{ "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.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": "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2023-11-19 16:32:39,711] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\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 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", "from torchvision.transforms import ToPILImage #CHANGED (added)\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\n", "\n", "global_batch_size = 128 #128" ] }, { "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" ] } ], "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", "accelerator = Accelerator(split_batches=False)\n", "\n", "### UNCOMMENT BELOW STUFF TO USE DEEPSPEED (also comment out the immediately above \"accelerator = \" line) ###\n", "\n", "# if num_devices <= 1 and utils.is_interactive():\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", "# global_batch_size = os.environ[\"GLOBAL_BATCH_SIZE\"]\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'] = int(os.environ[\"GLOBAL_BATCH_SIZE\"]) // num_devices\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.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 = 2370606\n", "device: cuda\n", "Distributed environment: NO\n", "Num processes: 1\n", "Process index: 0\n", "Local process index: 0\n", "Device: cuda\n", "\n", "Mixed precision type: no\n", "\n", "distributed = False num_devices = 1 local rank = 0 world size = 1\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", "print(\"distributed =\",distributed, \"num_devices =\", num_devices, \"local rank =\", local_rank, \"world size =\", world_size)\n", "print = accelerator.print # only print if local_rank=0" ] }, { "cell_type": "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": [ "['--data_path=/fsx/proj-fmri/shared/mindeyev2_dataset', '--model_name=captions', '--subj=1', '--batch_size=128', '--n_samples_save=0', '--max_lr=3e-1', '--mixup_pct=.66', '--num_epochs=30', '--ckpt_interval=999', '--no-use_image_aug']\n" ] } ], "source": [ "# if running this interactively, can specify jupyter_args here for argparser to use\n", "if utils.is_interactive():\n", " # Example use\n", " jupyter_args = f\"--data_path=/fsx/proj-fmri/shared/mindeyev2_dataset \\\n", " --model_name=captions \\\n", " --subj=1 --batch_size={global_batch_size} --n_samples_save=0 \\\n", " --max_lr=3e-1 --mixup_pct=.66 --num_epochs=30 --ckpt_interval=999 --no-use_image_aug\"\n", " #max_lr=3e-5 originally\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": [ { "name": "stdout", "output_type": "stream", "text": [ "global batch_size 128\n", "batch_size 128\n" ] } ], "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", " \"--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=240,\n", " help=\"number of epochs of training\",\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", "parser.add_argument(\n", " \"--n_samples_save\",type=int,default=0,choices=[0,1],\n", " help=\"Number of reconstructions for monitoring progress, 0 will speed up training\",\n", ")\n", "\n", "if utils.is_interactive():\n", " args = parser.parse_args(jupyter_args)\n", "else:\n", " args = parser.parse_args()\n", "\n", "# create global variables without the args prefix\n", "for attribute_name in vars(args).keys():\n", " globals()[attribute_name] = getattr(args, attribute_name)\n", "\n", "print(\"global batch_size\", batch_size)\n", "batch_size = int(batch_size / num_devices)\n", "print(\"batch_size\", batch_size)" ] }, { "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):\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": "code", "execution_count": 8, "id": "e7807ba9-02b6-4bc0-873c-69869abe4091", "metadata": {}, "outputs": [], "source": [ "wandb_log = False" ] }, { "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": 9, "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", "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=False, 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=False, pin_memory=True)" ] }, { "cell_type": "markdown", "id": "203b060a-2dd2-4c35-929b-c576be82eb52", "metadata": {}, "source": [ "### check