0.7300, 0.2314, 0.1820]],\n", "\n", " [[ 0.6987, -1.0283, 0.8228, ..., -0.0037, -1.2510, -0.5884],\n", " [-0.6133, 0.8877, 1.2207, ..., 0.7803, 1.0908, 0.8496],\n", " [ 0.2534, -0.0533, 0.3386, ..., -0.4102, -0.5864, 0.3982]],\n", "\n", " [[ 0.2139, -0.6875, -0.3225, ..., 0.8423, -0.5718, -0.0623],\n", " [-0.3418, -1.8799, -1.8477, ..., -1.2676, -2.4707, -1.8398],\n", " [-0.3765, 1.1113, -1.2715, ..., 0.5498, 0.0262, -0.4839]],\n", "\n", " ...,\n", "\n", " [[-0.9604, 0.2109, -1.0596, ..., 0.2092, -0.7017, -0.7466],\n", " [ 0.8960, -1.1387, -1.4111, ..., -0.3269, -0.2957, -0.5659],\n", " [ 2.4707, 0.8105, 0.7910, ..., 0.6099, 1.0049, 0.3572]],\n", "\n", " [[ 0.8110, -0.8374, -0.8813, ..., 0.2411, 0.5176, 1.0039],\n", " [-0.0218, -0.6675, 0.0044, ..., 0.2050, 0.2045, 0.7485],\n", " [-0.3379, -1.2100, -0.0176, ..., -0.4167, -0.3860, -0.1342]],\n", "\n", " [[-0.2690, 0.8086, 0.2878, ..., 1.0840, 0.8159, -0.7021],\n", " [ 0.3433, 0.2678, -0.9961, ..., -0.2805, -1.2490, -0.7988],\n", " [ 0.0687, 0.5952, 0.0823, ..., -0.0443, 0.6401, -0.4092]]],\n", " dtype=torch.float16)\n" ] } ], "source": [ "pp = None\n", "for train_i, (behav, past_behav, future_behav, old_behav) in enumerate(train_dl):\n", " with torch.cuda.amp.autocast(dtype=data_type):\n", " #optimizer.zero_grad()\n", "\n", " voxel = voxels[behav[:,0,5].cpu().long()]#.to(device)\n", " image = images[behav[:,0,0].cpu().long()].float()#.to(device).float()\n", "\n", " past_15_voxels = voxels[past_behav[:,:seq_len-1,5].cpu().long()]#.to(device) # batch_size, 15, 15279\n", " past_15_times = torch.Tensor([i for i in range(seq_len)])#.to(device) # 15\n", " \n", " print(past_15_times)\n", " #for past in range(1):\n", " # past_voxel = voxels[past_behav[:,past,5].cpu().long()].to(device)\n", " \"\"\"\n", " #if blurry_recon:\n", " # blurry_image_enc = autoenc.encode(2*utils.resize(image,128)-1).latent_dist.mode() * 0.18215\n", " blurry_image_enc = autoenc.encode(2*utils.resize(add_saturation(image),128)-1).latents * 0.18215\n", "\n", " if depth_recon:\n", " # depth_images = utils.resize(midas_depth.model(image).unsqueeze(1).repeat(1,3,1,1), 128)\n", " depth_images = utils.resize(midas_depth.model(image).unsqueeze(1), 32)\n", " depth_images = (depth_images / depth_images.view(depth_images.shape[0], -1).max(dim=1)[0].view(-1, 1, 1, 1).expand_as(depth_images)).half()\n", " depth_image_enc = depth_images # autoenc.encode(2*depth_images-1).latents * 0.18215\n", "\n", " if use_image_aug: \n", " image = img_augment(image)\n", "\n", " clip_target = clip_model.embed_image(image)\n", " assert not torch.any(torch.isnan(clip_target))\n", "\n", " if epoch < int(mixup_pct * num_epochs):\n", " voxel, perm, betas, select = utils.mixco(voxel)\n", " past_voxel, _, _, _ = utils.mixco(voxel, perm=perm, betas=betas, select=select)\n", " \"\"\"\n", " for p in range(seq_len-1):\n", " print(past_behav.shape) #128, 15, 17\n", " print(past_behav[:,p,-1])\n", " print(past_15_voxels.shape) # 128, 1, 15724\n", " mask = past_behav[:,p,-1] == torch.ones_like(past_behav[:,p,-1])\n", " print(mask) # 128\n", " past_15_voxels[mask, p, :] = torch.zeros_like(past_15_voxels[0, p, :])\n", " print(past_15_voxels)\n", " pp = past_15_voxels\n", " \n", " break" ] }, { "cell_type": "code", "execution_count": 54, "id": "5c7eb009-9fcf-4e35-9e1b-c0ca6418b6b8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "tensor([0., 0., 0., ..., 0., 0., 0.], dtype=torch.float16)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp[20, 0, :]" ] }, { "cell_type": "markdown", "id": "983f458b-35b8-49f2-b6db-80296cece730", "metadata": {}, "source": [ "# Weights and Biases" ] }, { "cell_type": "code", "execution_count": 66, "id": "0a25a662-daa8-4de9-9233-8364800fcb6b", "metadata": {}, "outputs": [], "source": [ "if local_rank==0 and wandb_log: # only use main process for wandb logging\n", " import wandb\n", " wandb_project = 'mindeyev2'\n", " wandb_run = model_name\n", " wandb_notes = ''\n", " \n", " print(f\"wandb {wandb_project} run {wandb_run}\")\n", " wandb.login(host='https://stability.wandb.io')#, relogin=True)\n", " wandb_config = {\n", " \"model_name\": model_name,\n", " \"global_batch_size\": global_batch_size,\n", " \"batch_size\": batch_size,\n", " \"num_epochs\": num_epochs,\n", " \"clip_scale\": clip_scale,\n", " \"blur_scale\": blur_scale,\n", " \"use_image_aug\": use_image_aug,\n", " \"max_lr\": max_lr,\n", " \"mixup_pct\": mixup_pct,\n", " \"num_train\": num_train,\n", " \"num_test\": num_test,\n", " \"ckpt_interval\": ckpt_interval,\n", " \"ckpt_saving\": ckpt_saving,\n", " \"seed\": seed,\n", " \"distributed\": distributed,\n", " \"num_devices\": num_devices,\n", " \"world_size\": world_size,\n", " \"train_url\": train_url,\n", " \"test_url\": test_url,\n", " }\n", " print(\"wandb_config:\\n\",wandb_config)\n", " if True: # wandb_auto_resume\n", " print(\"wandb_id:\",model_name)\n", " wandb.init(\n", " id = model_name,\n", " project=wandb_project,\n", " name=wandb_run,\n", " config=wandb_config,\n", " notes=wandb_notes,\n", " resume=\"allow\",\n", " )\n", " else:\n", " wandb.init(\n", " project=wandb_project,\n", " name=wandb_run,\n", " config=wandb_config,\n", " notes=wandb_notes,\n", " )\n", "else:\n", " wandb_log = False" ] }, { "cell_type": "markdown", "id": "d5690151-2131-4918-b750-e869cbd1a8a8", "metadata": {}, "source": [ "# Main" ] }, { "cell_type": "code", "execution_count": 67, "id": "12de6387-6e18-4e4b-b5ce-a847d625330a", "metadata": {}, "outputs": [], "source": [ "epoch = 0\n", "losses, test_losses, lrs = [], [], []\n", "best_test_loss = 1e9\n", "soft_loss_temps = utils.cosine_anneal(0.004, 0.0075, num_epochs - int(mixup_pct * num_epochs))\n", "\n", "# Optionally resume from checkpoint #\n", "if resume_from_ckpt:\n", " print(\"\\n---resuming from last.pth ckpt---\\n\")\n", " try:\n", " checkpoint = torch.load(outdir+'/last.pth', map_location='cpu')\n", " except:\n", " print('last.pth failed... trying last_backup.pth')\n", " checkpoint = torch.load(outdir+'/last_backup.pth', map_location='cpu')\n", " epoch = checkpoint['epoch']\n", " print(\"Epoch\",epoch)\n", " optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n", " lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n", " model.load_state_dict(checkpoint['model_state_dict'])\n", " del checkpoint\n", "elif wandb_log:\n", " if wandb.run.resumed:\n", " print(\"\\n---resuming from last.pth ckpt---\\n\")\n", " try:\n", " checkpoint = torch.load(outdir+'/last.pth', map_location='cpu')\n", " except:\n", " print('last.pth failed... trying last_backup.pth')\n", " checkpoint = torch.load(outdir+'/last_backup.pth', map_location='cpu')\n", " epoch = checkpoint['epoch']\n", " print(\"Epoch\",epoch)\n", " optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n", " lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n", " model.load_state_dict(checkpoint['model_state_dict'])\n", " del checkpoint\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": 68, "id": "99f09f76-4481-4133-b09a-a22b10dbc0c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[93m [WARNING] \u001b[0m cpu_adam cuda is missing or is incompatible with installed torch, only cpu ops can be compiled!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using /admin/home-ckadirt/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...\n", "Emitting ninja build file /admin/home-ckadirt/.cache/torch_extensions/py310_cu117/cpu_adam/build.ninja...\n", "Building extension module cpu_adam...\n", "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ninja: no work to do.\n", "Time to load cpu_adam op: 3.0333330631256104 seconds\n", "[2023-11-03 13:59:08,078] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed info: version=0.9.5, git-hash=unknown, git-branch=unknown\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Loading extension module cpu_adam...\n" ] }, { "data": { "text/html": [ "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
       "│ in <module>:1                                                                                    │\n",
       "│                                                                                                  │\n",
       "│ ❱ 1 model, optimizer, train_dl, lr_scheduler = accelerator.prepare(                              │\n",
       "│   2 model, optimizer, train_dl, lr_scheduler                                                     │\n",
       "│   3 )                                                                                            │\n",
       "│   4 # leaving out test_dl since we will only have local_rank 0 device do evals                   │\n",
       "│                                                                                                  │\n",
       "│ /admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/accelerate/accelerator. │\n",
       "│ py:1178 in prepare                                                                               │\n",
       "│                                                                                                  │\n",
       "│   1175 │   │   │   elif self.device.type == \"xpu\" and is_xpu_available():                        │\n",
       "│   1176 │   │   │   │   args = self._prepare_ipex(*args)                                          │\n",
       "│   1177 │   │   if self.distributed_type == DistributedType.DEEPSPEED:                            │\n",
       "│ ❱ 1178 │   │   │   result = self._prepare_deepspeed(*args)                                       │\n",
       "│   1179 │   │   elif self.distributed_type == DistributedType.MEGATRON_LM:                        │\n",
       "│   1180 │   │   │   result = self._prepare_megatron_lm(*args)                                     │\n",
       "│   1181 │   │   else:                                                                             │\n",
       "│                                                                                                  │\n",
       "│ /admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/accelerate/accelerator. │\n",
       "│ py:1505 in _prepare_deepspeed                                                                    │\n",
       "│                                                                                                  │\n",
       "│   1502 │   │   │   │   │   │   if type(scheduler).__name__ in deepspeed.runtime.lr_schedules.VA  │\n",
       "│   1503 │   │   │   │   │   │   │   kwargs[\"lr_scheduler\"] = scheduler                            │\n",
       "│   1504 │   │   │                                                                                 │\n",
       "│ ❱ 1505 │   │   │   engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs)           │\n",
       "│   1506 │   │   │   if optimizer is not None:                                                     │\n",
       "│   1507 │   │   │   │   optimizer = DeepSpeedOptimizerWrapper(optimizer)                          │\n",
       "│   1508 │   │   │   if scheduler is not None:                                                     │\n",
       "│                                                                                                  │\n",
       "│ /admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/__init__.py:1 │\n",
       "│ 51 in initialize                                                                                 │\n",
       "│                                                                                                  │\n",
       "│   148 │   assert config != None, \"DeepSpeed requires --deepspeed_config to specify configurati   │\n",
       "│   149 │                                                                                          │\n",
