From d5b9242b2c2f8772acd848da2d84f5c5488423df Mon Sep 17 00:00:00 2001 From: weihao Date: Thu, 11 Apr 2024 20:40:59 +0100 Subject: [PATCH] update inference --- README.md | 73 +++++++ docs/index.html | 2 +- umbrae/download_checkpoint.sh | 10 + umbrae/download_data.sh | 47 +++++ umbrae/inference.py | 210 ++++++++++++++++++++ umbrae/inference_rec.py | 232 ++++++++++++++++++++++ umbrae/model.py | 155 +++++++++++++++ umbrae/model_weights/mm_projector.bin | Bin 0 -> 8398326 bytes umbrae/perceiver.py | 121 ++++++++++++ umbrae/requirements.txt | 30 +++ umbrae/utils.py | 265 ++++++++++++++++++++++++++ 11 files changed, 1144 insertions(+), 1 deletion(-) create mode 100644 umbrae/download_checkpoint.sh create mode 100644 umbrae/download_data.sh create mode 100644 umbrae/inference.py create mode 100644 umbrae/inference_rec.py create mode 100644 umbrae/model.py create mode 100644 umbrae/model_weights/mm_projector.bin create mode 100644 umbrae/perceiver.py create mode 100644 umbrae/requirements.txt create mode 100644 umbrae/utils.py diff --git a/README.md b/README.md index c56b599..b4a6813 100644 --- a/README.md +++ b/README.md @@ -43,6 +43,8 @@

UMBRAE decodes multimodal explanations from brain signals. (1) We introduce a universal brain encoder for multimodal-brain alignment and recover conceptual and spatial details by using multimodal large language models. (2) We introduce cross-subject training to overcome unique brain patterns of different individuals. This allows brain signals from multiple subjects to be trained within the same model This allows brain signals from multiple subjects to be trained within the same model. (3) Our method supports weakly-supervised subject adaptation, enabling the training of a model for a new subject in a data-efficient manner. (4) For evaluation, we introduce BrainHub, a brain understanding benchmark, based on NSD and COCO. ## News :triangular_flag_on_post: +- [2024/04/12] Inference and pretrained model available. Training code coming up soon. +- [2024/04/11] BrainHub is available. - [2024/03/15] Both project and arXiv are available. ## Method @@ -53,6 +55,77 @@ Overview of UMBRAE. Our brain encoder includes subject-specific tokenizers and a + +## Installation + +### Environment + +```bash +conda create -n brainx python=3.10 +conda activate brainx +pip install -r requirements.txt +``` + +### Download Data and Checkpoints + +The training and inference scripts support automatically downloading the dataset if the designated path is empty. However, this process can be quite slow. You can try the following script to download all data in advance if this happens. Please fill out the NSD [Data Access form](https://forms.gle/xue2bCdM9LaFNMeb7) and agree to the [Terms and Conditions](https://cvnlab.slite.page/p/IB6BSeW_7o/Terms-and-Conditions). + +Download Checkpoints from [Hugging Face](https://huggingface.co/datasets/weihaox/brainx). + +```bash +bash download_data.sh +bash download_checkpoint.sh +``` + +## Inference + +Our method inherits multimodal understanding capabilities of MLLMs, enabling the switch between different tasks through different prompts. You can either use the prompts listed in our paper or create customised instructions according to actual needs. + +```bash +prompt_caption='Describe this image as simply as possible.' +prompt_ground='Please interpret this image and give coordinates [x1,y1,x2,y2] for each object you mention.' + +python inference.py --fmri_encoder 'brainx' --subj 1 --prompt "$prompt_ground" \ + --data_path 'nsd_data' --brainx_path 'train_logs/brainx.pth' \ + --save_path 'evaluation/eval_caption/caption_results/umbrae/sub01_dim1024' +``` + +Given that identified classes might be named differently, or simply absent from ground truth labels, we evaluate bounding boxes through REC. We use prompt `"Locate in and provide its coordinates, please"`, but others like `"Can you point out in the image and provide the bounding boxes of its location?"