dataloaders are working" ] }, { "cell_type": "code", "execution_count": 10, "id": "e7a9c68c-c3c9-4080-bd99-067c4486dc37", "metadata": { "tags": [] }, "outputs": [], "source": [ "# test_indices = []\n", "# test_images = []\n", "# for test_i, (behav, past_behav, future_behav, old_behav) in enumerate(test_dl):\n", "# test_indices = np.append(test_indices, behav[:,0,5].numpy())\n", "# test_images = np.append(test_images, behav[:,0,0].numpy())\n", "# test_indices = test_indices.astype(np.int16)\n", "# print(test_i, (test_i+1) * test_batch_size, len(test_indices))\n", "# print(\"---\\n\")\n", "\n", "# train_indices = []\n", "# train_images = []\n", "# for train_i, (behav, past_behav, future_behav, old_behav) in enumerate(train_dl):\n", "# train_indices = np.append(train_indices, behav[:,0,5].long().numpy())\n", "# train_images = np.append(train_images, behav[:,0,0].numpy())\n", "# train_indices = train_indices.astype(np.int16)\n", "# print(train_i, (train_i+1) * batch_size, len(train_indices))\n", "\n", "# # train_images = np.hstack((train_images, test_images))\n", "# # print(\"WARNING: ADDED TEST IMAGES TO TRAIN IMAGES\")" ] }, { "cell_type": "markdown", "id": "45fad12c-f9fb-4408-8fd4-9bca324ad634", "metadata": {}, "source": [ "## Load data and images" ] }, { "cell_type": "code", "execution_count": 11, "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, 15729])\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", "voxels = f['betas'][:]\n", "print(f\"subj0{subj} betas loaded into memory\")\n", "voxels = torch.Tensor(voxels).to(\"cpu\").half()\n", "if subj==1:\n", " voxels = torch.hstack((voxels, torch.zeros((len(voxels), 5))))\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\").half()\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": 12, "id": "795e2885-bd07-4e27-bed7-181473c06df9", "metadata": { "tags": [] }, "outputs": [], "source": [ "import transformers\n", "from transformers import Blip2Processor, Blip2ForConditionalGeneration\n", "\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 13, "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)" ] }, { "cell_type": "code", "execution_count": 14, "id": "23428fb7-2955-4295-bea1-447cebf9f72e", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:08<00:00, 34.47s/it]\n" ] }, { "data": { "text/plain": [ "'from lavis.models import load_model_and_preprocess\\nfrom lavis.models import model_zoo\\nblip2_model, vis_processors, _ = load_model_and_preprocess(\\n name=\"blip2_t5\", model_type=\"pretrain_flant5xl_vitL\", is_eval=True, device=device)\\n\\nclip_seq_dim = 257\\nclip_emb_dim = 1024\\nhidden_dim = 4096'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cache_blip2 = \"/fsx/proj-fmri/shared/cache/models--Salesforce--blip2-opt-2.7b/snapshots/6e723d92ee91ebcee4ba74d7017632f11ff4217b\"\n", "\n", "b2_processor = Blip2Processor.from_pretrained(cache_blip2)\n", "b2_model = Blip2ForConditionalGeneration.from_pretrained(cache_blip2, torch_dtype=torch.float16, device_map=\"auto\")\n", "\n", "#Load in blip2 as well\n", "\"\"\"from lavis.models import load_model_and_preprocess\n", "from lavis.models import model_zoo\n", "blip2_model, vis_processors, _ = load_model_and_preprocess(\n", " name=\"blip2_t5\", model_type=\"pretrain_flant5xl_vitL\", is_eval=True, device=device)\n", "\n", "clip_seq_dim = 257\n", "clip_emb_dim = 1024\n", "hidden_dim = 4096\"\"\"" ] }, { "cell_type": "code", "execution_count": 15, "id": "b06f3de2-a8da-4ba0-94f0-99096f738d55", "metadata": { "tags": [] }, "outputs": [], "source": [ "def embed_images_b2(images):\n", " images = (images * 255).type(torch.uint8)\n", " with torch.no_grad():\n", " inputs_processed = b2_processor(images, return_tensors=\"pt\").to(\"cuda\", torch.float16)\n", " enc_imgs = b2_model.vision_model.forward(inputs_processed['pixel_values'])\n", " return enc_imgs.last_hidden_state.detach(), inputs_processed\n", "\n", "def embeds_to_captions_b2(embeds):\n", " with torch.no_grad():\n", " input_ids = None #inputs['input_ids']\n", " attention_mask = None\n", " batch_size = embeds.shape[0]\n", " image_embeds = embeds\n", " image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)\n", "\n", " query_tokens = b2_model.query_tokens.expand(image_embeds.shape[0], -1, -1)\n", " query_outputs = b2_model.qformer(\n", " query_embeds=query_tokens,\n", " encoder_hidden_states=image_embeds,\n", " encoder_attention_mask=image_attention_mask,\n", " return_dict=True,\n", " )\n", " query_output = query_outputs.last_hidden_state\n", "\n", " language_model_inputs = b2_model.language_projection(query_output)\n", " language_attention_mask = torch.ones(\n", " language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device\n", " )\n", " if input_ids is None:\n", " input_ids = (\n", " torch.LongTensor([[b2_model.config.text_config.bos_token_id]])\n", " .repeat(batch_size, 1)\n", " .to(image_embeds.device)\n", " )\n", " if attention_mask is None:\n", " attention_mask = torch.ones_like(input_ids)\n", " attention_mask = torch.cat([language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1)\n", "\n", " # concatenate query embeddings with prompt embeddings\n", " inputs_embeds = b2_model.get_input_embeddings()(input_ids)\n", " inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)\n", "\n", " outputs = b2_model.language_model.generate(\n", " inputs_embeds=inputs_embeds,\n", " attention_mask=attention_mask,\n", " )\n", " text = b2_processor.batch_decode(outputs, skip_special_tokens=True)\n", " \n", " return outputs, text\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "ec0a34d3-76e0-4a47-a9ab-6131ab2ccecd", "metadata": { "tags": [] }, "outputs": [], "source": [ "image_test = images[1:20].permute(0,2,3,1)\n", "#raw_image = Image.open('/fsx/proj-fmri/shared/controlNetData/target/img_t1.jpg').convert('RGB')\n", "# Convert the image to a NumPy array\n", "#image_test = np.array(raw_image)\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "e04876a4-45c7-4015-8255-8574c8f50f14", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "\"import matplotlib.pyplot as plt\\n# Plotting one of the images (taking the first image as an example)\\nimg_to_plot = inputs_rec['pixel_values'][-1]\\n\\n# Transpose the image for correct display (PyTorch: [C, H, W], Matplotlib: [H, W, C])\\nimg_to_plot = img_to_plot.permute(1, 2, 0).to(torch.float32).to('cpu')\\nprint(img_to_plot.shape)\\n\\nplt.imshow(img_to_plot)\\nplt.show()\"" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"import matplotlib.pyplot as plt\n", "# Plotting one of the images (taking the first image as an example)\n", "img_to_plot = inputs_rec['pixel_values'][-1]\n", "\n", "# Transpose the image for correct display (PyTorch: [C, H, W], Matplotlib: [H, W, C])\n", "img_to_plot = img_to_plot.permute(1, 2, 0).to(torch.float32).to('cpu')\n", "print(img_to_plot.shape)\n", "\n", "plt.imshow(img_to_plot)\n", "plt.show()\"\"\"" ] }, { "cell_type": "code", "execution_count": 18, "id": "328a17d0-593b-4d1e-812a-10a3b6efea6a", "metadata": { "tags": [] }, "outputs": [], "source": [ "embeds_test, inputs_rec = embed_images_b2(image_test)" ] }, { "cell_type": "code", "execution_count": 19, "id": "abe5f8a8-fca9-4083-8596-a913bdb57de7", "metadata": { "tags": [] }, "outputs": [], "source": [ "#inputs_rec['pixel_values'].shape" ] }, { "cell_type": "code", "execution_count": 20, "id": "c5f3ca7e-b880-421e-b354-7b6c3df565e9", "metadata": { "tags": [] }, "outputs": [], "source": [ "#out = b2_model.generate(**inputs_rec)\n", "#print(b2_processor.decode(out[0], skip_special_tokens=True).strip())" ] }, { "cell_type": "code", "execution_count": 21, "id": "fb462016-78d7-46ea-8058-0d608f17ea65", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/admin/home-ckadirt/miniconda3/envs/mindeye/lib/python3.10/site-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control thegeneration length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n" ] } ], "source": [ "outputs_test, text_test = embeds_to_captions_b2(embeds_test)" ] }, { "cell_type": "code", "execution_count": 22, "id": "6a95fcdf-db87-4c02-9728-09f85605fb1c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['a cat sitting on a toilet seat\\n',\n", " 'a person cutting a pizza on a cutting board\\n',\n", " 'a sandwich and a drink on a table\\n',\n", " 'a man