       "│   150 │   if not isinstance(model, PipelineModule):                                              │\n",
       "│ ❱ 151 │   │   config_class = DeepSpeedConfig(config, mpu)                                        │\n",
       "│   152 │   │   if config_class.hybrid_engine.enabled:                                             │\n",
       "│   153 │   │   │   engine = DeepSpeedHybridEngine(args=args,                                      │\n",
       "│   154 │   │   │   │   │   │   │   │   │   │      model=model,                                    │\n",
       "│                                                                                                  │\n",
       "│ /admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/runtime/confi │\n",
       "│ g.py:769 in __init__                                                                             │\n",
       "│                                                                                                  │\n",
       "│    766 │   │                                                                                     │\n",
       "│    767 │   │   # Pass a copy so that user json is unmodified, e.g. for logging                   │\n",
       "│    768 │   │   self._initialize_params(copy.copy(self._param_dict))                              │\n",
       "│ ❱  769 │   │   self._configure_train_batch_size()                                                │\n",
       "│    770 │   │   self._do_sanity_check()                                                           │\n",
       "│    771 │                                                                                         │\n",
       "│    772 │   def _initialize_params(self, param_dict):                                             │\n",
       "│                                                                                                  │\n",
       "│ /admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/runtime/confi │\n",
       "│ g.py:942 in _configure_train_batch_size                                                          │\n",
       "│                                                                                                  │\n",
       "│    939 │                                                                                         │\n",
       "│    940 │   def _configure_train_batch_size(self):                                                │\n",
       "│    941 │   │   self._set_batch_related_parameters()                                              │\n",
       "│ ❱  942 │   │   self._batch_assertion()                                                           │\n",
       "│    943 │                                                                                         │\n",
       "│    944 │   def _do_sanity_check(self):                                                           │\n",
       "│    945 │   │   self._do_error_check()                                                            │\n",
       "│                                                                                                  │\n",
       "│ /admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/runtime/confi │\n",
       "│ g.py:890 in _batch_assertion                                                                     │\n",
       "│                                                                                                  │\n",
       "│    887 │   │                                                                                     │\n",
       "│    888 │   │   assert (grad_acc > 0), f\"Gradient accumulation steps: {grad_acc} has to be great  │\n",
       "│    889 │   │                                                                                     │\n",
       "│ ❱  890 │   │   assert train_batch == micro_batch * grad_acc * self.world_size, (                 │\n",
       "│    891 │   │   │   f\"Check batch related parameters. train_batch_size is not equal \"             │\n",
       "│    892 │   │   │   \"to micro_batch_per_gpu * gradient_acc_step * world_size \"                    │\n",
       "│    893 │   │   │   f\"{train_batch} != {micro_batch} * {grad_acc} * {self.world_size}\")           │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "AssertionError: Check batch related parameters. train_batch_size is not equal to micro_batch_per_gpu * \n",
       "gradient_acc_step * world_size 256 != 32 * 1 * 1\n",
       "
\n" ], "text/plain": [ "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m1\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1 model, optimizer, train_dl, lr_scheduler = accelerator.prepare( \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m2 \u001b[0mmodel, optimizer, train_dl, lr_scheduler \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m3 \u001b[0m) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m4 \u001b[0m\u001b[2m# leaving out test_dl since we will only have local_rank 0 device do evals\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/accelerate/\u001b[0m\u001b[1;33maccelerator.\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[1;33mpy\u001b[0m:\u001b[94m1178\u001b[0m in \u001b[92mprepare\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1175 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94melif\u001b[0m \u001b[96mself\u001b[0m.device.type == \u001b[33m\"\u001b[0m\u001b[33mxpu\u001b[0m\u001b[33m\"\u001b[0m \u001b[95mand\u001b[0m is_xpu_available(): \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1176 \u001b[0m\u001b[2m│ │ │ │ \u001b[0margs = \u001b[96mself\u001b[0m._prepare_ipex(*args) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1177 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.distributed_type == DistributedType.DEEPSPEED: \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1178 \u001b[2m│ │ │ \u001b[0mresult = \u001b[96mself\u001b[0m._prepare_deepspeed(*args) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1179 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melif\u001b[0m \u001b[96mself\u001b[0m.distributed_type == DistributedType.MEGATRON_LM: \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1180 \u001b[0m\u001b[2m│ │ │ \u001b[0mresult = \u001b[96mself\u001b[0m._prepare_megatron_lm(*args) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1181 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/accelerate/\u001b[0m\u001b[1;33maccelerator.