` shall also work. + +```bash +python inference_rec.py --data_path 'nsd_data' --fmri_encoder 'brainx' \ + --subj 1 --brainx_path 'train_logs/brainx.pth' \ + --save_path 'evaluation/eval_bbox_rec/rec_results/umbrae/sub01_dim1024' +``` + +## Evaluation + +The benchmark, including groundtruth data, evaluation scripts, and baseline results, is in [brainhub](https://github.com/weihaox/BrainHub). + +1. Download `brainhub` to the root path: `git clone https://github.com/weihaox/BrainHub` + +2. Process groundtruth test images: `python processing/decode_images.py` + +3. Run evaluation for brain captioning and grounding: + +```bash +cd BrainHub +for sub in 1 2 5 7 +do + python eval_caption.py ../umbrae/evaluation/caption_results/umbrae/sub0${sub}_dim1024/fmricap.json \ + caption/images --references_json caption/fmri_cococap.json +done +``` + +```bash +for sub in 1 2 5 7 +do + python eval_bbox_rec.py --path_out "../umbrae/evaluation/bbox_results/umbrae/sub0${sub}_dim1024" +done +``` + +We also provide baseline results associated with BrainHub, including the captioning results from [SDRecon](https://github.com/yu-takagi/StableDiffusionReconstruction), [BrainCap](https://arxiv.org/abs/2305.11560), and [OneLLM](https://onellm.csuhan.com/), as well as the captioning and grounding results from [UMBRAE](https://weihaox.github.io/UMBRAE/). + ## Citation ```bibtex diff --git a/docs/index.html b/docs/index.html index 014a680..df2aed5 100644 --- a/docs/index.html +++ b/docs/index.html @@ -29,7 +29,7 @@

Multimodal Brain Decoding from our UMBRAE appl

[Paper]      [Code]      - [Data] + [BrainHub]

diff --git a/umbrae/download_checkpoint.sh b/umbrae/download_checkpoint.sh new file mode 100644 index 0000000..29287e9 --- /dev/null +++ b/umbrae/download_checkpoint.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# ------------------------------------------------------------------ +# @File : download_checkpoint.sh +# @Time : 2024/03/16 17:30:00 +# @Author : Weihao Xia (xiawh3@outlook.com) +# @Version : 1.0 +# @Desc : download Checkpoints from Hugging Face +# ------------------------------------------------------------------ + +python -c 'from huggingface_hub import snapshot_download; snapshot_download(repo_id="weihaox/brainx", repo_type="dataset", local_dir="./", , ignore_patterns=["all_images.pt", ".gitattributes"])' \ No newline at end of file diff --git a/umbrae/download_data.sh b/umbrae/download_data.sh new file mode 100644 index 0000000..d481839 --- /dev/null +++ b/umbrae/download_data.sh @@ -0,0 +1,47 @@ +#!/bin/bash +# ------------------------------------------------------------------ +# @File : download_data.sh +# @Time : 2024/02/13 22:00:00 +# @Author : Weihao Xia (xiawh3@outlook.com) +# @Version : 1.0 +# @Desc : download the Natural Scenes Dataset from Hugging Face +# ------------------------------------------------------------------ + +# set the destination +destination="nsd" + +subdirs=("train" "test" "val") + +for subdir in "${subdirs[@]}"; do + full_destination="${destination}/webdataset_avg_split/${subdir}/" + mkdir -p "$full_destination" +done + +declare -a i_values=(1 2 5 7) + +# Download the train set +for i in "${i_values[@]}"; do + for j in {0..17}; do + url="https://huggingface.co/datasets/pscotti/naturalscenesdataset/resolve/main/webdataset_avg_split/train/train_subj0${i}_${j}.tar" + wget -P "$train_destination" "$url" + done +done + +# Download the validation set +for i in "${i_values[@]}"; do + url="https://huggingface.co/datasets/pscotti/naturalscenesdataset/resolve/main/webdataset_avg_split/val/val_subj0${i}_0.tar" + wget -P "$val_destination" "$url" + done +done + +# Download the test set +for i in "${i_values[@]}"; do + for j in {0..1}; do + url="https://huggingface.co/datasets/pscotti/naturalscenesdataset/resolve/main/webdataset_avg_split/test/test_subj0${i}_${j}.tar" + wget -P "$test_destination" "$url" + done +done + + +# download test set images (just for evaluation) +wget -P "../brainhub/caption" "https://huggingface.co/datasets/weihaox/brainx/resolve/main/all_images.pt" \ No newline at end of file diff --git a/umbrae/inference.py b/umbrae/inference.py new file mode 100644 index 0000000..8a47dfc --- /dev/null +++ b/umbrae/inference.py @@ -0,0 +1,210 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : inference_brainx.py +@Time : 2024/02/15 16:54:30 +@Author : Weihao Xia +@Version : 1.0 +@Desc : +python inference.py --fmri_encoder 'brainx' --subj 1 \ + --data_path '/home/wx258/project/nsd_data' \ + --brainx_path 'train_logs/brainx.pth' \ + --prompt 'Describe this image as simply as possible.' \ + --save_path 'evaluation/eval_caption/caption_results/brainx_sub01_dim1024' +''' + +import os +import json +import time +import argparse +import braceexpand +import webdataset as wds +import utils + +import torch +from transformers import LlamaForCausalLM, LlamaTokenizer +from torchvision.transforms import ToPILImage + +from model import BrainX +from utils import postprocess + +parser = argparse.ArgumentParser() +parser.add_argument('--shikra_path', default='model_weights/shikra-7b') +parser.add_argument('--brainx_path', default='train_logs/training_demo/best.pth') +parser.add_argument('--adapter_path', default='model_weights/mm_projector.bin') +parser.add_argument('--fmri_encoder', type=str, default='brainx', help='type of brainnet', choices=['brainx']) +parser.add_argument('--use_norm', type=bool, default=False, help='whether to use norm layer in the model') +parser.add_argument('--use_token', type=bool, default=False, help='whether to use learnable token in the model') +parser.add_argument('--feat_dim', type=int, help='output dimension of the fmri encoder', default=1024, choices=[1024, 4096]) +parser.add_argument('--data_path', type=str, default="nsd_data", help='path to nsd data') +parser.add_argument('--save_path', type=str, default='results', help='path to save results') +parser.add_argument('--save_image', type=bool, default=False, help='save image or not') +parser.add_argument('--prompt', required=True, help='prompt for the model') +parser.add_argument('--subj', type=int, default=1, choices=[1, 2, 5, 7]) +parser.add_argument('--seed', type=int, default=42) +args = parser.parse_args() + +# create global variables without the args prefix +for attribute_name in vars(args).keys(): + globals()[attribute_name] = getattr(args, attribute_name) + +# need non-deterministic CuDNN for conv3D to work +utils.seed_everything(seed, cudnn_deterministic=False) + +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + +# prepare models and data loaders +print('prepare NSD webdataset data...') +val_url = f"{data_path}/webdataset_avg_split/test/test_subj0{subj}_" + "{0..1}.tar" +meta_url = f"{data_path}/webdataset_avg_split/metadata_subj0{subj}.json" +num_val = 982 + +# result_dir = os.path.join(os.path.dirname(__file__), 'results/sub{:02d}_dim{}'.format(subj, feat_dim)) +result_dir = os.path.join(save_path, 'sub{:02d}_dim{}'.format(subj, feat_dim)) +os.makedirs(result_dir, exist_ok=True) + +# save config in a json file +args_dict = vars(args) +with open(os.path.join(result_dir, 'config.json'), 'w') as file: + json.dump(args_dict, file, indent=4) + +print('prepare train and validation dataloaders...') +to_tuple = ["voxels", "images"] +val_batch_size = 1 +split_by_node = lambda urls: urls +val_url = list(braceexpand.braceexpand(val_url)) +val_data = wds.WebDataset(val_url, resampled=False, cache_dir=data_path, nodesplitter=split_by_node) \ + .decode("torch")\ + .rename(images="jpg;png", voxels='nsdgeneral.npy', trial="trial.npy", coco="coco73k.npy", reps="num_uniques.npy") \ + .to_tuple(*to_tuple) \ + .batched(val_batch_size, partial=False) + +val_dl = torch.utils.data.DataLoader(val_data, batch_size=None, num_workers=1, shuffle=False) + +voxels_per_subj = {1: 15724, 2: 14278, 3: 15226, 4: 13153, 5: 13039, 6: 17907, 7: 12682, 8: 14386} +num_voxels = voxels_per_subj.get(subj) + +kwargs = {'hidden_dim': 1024, 'out_dim': feat_dim, 'num_latents': 256, 'use_norm': use_norm, 'use_token': use_token} + +if fmri_encoder == 'brainx': + voxel2emb = BrainX(**kwargs) +else: + raise ValueError("The fmri encoder is not implemented.") +voxel2emb.to(device) + +checkpoint = torch.load(brainx_path, map_location='cpu') +voxel2emb.load_state_dict(checkpoint['model_state_dict'], strict=False) +voxel2emb.eval() + +# inference: predict image features from fmri +print('inference: predict image features from fmri...') +emb_voxel_list, image_list = [], [] +for val_i, (voxel, image) in enumerate(val_dl): + with torch.no_grad(): + with torch.cuda.amp.autocast(): + # repeat_index = val_i % 3 + # voxel = voxel[:,repeat_index].float() + voxel = torch.mean(voxel, axis=1).float() + + emb_voxel = voxel2emb(voxel.to(device), modal=f'fmri{subj}') + + emb_voxel_list.append(emb_voxel) + if save_image: + image_list.append(image) # for visualization + +# assign image features to the predicted features from fmri +image_features = torch.cat(emb_voxel_list, dim=0) +print(f"image_features.shape: {image_features.shape}") + +if save_image: + image_list = torch.cat(image_list, dim=0) + +# load llama with the fine-tuned shikra model +finetuned_llama = shikra_path # 'model_weights/shikra-7b' # shikra +tokenizer = LlamaTokenizer.from_pretrained(finetuned_llama, padding_side='left') +model = LlamaForCausalLM.from_pretrained(finetuned_llama) +model.to(device) + +if feat_dim == 1024: + # load mm_projector + mm_projector = torch.nn.Linear(1024, 4096) + mm_projector_weights = torch.load(adapter_path, map_location='cpu') + if adapter_path == 'model_weights/mm_projector.bin': + adjusted_state_dict = {k.split('.')[-1]: v for k, v in mm_projector_weights.items()} + mm_projector.load_state_dict(adjusted_state_dict) + else: + mm_projector.load_state_dict(mm_projector_weights['model_state_dict'], strict=False) + + mm_projector.to("cuda:0") + image_features = mm_projector(image_features.to(torch.float32)) + print(f"image_features.shape: {image_features.shape}") + +# process prompt +system = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:" +user_image = " " + "" * 256 + " " + +if '' in prompt: + user_prompt = prompt.replace('', user_image) +else: + user_prompt = prompt + user_image +input_text = system + user_prompt + " ASSISTANT:" + +input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)#.cuda() +inputs_embeds = model.model.embed_tokens(input_ids) + +gen_kwargs = dict( + use_cache=True, + do_sample=False, + pad_token_id=2, # tokenizer.pad_token_id, + bos_token_id=1, # tokenizer.bos_token_id, + eos_token_id=2, # tokenizer.eos_token_id, + max_new_tokens=512, +) + +cap_result = {} +for cur_image_idx in range(image_features.shape[0]): + new_input_embeds = [] + for cur_input_ids, cur_input_embeds in zip(input_ids, inputs_embeds): + cur_image_features = image_features[cur_image_idx] + num_patches = cur_image_features.shape[0] + image_start_tokens = torch.where(cur_input_ids == 32001)[0] + for image_start_token_pos in