crossing the street in front of a truck\\n',\n", " 'a giraffe standing in front of trees\\n',\n", " 'three men standing together\\n',\n", " 'a bird standing on a rock next to a body of water\\n',\n", " 'two men sitting on a street corner in asia\\n',\n", " 'a woman and two children playing tennis on a court\\n',\n", " 'a tall brick building with a clock on the side\\n',\n", " 'a train is on the tracks\\n',\n", " 'a man and woman in the water with a surfboard\\n',\n", " 'a living room with a desk and a chair\\n',\n", " 'a group of men on a basketball court\\n',\n", " 'a man holding an umbrella\\n',\n", " 'a man in a red shirt\\n',\n", " 'a group of people holding cell phones and wine glasses\\n',\n", " 'a laptop computer sitting on a table in front of a television\\n',\n", " 'a baseball player is swinging a bat on a field\\n']" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text_test" ] }, { "cell_type": "code", "execution_count": 23, "id": "9ac69fbd-55db-435b-bed6-5ae9186450e3", "metadata": { "tags": [] }, "outputs": [], "source": [ "#inputss['pixel_values'].shape" ] }, { "cell_type": "code", "execution_count": 24, "id": "0524f498-c8da-4e8a-8970-d75d2d0f6b8b", "metadata": { "tags": [] }, "outputs": [], "source": [ "#image_test.shape" ] }, { "cell_type": "code", "execution_count": 25, "id": "5417541b-49eb-4e43-a3e2-d937d9653e04", "metadata": { "tags": [] }, "outputs": [], "source": [ "max_lr = 1e-4" ] }, { "cell_type": "code", "execution_count": 26, "id": "da0ce190-1b3e-4c12-9e9f-91cbc076d044", "metadata": { "tags": [] }, "outputs": [], "source": [ "clip_seq_dim = 257 #blip2 image encoder shapes\n", "clip_emb_dim = 1408 #blip2 image encoder shapes\n", "hidden_dim = 2048" ] }, { "cell_type": "markdown", "id": "5b79bd38-6990-4504-8d45-4a68d57d8885", "metadata": {}, "source": [ "### SD VAE (blurry images)" ] }, { "cell_type": "code", "execution_count": 27, "id": "01baff79-8114-482b-b115-6f05aa8ad691", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "param counts:\n", "83,653,863 total\n", "0 trainable\n" ] } ], "source": [ "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)" ] }, { "cell_type": "markdown", "id": "260e5e4a-f697-4b2c-88fc-01f6a54886c0", "metadata": {}, "source": [ "### MindEye modules" ] }, { "cell_type": "code", "execution_count": 28, "id": "c44c271b-173f-472e-b059-a2eda0f4c4c5", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "MindEyeModule()" ] }, "execution_count": 28, "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": 29, "id": "038a5d61-4769-40b9-a004-f4e7b5b38bb0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "param counts:\n", "32,215,040 total\n", "32,215,040 trainable\n", "param counts:\n", "32,215,040 total\n", "32,215,040 trainable\n", "torch.Size([2, 1, 15729]) torch.Size([2, 1, 2048])\n" ] } ], "source": [ "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], 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", "print(b.shape, model.ridge(b).shape)" ] }, { "cell_type": "code", "execution_count": 30, "id": "3602c333-d029-465c-8fb4-c3ccffdba6fd", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "param counts:\n", "772,419,072 total\n", "772,419,072 trainable\n", "param counts:\n", "804,634,112 total\n", "804,634,112 trainable\n", "torch.Size([4, 2048])\n", "torch.Size([4, 257, 1408])\n" ] } ], "source": [ "from functools import partial\n", "from diffusers.models.vae import Decoder\n", "class BrainNetwork(nn.Module):\n", " def __init__(self, out_dim=768, in_dim=15724, clip_size=768, h=4096, n_blocks=4, norm_type='ln', act_first=False, drop=.15, blurry_dim=16):\n", " super().