\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[1;33mpy\u001b[0m:\u001b[94m1505\u001b[0m in \u001b[92m_prepare_deepspeed\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mtype\u001b[0m(scheduler).\u001b[91m__name__\u001b[0m \u001b[95min\u001b[0m deepspeed.runtime.lr_schedules.VA \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ │ │ │ │ │ \u001b[0mkwargs[\u001b[33m\"\u001b[0m\u001b[33mlr_scheduler\u001b[0m\u001b[33m\"\u001b[0m] = scheduler \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ │ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1505 \u001b[2m│ │ │ \u001b[0mengine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1506 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m optimizer \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1507 \u001b[0m\u001b[2m│ │ │ │ \u001b[0moptimizer = DeepSpeedOptimizerWrapper(optimizer) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m1508 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m scheduler \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/\u001b[0m\u001b[1;33m__init__.py\u001b[0m:\u001b[94m1\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[94m51\u001b[0m in \u001b[92minitialize\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m148 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94massert\u001b[0m config != \u001b[94mNone\u001b[0m, \u001b[33m\"\u001b[0m\u001b[33mDeepSpeed requires --deepspeed_config to specify configurati\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m149 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m150 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m \u001b[96misinstance\u001b[0m(model, PipelineModule): \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m151 \u001b[2m│ │ \u001b[0mconfig_class = DeepSpeedConfig(config, mpu) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m152 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m config_class.hybrid_engine.enabled: \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m153 \u001b[0m\u001b[2m│ │ │ \u001b[0mengine = DeepSpeedHybridEngine(args=args, \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m154 \u001b[0m\u001b[2m│ │ │ │ │ │ │ │ │ │ \u001b[0mmodel=model, \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/runtime/\u001b[0m\u001b[1;33mconfi\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[1;33mg.py\u001b[0m:\u001b[94m769\u001b[0m in \u001b[92m__init__\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 766 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 767 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Pass a copy so that user json is unmodified, e.g. for logging\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 768 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._initialize_params(copy.copy(\u001b[96mself\u001b[0m._param_dict)) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 769 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._configure_train_batch_size() \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 770 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._do_sanity_check() \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 771 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 772 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_initialize_params\u001b[0m(\u001b[96mself\u001b[0m, param_dict): \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/runtime/\u001b[0m\u001b[1;33mconfi\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[1;33mg.py\u001b[0m:\u001b[94m942\u001b[0m in \u001b[92m_configure_train_batch_size\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 939 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 940 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_configure_train_batch_size\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 941 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._set_batch_related_parameters() \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 942 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._batch_assertion() \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 943 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 944 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_do_sanity_check\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 945 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._do_error_check() \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/deepspeed/runtime/\u001b[0m\u001b[1;33mconfi\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[1;33mg.py\u001b[0m:\u001b[94m890\u001b[0m in \u001b[92m_batch_assertion\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 887 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 888 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94massert\u001b[0m (grad_acc > \u001b[94m0\u001b[0m), \u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mGradient accumulation steps: \u001b[0m\u001b[33m{\u001b[0mgrad_acc\u001b[33m}\u001b[0m\u001b[33m has to be great\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 889 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 890 \u001b[2m│ │ \u001b[0m\u001b[94massert\u001b[0m train_batch == micro_batch * grad_acc * \u001b[96mself\u001b[0m.world_size, ( \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 891 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mCheck batch related parameters. train_batch_size is not equal \u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 892 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mto micro_batch_per_gpu * gradient_acc_step * world_size \u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 893 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m{\u001b[0mtrain_batch\u001b[33m}\u001b[0m\u001b[33m != \u001b[0m\u001b[33m{\u001b[0mmicro_batch\u001b[33m}\u001b[0m\u001b[33m * \u001b[0m\u001b[33m{\u001b[0mgrad_acc\u001b[33m}\u001b[0m\u001b[33m * \u001b[0m\u001b[33m{\u001b[0m\u001b[96mself\u001b[0m.world_size\u001b[33m}\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", "\u001b[1;91mAssertionError: \u001b[0mCheck batch related parameters. train_batch_size is not equal to micro_batch_per_gpu * \n", "gradient_acc_step * world_size \u001b[1;36m256\u001b[0m != \u001b[1;36m32\u001b[0m * \u001b[1;36m1\u001b[0m * \u001b[1;36m1\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model, optimizer, train_dl, lr_scheduler = accelerator.prepare(\n", "model, optimizer, train_dl, lr_scheduler\n", ")\n", "# leaving out test_dl since we will only have local_rank 0 device do evals" ] }, { "cell_type": "code", "execution_count": null, "id": "469e6313-425f-45ed-875a-ecd5df343e31", "metadata": {}, "outputs": [], "source": [ "def add_saturation(image, alpha=2):\n", " gray_image = 0.2989 * image[:, 0, :, :] + 0.5870 * image[:, 1, :, :] + 0.1140 * image[:, 2, :, :]\n", " gray_image = gray_image.unsqueeze(1).expand_as(image)\n", " saturated_image = alpha * image + (1 - alpha) * gray_image\n", " return torch.clamp(saturated_image, 0, 1)" ] }, { "cell_type": "code", "execution_count": 65, "id": "60be0d5f-3e94-4612-9373-61b53d836393", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
       "│ in <module>:1                                                                                    │\n",
       "│                                                                                                  │\n",
       "│ ❱   1 print(f\"{model_name} starting with epoch {epoch} / {num_epochs}\")                          │\n",
       "│     2 progress_bar = tqdm(range(epoch,num_epochs), ncols=1200, disable=(local_rank!=0))          │\n",
       "│     3 test_image, test_voxel = None, None                                                        │\n",
       "│     4 mse = nn.MSELoss()                                                                         │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "NameError: name 'epoch' is not defined\n",
       "
\n" ], "text/plain": [ "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m1\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 1 \u001b[96mprint\u001b[0m(\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m{\u001b[0mmodel_name\u001b[33m}\u001b[0m\u001b[33m starting with epoch \u001b[0m\u001b[33m{\u001b[0mepoch\u001b[33m}\u001b[0m\u001b[33m / \u001b[0m\u001b[33m{\u001b[0mnum_epochs\u001b[33m}\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 2 \u001b[0mprogress_bar = tqdm(\u001b[96mrange\u001b[0m(epoch,num_epochs), ncols=\u001b[94m1200\u001b[0m, disable=(local_rank!=\u001b[94m0\u001b[0m)) \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 3 \u001b[0mtest_image, test_voxel = \u001b[94mNone\u001b[0m, \u001b[94mNone\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2m 4 \u001b[0mmse = nn.MSELoss() \u001b[31m│\u001b[0m\n", "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", "\u001b[1;91mNameError: \u001b[0mname \u001b[32m'epoch'\u001b[0m is not defined\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "print(f\"{model_name} starting with epoch {epoch} / {num_epochs}\")\n", "progress_bar = tqdm(range(epoch,num_epochs), ncols=1200, disable=(local_rank!=0))\n", "test_image, test_voxel = None, None\n", "mse = nn.MSELoss()\n", "l1 = nn.L1Loss()\n", "\n", "for epoch in progress_bar:\n", " model.train()\n", " \n", " fwd_percent_correct = 0.\n", " bwd_percent_correct = 0.\n", " test_fwd_percent_correct = 0.\n", " test_bwd_percent_correct = 0.\n", "\n", " loss_clip_total = 0.\n", " loss_blurry_total = 0.\n", " loss_depth_total = 0.\n", " test_loss_clip_total = 0.\n", " test_loss_blurry_total = 0.\n", " test_loss_depth_total = 0.\n", "\n", " blurry_pixcorr = 0.\n", " test_blurry_pixcorr = 0. # needs >.456 to beat low-level subj01 results in mindeye v1\n", " \n", " for train_i, (behav, past_behav, future_behav, old_behav) in enumerate(train_dl):\n", " with torch.cuda.amp.autocast(dtype=data_type):\n", " optimizer.zero_grad()\n", " \n", " #voxel = voxels[behav[:,0,5].cpu().long()].to(device)\n", " #image = images[behav[:,0,0].cpu().long()].to(device).float()\n", " \n", " #past_15_voxels = voxels[past_behav[:,:,5].cpu().long()].to(device) # batch_size, 15, 15279\n", " #past_15_times = torch.Tensor([i for i in range(seq_len - 1)]).to(device) # 15\n", " \n", " voxel = voxels[behav[:,0,5].cpu().long()].to(device)\n", " image = images[behav[:,0,0].cpu().long()].to(device).float()\n", "\n", " past_15_voxels = voxels[past_behav[:,:seq_len-1,5].cpu().long()].to(device) # batch_size, 15, 15279\n", " past_15_times = torch.Tensor([i for i in range(seq_len-1)]).to(device) # 15\n", " #for past in range(1):\n", " # past_voxel = voxels[past_behav[:,past,5].cpu().long()].to(device)\n", " \n", " if blurry_recon:\n", " # blurry_image_enc = autoenc.encode(2*utils.resize(image,128)-1).latent_dist.mode() * 0.18215\n", " blurry_image_enc = autoenc.encode(2*utils.resize(add_saturation(image),128)-1).latents * 0.18215\n", "\n", " if depth_recon:\n", " # depth_images = utils.resize(midas_depth.model(image).unsqueeze(1).repeat(1,3,1,1), 128)\n", " depth_images = utils.resize(midas_depth.model(image).unsqueeze(1), 32)\n", " depth_images = (depth_images / depth_images.view(depth_images.shape[0], -1).max(dim=1)[0].view(-1, 