__init__()\n", " self.blurry_dim = blurry_dim\n", " norm_func = partial(nn.BatchNorm1d, num_features=h) if norm_type == 'bn' else partial(nn.LayerNorm, normalized_shape=h)\n", " act_fn = partial(nn.ReLU, inplace=True) if norm_type == 'bn' else nn.GELU\n", " act_and_norm = (act_fn, norm_func) if act_first else (norm_func, act_fn)\n", " self.lin0 = nn.Linear(in_dim, h)\n", " self.mlp = nn.ModuleList([\n", " nn.Sequential(\n", " nn.Linear(h, h),\n", " *[item() for item in act_and_norm],\n", " nn.Dropout(drop)\n", " ) for _ in range(n_blocks)\n", " ])\n", " self.lin1 = nn.Linear(h, out_dim, bias=True)\n", " # self.blin1 = nn.Linear(out_dim, blurry_dim, bias=True)\n", " self.n_blocks = n_blocks\n", " self.clip_size = clip_size\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", " # self.upsampler = Decoder(\n", " # in_channels=64,\n", " # out_channels=4,\n", " # up_block_types=[\"UpDecoderBlock2D\",\"UpDecoderBlock2D\",\"UpDecoderBlock2D\"],\n", " # block_out_channels=[64, 128, 256],\n", " # layers_per_block=1,\n", " # )\n", " \n", " def forward(self, x):\n", " x = self.lin0(x)\n", " residual = x\n", " for res_block in range(self.n_blocks):\n", " x = self.mlp[res_block](x)\n", " x += residual\n", " residual = x\n", " x = x.reshape(len(x), -1)\n", " x = self.lin1(x)\n", " # b = self.blin1(x)\n", " # b = self.upsampler(b.reshape(len(b), -1, 7, 7))\n", " c = self.clip_proj(x.reshape(len(x), -1, self.clip_size))\n", " # return c, b\n", " return c\n", "\n", "model.backbone = BrainNetwork(h=2048, in_dim=hidden_dim, clip_size=clip_emb_dim, out_dim=clip_emb_dim*clip_seq_dim, blurry_dim=64*7*7) \n", "utils.count_params(model.backbone)\n", "utils.count_params(model)\n", "\n", "b = torch.randn((4,hidden_dim))\n", "print(b.shape)\n", "clip_ = model.backbone(b)\n", "print(clip_.shape)" ] }, { "cell_type": "code", "execution_count": 31, "id": "e14d0482-dc42-43b9-9ce1-953c32f2c9c1", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Done with model preparations!\n", "param counts:\n", "804,634,112 total\n", "804,634,112 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, betas=(0.9, 0.95))\n", "\n", "if lr_scheduler_type == 'linear':\n", " lr_scheduler = torch.optim.lr_scheduler.LinearLR(\n", " optimizer,\n", " total_iters=int(num_epochs*(num_train*num_devices//batch_size)),\n", " last_epoch=-1\n", " )\n", "elif lr_scheduler_type == 'cycle':\n", " total_steps=int(num_epochs*(num_train*num_devices//batch_size))\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": "markdown", "id": "983f458b-35b8-49f2-b6db-80296cece730", "metadata": {}, "source": [ "# Weights and Biases" ] }, { "cell_type": "code", "execution_count": 32, "id": "0a25a662-daa8-4de9-9233-8364800fcb6b", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wandb mindeyev2 run captions\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mckadirt\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "wandb_config:\n", " {'model_name': 'captions', 'batch_size': 128, 'num_epochs': 30, 'use_image_aug': False, 'max_lr': 0.0001, 'lr_scheduler_type': 'cycle', 'mixup_pct': 0.66, 'num_train': 24958, 'num_test': 2770, 'seed': 42, 'distributed': False, 'num_devices': 1, 'world_size': 1}\n" ] }, { "data": { "text/html": [ "wandb version 0.16.0 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.5" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /fsx/proj-fmri/ckadirt/MindEyeV2/src/wandb/run-20231119_163615-o1xwsqre" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run captions to Weights & Biases (docs)
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://stability.wandb.io/ckadirt/mindeyev2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://stability.wandb.io/ckadirt/mindeyev2/runs/o1xwsqre" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# params for wandb\n", "if local_rank==0 and True: # only use main process for wandb logging\n", " import wandb\n", " \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", " \"batch_size\": batch_size,\n", " \"num_epochs\": num_epochs,\n", " \"use_image_aug\": use_image_aug,\n", " \"max_lr\": max_lr,\n", " \"lr_scheduler_type\": lr_scheduler_type,\n", " \"mixup_pct\": mixup_pct,\n", " \"num_train\": num_train,\n", " \"num_test\": num_test,\n", " \"seed\": seed,\n", " \"distributed\": distributed,\n", " \"num_devices\": num_devices,\n", " \"world_size\": world_size,\n", " }\n", " print(\"wandb_config:\\n\",wandb_config)\n", " if False: # 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": "code", "execution_count": 33, "id": "4e5de216-5318-4b45-ac02-113f03105adc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
       "│ n++                                                                                              │\n",