1, 1, 1).expand_as(depth_images)).half()\n", " depth_image_enc = depth_images # autoenc.encode(2*depth_images-1).latents * 0.18215\n", " \n", " if use_image_aug: \n", " image = img_augment(image)\n", " \n", " clip_target = clip_model.embed_image(image)\n", " assert not torch.any(torch.isnan(clip_target))\n", " \n", " if epoch < int(mixup_pct * num_epochs):\n", " voxel, perm, betas, select = utils.mixco(voxel)\n", " past_voxel, _, _, _ = utils.mixco(voxel, perm=perm, betas=betas, select=select)\n", " \n", " for p in range(seq_len-1):\n", " #print(past_behav.shape) #128, 15, 17\n", " #print(past_behav[:,p,-1])\n", " #print(past_15_voxels.shape) # 128, 1, 15724\n", " mask = past_behav[:,p,-1] == torch.ones_like(past_behav[:,p,-1])\n", " #print(mask) # 128\n", " past_15_voxels[mask, p, :] = torch.zeros_like(past_15_voxels[0, p, :])\n", " #print(past_15_voxels)\n", " \n", " past_15_voxels = past_15_voxels.reshape(-1, past_15_voxels.shape[-1])\n", " past_15_times = past_15_times.repeat(voxel.shape[0], 1)\n", " past_15_times = past_15_times.reshape(-1)\n", " time_embeddings = model.time_embedding(past_15_times)\n", " past_info_full = torch.cat((past_15_voxels, time_embeddings), dim=-1)\n", " \n", " positional_current_voxel = torch.zeros((voxel.shape[0], time_embeddings.shape[-1])).to(voxel.device)\n", " voxel = torch.cat((voxel, positional_current_voxel), dim=-1)\n", " voxel_ridge = model.ridge(torch.cat((voxel, past_info_full), dim=-2))\n", " voxel_ridge = voxel_ridge.view(int(voxel_ridge.shape[0]/seq_len), seq_len, hidden_dim)\n", " #unsqueeze(1) # bz * 2, 1, 4096\n", " \n", " # past_voxel_ridge = model.ridge(past_voxel)\n", " # voxel_ridge = torch.cat((voxel_ridge.unsqueeze(1), past_voxel_ridge.unsqueeze(1)), axis=1)\n", " print(voxel_ridge.shape)\n", " \n", " clip_voxels, blurry_image_enc_, depth_image_enc_ = model.backbone(voxel_ridge)\n", " \n", " clip_voxels_norm = nn.functional.normalize(clip_voxels.flatten(1), dim=-1)\n", " clip_target_norm = nn.functional.normalize(clip_target.flatten(1), dim=-1)\n", " \n", " if epoch < int(mixup_pct * num_epochs): \n", " loss_clip = utils.mixco_nce(\n", " clip_voxels_norm,\n", " clip_target_norm,\n", " temp=.006, \n", " perm=perm, betas=betas, select=select)\n", " else:\n", " epoch_temp = soft_loss_temps[epoch-int(mixup_pct*num_epochs)]\n", " loss_clip = utils.soft_clip_loss(\n", " clip_voxels_norm,\n", " clip_target_norm,\n", " temp=epoch_temp)\n", "\n", " loss_clip_total += loss_clip.item()\n", " loss_clip *= clip_scale\n", " loss = loss_clip\n", " \n", " if blurry_recon:\n", " downsampled_image = nn.functional.interpolate(image, size=(8, 8), mode='bilinear', align_corners=False)\n", " re_upsampled_image = add_saturation(nn.functional.interpolate(downsampled_image, size=(128, 128), mode='nearest'))\n", " re_upsampled_enc = autoenc.encode(2*re_upsampled_image-1).latents * 0.18215\n", " \n", " loss_blurry = (l1(blurry_image_enc_, blurry_image_enc) + l1(blurry_image_enc_, re_upsampled_enc))\n", " loss_blurry += l1(torch.var(blurry_image_enc), torch.var(blurry_image_enc_))\n", " loss_blurry_total += loss_blurry.item()\n", " loss_blurry *= blur_scale\n", " loss += loss_blurry\n", "\n", " if depth_recon:\n", " loss_depth = l1(depth_image_enc_, depth_image_enc)\n", " # loss_depth += l1(torch.var(depth_image_enc_), torch.var(depth_image_enc))\n", " loss_depth_total += loss_depth.item()\n", " loss_depth *= depth_scale\n", " loss += loss_depth\n", " \n", " # forward and backward top 1 accuracy \n", " labels = torch.arange(len(clip_target_norm)).to(clip_voxels_norm.device) \n", " fwd_percent_correct += utils.topk(torch.abs(utils.batchwise_cosine_similarity(clip_voxels_norm, clip_target_norm)), labels, k=1).item()\n", " bwd_percent_correct += utils.topk(torch.abs(utils.batchwise_cosine_similarity(clip_target_norm, clip_voxels_norm)), labels, k=1).item()\n", " \n", " if blurry_recon:\n", " with torch.no_grad():\n", " # only doing pixcorr eval on a subset of the samples per batch because its costly & slow to compute autoenc.decode()\n", " random_samps = np.random.choice(np.arange(len(voxel)), size=batch_size//5, replace=False)\n", " # random_samps = np.arange(batch_size//5)\n", " blurry_recon_images = (autoenc.decode(blurry_image_enc_[random_samps]/0.18215).sample/ 2 + 0.5).clamp(0,1)\n", " # pixcorr_origsize_nanmean is computationally less intense than utils.pixcorr and uses nanmean instead of mean\n", " pixcorr = utils.pixcorr_origsize_nanmean(image[random_samps], blurry_recon_images)\n", " # pixcorr = utils.pixcorr(image[random_samps], blurry_recon_images)\n", " # loss += (1 - pixcorr)\n", " blurry_pixcorr += pixcorr.item()\n", " # utils.check_loss(pixcorr)\n", "\n", " utils.check_loss(loss)\n", " accelerator.backward(loss)\n", " optimizer.step()\n", " \n", " losses.append(loss.item())\n", " lrs.append(optimizer.param_groups[0]['lr'])\n", " \n", " if lr_scheduler_type is not None:\n", " lr_scheduler.step()\n", "\n", " model.eval()\n", " if local_rank==0:\n", " with torch.no_grad(), torch.cuda.amp.autocast(dtype=data_type): \n", " for test_i, (behav, past_behav, future_behav, old_behav) in enumerate(test_dl): \n", " # all test samples should be loaded per batch such that test_i should never exceed 0\n", " assert len(behav) == num_test\n", " \n", " ## Average same-image repeats ##\n", " if test_image is None:\n", " voxel = voxels[behav[:,0,5].cpu().long()]\n", " image = behav[:,0,0].cpu().long()\n", " \n", " unique_image, sort_indices = torch.unique(image, return_inverse=True)\n", " for im in unique_image:\n", " locs = torch.where(im == image)[0]\n", " if test_image is None:\n", " test_image = images[im][None]\n", " test_voxel = torch.mean(voxel[locs],axis=0)[None]\n", " else:\n", " test_image = torch.vstack((test_image, images[im][None]))\n", " test_voxel = torch.vstack((test_voxel, torch.mean(voxel[locs],axis=0)[None]))\n", " \n", " # random