       "│    ▲                                                                                             │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "SyntaxError: invalid syntax\n",
       "
\n" ], "text/plain": [ "\u001b[91m╭──────────────────────────────────────────────────────────────────────────────────────────────────╮\u001b[0m\n", "\u001b[91m│\u001b[0m n++ \u001b[91m│\u001b[0m\n", "\u001b[91m│\u001b[0m \u001b[1;91m▲\u001b[0m \u001b[91m│\u001b[0m\n", "\u001b[91m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", "\u001b[1;91mSyntaxError: \u001b[0minvalid syntax\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n++" ] }, { "cell_type": "markdown", "id": "5b0ae095-3203-4eb8-8606-acc2db6ccf20", "metadata": {}, "source": [ "# More custom functions" ] }, { "cell_type": "code", "execution_count": null, "id": "827ead88-7eb3-47cc-82da-31565063b927", "metadata": { "tags": [] }, "outputs": [], "source": [ "# using the same preprocessing as was used in MindEye + BrainDiffuser\n", "pixcorr_preprocess = transforms.Compose([\n", " transforms.Resize(425, interpolation=transforms.InterpolationMode.BILINEAR),\n", "])\n", "def pixcorr(images,brains):\n", " # Flatten images while keeping the batch dimension\n", " all_images_flattened = pixcorr_preprocess(images).reshape(len(images), -1)\n", " all_brain_recons_flattened = pixcorr_preprocess(brains).view(len(brains), -1)\n", " corrmean = torch.diag(utils.batchwise_pearson_correlation(all_images_flattened, all_brain_recons_flattened)).mean()\n", " return corrmean" ] }, { "cell_type": "markdown", "id": "d5690151-2131-4918-b750-e869cbd1a8a8", "metadata": {}, "source": [ "# Main" ] }, { "cell_type": "code", "execution_count": null, "id": "12de6387-6e18-4e4b-b5ce-a847d625330a", "metadata": { "tags": [] }, "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", " diffusion_diffuser.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", " diffusion_diffuser.load_state_dict(checkpoint['model_state_dict'])\n", " del checkpoint\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": null, "id": "99f09f76-4481-4133-b09a-a22b10dbc0c4", "metadata": { "tags": [] }, "outputs": [], "source": [ "model, optimizer, train_dl, test_dl, lr_scheduler = accelerator.prepare(\n", "model, optimizer, train_dl, test_dl, lr_scheduler\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "bfeeda32-82ca-4364-bce1-eaa41b4f3e25", "metadata": { "tags": [] }, "outputs": [], "source": [ "\"\"\"transform = transforms.Compose(\n", " [\n", " transforms.Resize(\n", " (224, 224),\n", " ),\n", " transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),\n", " ]\n", " )\n", "\n", "def tensor_2_embed(image): \n", " image_for_blip2 = transform(image)\n", " \n", " #Generate embeddings\n", " with blip2_model.maybe_autocast():\n", " blip2_target = blip2_model.ln_vision(blip2_model.visual_encoder(image_for_blip2))\n", " \n", " return blip2_target\n", "\n", "def embed_2_caption(image_embeds, model):\n", " image_embeds = image_embeds.float()\n", " image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(\n", " image.device)\n", "\n", " query_tokens = model.query_tokens.expand(image_embeds.shape[0], -1, -1)\n", " query_output = model.Qformer.bert(\n", " query_embeds=query_tokens,\n", " encoder_hidden_states=image_embeds,\n", " encoder_attention_mask=image_atts,\n", " return_dict=True)\n", "\n", " inputs_t5 = model.t5_proj(query_output.last_hidden_state)\n", " atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)\n", " prompt = model.prompt\n", " input_tokens = model.t5_tokenizer(\n", " prompt, padding=\"longest\", return_tensors=\"pt\"\n", " ).to(image.device)\n", " encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)\n", " \n", " with model.maybe_autocast(dtype=torch.bfloat16):\n", " inputs_embeds = model.t5_model.encoder.embed_tokens(input_tokens.input_ids)\n", " inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)\n", "\n", " outputs = model.t5_model.generate(\n", " inputs_embeds=inputs_embeds,\n", " attention_mask=encoder_atts)\n", " output_text = model.t5_tokenizer.batch_decode(\n", " outputs, skip_special_tokens=True)\n", " \n", " return output_text\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "636b4684-df9a-4e29-8683-86fb035ba690", "metadata": { "tags": [] }, "outputs": [], "source": [ "wandb_log = True" ] }, { "cell_type": "code", "execution_count": null, "id": "60be0d5f-3e94-4612-9373-61b53d836393", "metadata": { "tags": [] }, "outputs": [], "source": [ "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", "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", " test_loss_clip_total = 0.