sample of 300\n", " random_indices = torch.arange(len(test_voxel))[:300]\n", " voxel = test_voxel[random_indices].to(device)\n", " image = test_image[random_indices].to(device)\n", " assert len(image) == 300\n", " \n", " current_past_behav = past_behav[random_indices]\n", "\n", " past_15_voxels = voxels[current_past_behav[:,:seq_len-1,5].cpu().long()].to(device) # batch_size, 15, 15279\n", " past_15_times = torch.Tensor([i for i in range(seq_len-1)]).to(device) # 15\n", "\n", " if blurry_recon:\n", " # blurry_image_enc = autoenc.encode(2*utils.resize(image,128)-1).latent_dist.mode() * 0.18215\n", " blurry_image_enc = autoenc.encode(2*utils.resize(add_saturation(image),128)-1).latents * 0.18215\n", "\n", " if depth_recon:\n", " # depth_images = utils.resize(midas_depth.model(image).unsqueeze(1).repeat(1,3,1,1), 128)\n", " depth_images = utils.resize(midas_depth.model(image).unsqueeze(1), 32)\n", " depth_images = (depth_images / depth_images.view(depth_images.shape[0], -1).max(dim=1)[0].view(-1, 1, 1, 1).expand_as(depth_images)).half()\n", " depth_image_enc = depth_images # autoenc.encode(2*depth_images-1).latents * 0.18215\n", " \n", " clip_target = clip_model.embed_image(image.float())\n", " \n", "\n", " past_15_voxels = past_15_voxels.reshape(-1, past_15_voxels.shape[-1])\n", " past_15_times = past_15_times.repeat(voxel.shape[0], 1)\n", " past_15_times = past_15_times.reshape(-1)\n", " time_embeddings = model.time_embedding(past_15_times)\n", " past_info_full = torch.cat((past_15_voxels, time_embeddings), dim=-1)\n", "\n", " positional_current_voxel = torch.zeros((voxel.shape[0], time_embeddings.shape[-1])).to(voxel.device)\n", " voxel = torch.cat((voxel, positional_current_voxel), dim=-1)\n", " voxel_ridge = model.ridge(torch.cat((voxel, past_info_full), dim=-2)).unsqueeze(1)\n", " \n", " #voxel_ridge = model.ridge(voxel).unsqueeze(1)\n", "\n", " # voxel_ridge = torch.cat((voxel_ridge.unsqueeze(1),voxel_ridge.unsqueeze(1)),axis=1)\n", " \n", " clip_voxels, blurry_image_enc_, depth_image_enc_ = model.backbone(voxel_ridge)\n", " \n", " clip_voxels_norm = nn.functional.normalize(clip_voxels.flatten(1), dim=-1)\n", " clip_target_norm = nn.functional.normalize(clip_target.flatten(1), dim=-1)\n", " \n", " loss_clip = utils.soft_clip_loss(\n", " clip_voxels_norm,\n", " clip_target_norm,\n", " temp=.006)\n", " test_loss_clip_total += loss_clip.item()\n", " loss_clip = loss_clip * clip_scale\n", " loss = loss_clip\n", "\n", " if blurry_recon:\n", " downsampled_image = nn.functional.interpolate(image, size=(8, 8), mode='bilinear', align_corners=False)\n", " re_upsampled_image = add_saturation(nn.functional.interpolate(downsampled_image, size=(128, 128), mode='nearest'))\n", " re_upsampled_enc = autoenc.encode(2*re_upsampled_image-1).latents * 0.18215\n", " \n", " loss_blurry = (l1(blurry_image_enc_, blurry_image_enc) + l1(blurry_image_enc_, re_upsampled_enc))\n", " loss_blurry += l1(torch.var(blurry_image_enc), torch.var(blurry_image_enc_))\n", " test_loss_blurry_total += loss_blurry.item()\n", " loss_blurry *= blur_scale\n", " loss += loss_blurry\n", " \n", " # halving the batch size because the decoder is computationally heavy\n", " blurry_recon_images = (autoenc.decode(blurry_image_enc_[:len(voxel)//2]/0.18215).sample / 2 + 0.5).clamp(0,1)\n", " blurry_recon_images = torch.vstack((blurry_recon_images, (autoenc.decode(blurry_image_enc_[len(voxel)//2:]/0.18215).sample / 2 + 0.5).clamp(0,1)))\n", " pixcorr = utils.pixcorr(image, blurry_recon_images)\n", " loss += (1 - pixcorr)\n", " test_blurry_pixcorr += pixcorr.item()\n", "\n", " if depth_recon:\n", " loss_depth = l1(depth_image_enc_, depth_image_enc)\n", " # loss_depth += l1(torch.var(depth_image_enc_), torch.var(depth_image_enc))\n", " test_loss_depth_total += loss_depth.item()\n", " loss_depth *= depth_scale\n", " loss += loss_depth\n", " \n", " # forward and backward top 1 accuracy \n", " labels = torch.arange(len(clip_target_norm)).to(clip_voxels_norm.device) \n", " test_fwd_percent_correct += utils.topk(utils.batchwise_cosine_similarity(clip_voxels_norm, clip_target_norm), labels, k=1).item()\n", " test_bwd_percent_correct += utils.topk(utils.batchwise_cosine_similarity(clip_target_norm, clip_voxels_norm), labels, k=1).item()\n", "\n", " utils.check_loss(loss) \n", " test_losses.append(loss.item())\n", "\n", " # if utils.is_interactive(): clear_output(wait=True)\n", " print(\"---\")\n", " \n", " assert (test_i+1) == 1\n", " logs = {\"train/loss\": np.mean(losses[-(train_i+1):]),\n", " \"test/loss\": np.mean(test_losses[-(test_i+1):]),\n", " \"train/lr\": lrs[-1],\n", " \"train/num_steps\": len(losses),\n", " \"test/num_steps\": len(test_losses),\n", " \"train/fwd_pct_correct\": fwd_percent_correct / (train_i + 1),\n", " \"train/bwd_pct_correct\": bwd_percent_correct / (train_i + 1),\n", " \"test/test_fwd_pct_correct\": test_fwd_percent_correct / (test_i + 1),\n", " \"test/test_bwd_pct_correct\": test_bwd_percent_correct / (test_i + 1),\n", " \"train/loss_clip_total\": loss_clip_total / (train_i + 1),\n", " \"train/loss_blurry_total\": loss_blurry_total / (train_i + 1),\n", " \"test/loss_clip_total\": test_loss_clip_total / (test_i + 1),\n", " \"test/loss_blurry_total\": test_loss_blurry_total / (test_i + 1),\n", " \"train/blurry_pixcorr\": blurry_pixcorr / (train_i + 1),\n", " \"test/blurry_pixcorr\": test_blurry_pixcorr / (test_i + 1),\n", " \"train/loss_depth_total\": loss_depth_total / (train_i + 1),\n", " \"test/loss_depth_total\": test_loss_depth_total / (test_i + 1),\n", " }\n", " \n", " if blurry_recon: \n", " # transform blurry recon latents to images and plot it\n", " fig, axes = plt.subplots(1, 8, figsize=(10, 4))\n", " jj=-1\n", " for j in [0,1,2,3]:\n", " jj+=1\n", " axes[jj].imshow(utils.torch_to_Image((autoenc.decode(blurry_image_enc[[j]]/0.18215).sample / 2 + 0.5).clamp(0,1)))\n", " axes[jj].axis('off')\n", " jj+=1\n", " axes[jj].imshow(utils.torch_to_Image((autoenc.decode(blurry_image_enc_[[j]]/0.18215).sample / 2 + 0.5).clamp(0,1)))\n", " axes[jj].axis('off')\n", " \n", " if wandb_log:\n", " logs[f\"test/recons\"] = wandb.Image(fig, caption=f\"epoch{epoch:03d}\")\n", " plt.close()\n", " else:\n", " plt.show()\n", "\n", " if depth_recon:\n", " # transform blurry recon latents to images and plot it\n", " fig, axes = plt.subplots(1, 8, figsize=(10, 4))\n", " # axes[0].imshow(utils.torch_to_Image((autoenc.decode(depth_image_enc[[0]]/0.18215).sample / 2 + 0.5).clamp(0,1)))\n", " # axes[1].imshow(utils.torch_to_Image((autoenc.decode(depth_image_enc_[[0]]/0.18215).sample / 2 + 0.5).clamp(0,1)))\n", " jj=-1\n", " for j in [0,1,2,3]:\n", " jj+=1\n", " axes[jj].imshow(utils.torch_to_Image(utils.resize(depth_image_enc[[j]].view(1,1,32,32).clamp(0,1), 224)))\n", " axes[jj].axis('off')\n", " jj+=1\n", " axes[jj].imshow(utils.torch_to_Image(utils.resize(depth_image_enc_[[j]].view(1,1,32,32).clamp(0,1), 224)))\n", " axes[jj].axis('off')\n", " if wandb_log:\n", " logs[f\"test/depth_recons\"] = wandb.Image(fig, caption=f\"epoch{epoch:03d}\")\n", " plt.close()\n", " else:\n", " plt.show()\n", " \n", " progress_bar.set_postfix(**logs)\n", " \n", " # Save model checkpoint and reconstruct\n", " if epoch % ckpt_interval == 0:\n", " if not utils.is_interactive():\n", " save_ckpt(f'last')\n", " \n", " if wandb_log: wandb.log(logs)\n", "\n", " # wait for other GPUs to catch up if needed\n", " accelerator.wait_for_everyone()\n", " torch.cuda.empty_cache()\n", " gc.collect()\n", "\n", "print(\"\\n===Finished!===\\n\")\n", "if ckpt_saving:\n", " save_ckpt(f'last')\n", "if not utils.is_interactive():\n", " sys.exit(0)" ] }, { "cell_type": "code", "execution_count": null, "id": "93e87fde-815d-4452-9915-f5f5dacf7c2a", "metadata": {}, "outputs": [], "source": [ "plt.plot(losses)\n", "plt.show()\n", "plt.plot(test_losses)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f2690877-a431-44e8-a2ca-61f4b7397070", "metadata": {}, "source": [ "# Retrieve nearest neighbor in the training set using test set data" ] }, { "cell_type": "code", "execution_count": null, "id": "5b6b8feb-391d-437e-a5d9-a2088f1b1149", "metadata": {}, "outputs": [], "source": [ "annots = np.load(\"/fsx/proj-fmri/shared/mindeyev2_dataset/COCO_73k_annots_curated.npy\")" ] }, { "cell_type": "code", "execution_count": null, "id": "612ac5aa-6f0f-45ad-809e-03df905d184c", "metadata": {}, "outputs": [], "source": [ "ii=2\n", "all_indices = np.unique(train_73k_images) #np.hstack((test_vox_indices[ii],train_vox_indices))\n", "with torch.no_grad(), torch.cuda.amp.autocast():\n", " for batch in tqdm(range(0,len(all_indices),512)):\n", " if batch==0:\n", " clip_target = clip_model.embed_image(images[all_indices[batch:batch+512]]).cpu()\n", " else:\n", " target = clip_model.embed_image(images[all_indices[batch:batch+512]]).cpu()\n", " clip_target = torch.vstack((clip_target,target))\n", " clip_target_norm = nn.functional.normalize(clip_target.flatten(1), dim=-1)\n", "\n", " voxel = test_voxel[[ii]].to(device)\n", " image = test_image[[ii]].to(device)\n", "\n", " print(\"Original Image (test set)\")\n", " display(utils.torch_to_Image(image))\n", " \n", " clip_target = clip_model.embed_image(image).cpu()\n", " # clip_target_norm = torch.vstack((clip_target_norm, nn.functional.normalize(clip_target.flatten(1), dim=-1)))\n", " \n", " voxel_ridge = model.ridge(voxel).unsqueeze(1)\n", " clip_voxels, _, _ = model.backbone(voxel_ridge) \n", " clip_voxels_norm = nn.functional.normalize(clip_voxels.flatten(1), dim=-1)\n", " clip_voxels_norm = nn.functional.normalize(clip_target.flatten(1), dim=-1)\n", "\n", " print(\"clip_voxels_norm\", clip_voxels_norm.shape)\n", " print(\"clip_target_norm\", clip_target_norm.shape)\n", " \n", " sortt = torch.argsort(utils.batchwise_cosine_similarity(clip_voxels_norm.cpu(), \n", " clip_target_norm).flatten()).flip(0)\n", " picks = all_indices[sortt[:5]]\n", "\n", " print(\"\\nNearest neighbors in training set\")\n", " for ip,p in enumerate(picks):\n", " display(utils.torch_to_Image(images[[p]]))\n", " # print(utils.select_annotations([annots[int(p)]]))\n", " if ip==0: predicted_caption = utils.select_annotations([annots[int(p)]])[0]\n", "\n", "print(\"\\n=====\\npredicted_caption:\\n\", predicted_caption)" ] }, { "cell_type": "markdown", "id": "1473ddaa-5f2b-4448-9194-c7b0801d05db", "metadata": {}, "source": [ "# Feed into Stable Diffusion XL for reconstructions" ] }, { "cell_type": "code", "execution_count": null, "id": "70e50e0d-c44f-4d56-939a-2943535e1747", "metadata": {}, "outputs": [], "source": [ "from diffusers import StableDiffusionXLPipeline\n", "pipe = StableDiffusionXLPipeline.from_pretrained(\n", " \"/fsx/proj-fmri/shared/cache/models--stabilityai--stable-diffusion-xl-base-1.0/snapshots/f898a3e026e802f68796b95e9702464bac78d76f\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n", ")\n", "pipe.to(\"cuda\")\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "id": "479e6994-3eaa-47d2-89a3-422c464fab36", "metadata": {}, "outputs": [], "source": [ "prompt = predicted_caption\n", "recon = pipe(prompt=prompt).images[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "9dc48e1b-5842-4a29-963a-6469d943a72c", "metadata": { "tags": [] }, "outputs": [], "source": [ "print(\"Seen image\")\n", "display(utils.torch_to_Image(image))\n", "\n", "print(\"Reconstruction\")\n", "utils.torch_to_Image(utils.resize(transforms.ToTensor()(recon),224))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "165px" }, "toc_section_display": true, "toc_window_display": true }, "toc-autonumbering": true, "vscode": { "interpreter": { "hash": "62aae01ef0cf7b6af841ab1c8ce59175c4332e693ab3d00bc32ceffb78a35376" } } }, "nbformat": 4, "nbformat_minor": 5 }