\n", " test_loss_blurry_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", " if epoch == 0:\n", " lrs.append(0)\n", " break\n", " with torch.cuda.amp.autocast():\n", " optimizer.zero_grad()\n", "\n", " voxel = voxels[behav[:,0,5].cpu().long()].to(device)\n", " \n", " image = images[behav[:,0,0].cpu().long()].to(device).float()\n", "\n", " # blurry_image_enc = autoenc.encode(image).latent_dist.mode()\n", " \n", " if use_image_aug: image = img_augment(image)\n", " # clip_target = clip_model.embed_image(image)\n", " clip_target = embed_images_b2(image)[0].to(device) #####CHANGED\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", "\n", " voxel_ridge = model.ridge(voxel)\n", " \n", " # clip_voxels, blurry_image_enc_ = model.backbone(voxel_ridge)\n", " clip_voxels = 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_mse= mse(clip_voxels, clip_target)\n", "\n", " # loss_blurry = mse(blurry_image_enc_, blurry_image_enc) \n", "\n", " loss_clip_total += loss_clip.item()\n", " # loss_blurry_total += loss_blurry.item()\n", "\n", " # loss = loss_blurry + loss_clip\n", " loss = 0.7 * loss_clip + 0.3 * loss_mse\n", " if (train_i % 10 == 0):\n", " print(train_i, loss)\n", " # print(batch_size)\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", " # 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(utils.batchwise_cosine_similarity(clip_voxels_norm, clip_target_norm), labels, k=1)\n", " bwd_percent_correct += utils.topk(utils.batchwise_cosine_similarity(clip_target_norm, clip_voxels_norm), labels, k=1)\n", "\n", " # with torch.no_grad():\n", " # # only doing pixcorr eval on a subset (8) of the samples per batch because its costly & slow to compute autoenc.decode()\n", " # random_samps = np.random.choice(np.arange(len(voxel)), size=8, replace=False)\n", " # blurry_recon_images = autoenc.decode(blurry_image_enc_[random_samps]).sample.clamp(0,1)\n", " # blurry_pixcorr += pixcorr(image[random_samps], blurry_recon_images)\n", "\n", " if lr_scheduler_type is not None:\n", " lr_scheduler.step()\n", " \n", " model.eval()\n", " for test_i, (behav, past_behav, future_behav, old_behav) in enumerate(test_dl):\n", " with torch.cuda.amp.autocast():\n", " with torch.no_grad(): \n", " # all test samples should be loaded per batch such that test_i should never exceed 0\n", " if len(behav) != num_test: print(\"!\",len(behav),num_test)\n", " \n", " ## Average same-image repeats ##\n", " if test_image is None:\n", " voxel = voxels[behav[:,0,5].cpu().long()].to(device)\n", " \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", " # sample of batch_size\n", " random_indices = torch.arange(len(test_voxel))[:batch_size] #torch.randperm(len(test_voxel))[:300]\n", " voxel = test_voxel[random_indices].to(device)\n", " image = test_image[random_indices].to(device)\n", " assert len(image) == batch_size\n", " \n", " # blurry_image_enc = autoenc.encode(image).latent_dist.mode()\n", " \n", " # clip_target = clip_model.embed_image(image.float())\n", " clip_target = embed_images_b2(image)[0].to(device) #####CHANGED\n", " \n", " voxel_ridge = model.ridge(voxel)\n", " \n", " # clip_voxels, blurry_image_enc_ = model.backbone(voxel_ridge)\n", " clip_voxels = 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", " \n", " loss_clip = mse(clip_voxels, clip_target)\n", "\n", " # loss_blurry = mse(blurry_image_enc_, blurry_image_enc)\n", " \n", " # loss = loss_blurry + loss_clip\n", " loss = loss_clip\n", " \n", " utils.check_loss(loss)\n", " \n", " test_losses.append(loss.item())\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)\n", " test_bwd_percent_correct += utils.topk(utils.batchwise_cosine_similarity(clip_target_norm, clip_voxels_norm), labels, k=1)\n", "\n", " # # halving the batch size because the decoder is computationally heavy\n", " # blurry_recon_images = autoenc.decode(blurry_image_enc_[:len(voxel)//2]).sample.clamp(0,1)\n", " # blurry_recon_images = torch.vstack((blurry_recon_images, autoenc.decode(blurry_image_enc_[len(voxel)//2:]).sample.clamp(0,1)))\n", " # test_blurry_pixcorr += pixcorr(image, blurry_recon_images)\n", "\n", " #Find captions and print next to images\n", " #caption1 = embed_2_caption(clip_voxels[[0]], blip2_model)\n", " #caption2 = embed_2_caption(clip_voxels[[1]], blip2_model)\n", "\n", " #true_embed1 = tensor_2_embed(image[[0]])\n", " #true_embed2 = tensor_2_embed(image[[1]])\n", "\n", " # print(clip_voxels[[0]].shape)\n", " # print(true_embed1.shape)\n", " \n", " #true_caption1 = embed_2_caption(true_embed1, blip2_model)\n", " #true_caption2 = embed_2_caption(true_embed2, blip2_model)\n", " \n", " # transform blurry recon latents to images and plot it\n", " #fig, axes = plt.subplots(2, 2, figsize=(8, 4))\n", " #axes[0,0].imshow(utils.torch_to_Image(image[[0]]))\n", " #axes[0,1].imshow(utils.torch_to_Image(image[[1]]))\n", " #axes[0,0].axis('off'); axes[0,1].axis('off'); axes[1,0].axis('off'); axes[1,1].axis('off')\n", " #axes[0,0].set_title(caption1)\n", " #axes[0,1].set_title(caption2)\n", " #axes[1,0].set_title(true_caption1)\n", " #axes[1,1].set_title(true_caption2)\n", "\n", " #plt.show()\n", " \n", " # # transform blurry recon latents to images and plot it\n", " # fig, axes = plt.subplots(1, 4, figsize=(8, 4))\n", " # axes[0].imshow(utils.torch_to_Image(image[[0]]))\n", " # axes[1].imshow(utils.torch_to_Image(autoenc.decode(blurry_image_enc_[[0]]).sample.clamp(0,1)))\n", " # axes[2].imshow(utils.torch_to_Image(image[[1]]))\n", " # axes[3].imshow(utils.torch_to_Image(autoenc.decode(blurry_image_enc_[[1]]).sample.clamp(0,1)))\n", " # axes[0].axis('off'); axes[1].axis('off'); axes[2].axis('off'); axes[3].axis('off')\n", " # axes[0].set_title(caption1)\n", " # axes[3].set_title(caption2)\n", " # plt.show()\n", " \n", "\n", " if local_rank==0: \n", " # if utils.is_interactive(): clear_output(wait=True)\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", " }\n", " progress_bar.set_postfix(**logs)\n", " \n", " fig, axes = plt.subplots(1, 8, figsize=(10, 4))\n", " jj=-1\n", " for j in [0,1,2,3,4,5,6,7]:\n", " jj+=1\n", " axes[jj].imshow(utils.torch_to_Image(image[j]))\n", " axes[jj].axis('off')\n", "\n", " if wandb_log:\n", " generated_captions = embeds_to_captions_b2(clip_voxels[0:8])\n", " print(generated_captions[1])\n", " logs[f\"test/recons\"] = wandb.Image(fig, caption=f\"epoch{epoch:03d}\" + \"\\n\".join(generated_captions[1]))\n", " plt.close()\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": { "tags": [] }, "outputs": [], "source": [ "plt.plot(losses)\n", "plt.show()\n", "plt.plot(test_losses)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "ccfccd4f-764d-4624-842c-f931676eb43b", "metadata": {}, "outputs": [], "source": [ "print('test')" ] }, { "cell_type": "code", "execution_count": null, "id": "f1a60e19-c440-4c9c-a634-30186209012f", "metadata": {}, "outputs": [], "source": [ "def tensor_2_embed_old(tensor):\n", " embed_array = torch.zeros((tensor.shape[0],257, 1024)) \n", " to_pil = ToPILImage()\n", " for sample in range(tensor.shape[0]):\n", " PIL_image = to_pil(tensor[sample])\n", " image_for_blip2 = vis_processors[\"eval\"](PIL_image).unsqueeze(0).to(device)\n", " #Generate embeddings\n", " with blip2_model.maybe_autocast():\n", " blip2_target = blip2_model.ln_vision(blip2_model.visual_encoder(image_for_blip2))\n", " embed_array[sample] = blip2_target\n", " \n", " return embed_array" ] }, { "cell_type": "code", "execution_count": null, "id": "d39ddada-47f7-4111-92fa-0dd98e8a83d6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ec8ed96a-61fa-4c20-8da2-fcd9d0a2ed38", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6228eb1a-e8e7-4500-b7bc-d0c57bcac4c6", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" }, "toc": { "base_numbering": 1, 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