image_start_tokens: + cur_image_features = image_features[cur_image_idx].to(device=cur_input_embeds.device) + num_patches = cur_image_features.shape[0] + if cur_input_ids[image_start_token_pos + num_patches + 1] != 32002: + raise ValueError("The image end token should follow the image start token.") + + cur_new_input_embeds = torch.cat((cur_input_embeds[:image_start_token_pos + 1], cur_image_features, + cur_input_embeds[image_start_token_pos + num_patches + 1:]), dim=0) + new_input_embeds.append(cur_new_input_embeds) + inputs_embeds = torch.stack(new_input_embeds, dim=0) + + st_time = time.time() + with torch.inference_mode(): + with torch.autocast(dtype=torch.float16, device_type='cuda'): + output_ids = model.generate(inputs_embeds=inputs_embeds.float(), **gen_kwargs) + print(f"done generated in {time.time() - st_time} seconds") + + response = tokenizer.batch_decode(output_ids)[0] + + # print(f"input: {input_text}") + print(f"response: {response.strip(' ')}") + + # save response in a txt file + with open(os.path.join(result_dir, 'response.txt'), 'a') as f: + f.write(f'response_{cur_image_idx}: \n') + f.write(response + '\n') + + # save response in a json file + bbox, caption = utils.extract_id_bbox_caption(response) + cap_result[str(cur_image_idx)] = caption # {image_name: caption; ...} + + # save processed image (only for bbox tasks) + if save_image: + _, processed_image = postprocess(response, image=ToPILImage()(image_list[cur_image_idx]), width=5) + if processed_image is not None: + output_path = os.path.join(result_dir, f'{cur_image_idx}_prompt.png') + processed_image.save(output_path) + +with open(os.path.join(result_dir, 'fmricap.json'), 'w') as f: + json.dump(cap_result, f) \ No newline at end of file diff --git a/umbrae/inference_rec.py b/umbrae/inference_rec.py new file mode 100644 index 0000000..c61ee13 --- /dev/null +++ b/umbrae/inference_rec.py @@ -0,0 +1,232 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : inference_rec.py +@Time : 2024/02/15 23:40:24 +@Author : Weihao Xia +@Version : 1.0 +@Desc : +python inference_rec.py \ + --data_path '/home/wx258/project/nsd_data' --fmri_encoder 'brainx' --subj 1 \ + --brainx_path 'train_logs/brainx.pth' \ + --save_path 'evaluation/eval_bbox_rec/rec_results/brainx_sub01_dim1024' +''' + +import os +import json +import time +import argparse +import braceexpand +import webdataset as wds +import utils + +import torch +from transformers import LlamaForCausalLM, LlamaTokenizer +from torchvision.transforms import ToPILImage + +from model import BrainX +from utils import postprocess, extract_boxes + +parser = argparse.ArgumentParser() +parser.add_argument('--shikra_path', default='model_weights/shikra-7b') +parser.add_argument('--brainx_path', default='train_logs/training_demo/best.pth') +parser.add_argument('--adapter_path', default='model_weights/mm_projector.bin') +parser.add_argument('--fmri_encoder', type=str, default='brainx', help='type of brainnet', choices=['brainx']) +parser.add_argument('--use_norm', type=bool, default=False, help='whether to use norm layer in the model') +parser.add_argument('--use_token', type=bool, default=False, help='whether to use learnable token in the model') +parser.add_argument('--feat_dim', type=int, help='output dimension of the fmri encoder', default=1024, choices=[1024, 4096]) +parser.add_argument('--data_path', type=str, default='nsd_data', help='path to nsd data') +parser.add_argument('--save_path', type=str, default='rec_results', help='path to save results') +parser.add_argument('--save_image', type=bool, default=False, help='save image or not') +parser.add_argument('--subj', type=int, default=1, choices=[1, 2, 5, 7]) +parser.add_argument('--seed', type=int, default=42) +args = parser.parse_args() + +# create global variables without the args prefix +for attribute_name in vars(args).keys(): + globals()[attribute_name] = getattr(args, attribute_name) + +# need non-deterministic CuDNN for conv3D to work +utils.seed_everything(seed, cudnn_deterministic=False) + +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + +# prepare models and data loaders +print('prepare NSD webdataset data...') +val_url = f"{data_path}/webdataset_avg_split/test/test_subj0{subj}_" + "{0..1}.tar" +meta_url = f"{data_path}/webdataset_avg_split/metadata_subj0{subj}.json" +num_val = 982 + +# result_dir = os.path.join(os.path.dirname(__file__), 'rec_results_tmp/sub{:02d}_dim{}'.format(subj, feat_dim)) +# result_dir = os.path.join(save_path, 'sub{:02d}_dim{}'.format(subj, feat_dim)) +result_dir = save_path +os.makedirs(result_dir, exist_ok=True) + +# save config in a json file +args_dict = vars(args) +with open(os.path.join(result_dir, 'config.json'), 'w') as file: + json.dump(args_dict, file, indent=4) + +print('prepare train and validation dataloaders...') +to_tuple = ["voxels", "images"] +val_batch_size = 1 +split_by_node = lambda urls: urls +val_url = list(braceexpand.braceexpand(val_url)) +val_data = wds.WebDataset(val_url, resampled=False, cache_dir=data_path, nodesplitter=split_by_node) \ + .decode("torch")\ + .rename(images="jpg;png", voxels='nsdgeneral.npy', trial="trial.npy", coco="coco73k.npy", reps="num_uniques.npy") \ + .to_tuple(*to_tuple) \ + .batched(val_batch_size, partial=False) + +val_dl = torch.utils.data.DataLoader(val_data, batch_size=None, num_workers=1, shuffle=False) + +voxels_per_subj = {1: 15724, 2: 14278, 3: 15226, 4: 13153, 5: 13039, 6: 17907, 7: 12682, 8: 14386} +num_voxels = voxels_per_subj.get(subj) + +kwargs = {'hidden_dim': 1024, 'out_dim': feat_dim, 'num_latents': 256, 'use_norm': use_norm, 'use_token': use_token} + +if fmri_encoder == 'brainx': + voxel2emb = BrainX(**kwargs) +else: + raise ValueError("The fmri encoder is not implemented.") +voxel2emb.to(device) + +checkpoint = torch.load(brainx_path, map_location='cpu') +voxel2emb.load_state_dict(checkpoint['model_state_dict'], strict=False) +voxel2emb.eval() + +gen_kwargs = dict( + use_cache=True, + do_sample=False, + pad_token_id=2, # tokenizer.pad_token_id, + bos_token_id=1, # tokenizer.bos_token_id, + eos_token_id=2, # tokenizer.eos_token_id, + max_new_tokens=512, +) + +emb_voxel_list, image_list = [], [] + +# inference: predict image features from fmri +for val_i, (voxel, image) in enumerate(val_dl): + with torch.no_grad(): + with torch.cuda.amp.autocast(): + voxel = torch.mean(voxel, axis=1).float() + + emb_voxel = voxel2emb(voxel.to(device), modal=f'fmri{subj}') + emb_voxel_list.append(emb_voxel) + if save_image: + image_list.append(image) # for visualization + +# assign image features to the predicted features from fmri +image_features = torch.cat(emb_voxel_list, dim=0) +print(f"image_features.shape: {image_features.shape}") + +# load llama with the fine-tuned shikra model +finetuned_llama = shikra_path # 'model_weights/shikra-7b' # shikra +tokenizer = LlamaTokenizer.from_pretrained(finetuned_llama, padding_side='left') +model = LlamaForCausalLM.from_pretrained(finetuned_llama) +model.to(device) + +if feat_dim == 1024: + # load mm_projector + mm_projector = torch.nn.Linear(1024, 4096) + mm_projector_weights = torch.load(adapter_path, map_location='cpu') + if adapter_path == 'model_weights/mm_projector.bin': + adjusted_state_dict = {k.split('.')[-1]: v for k, v in mm_projector_weights.items()} + mm_projector.load_state_dict(adjusted_state_dict) + else: + mm_projector.load_state_dict(mm_projector_weights['model_state_dict'], strict=False) + + mm_projector.to("cuda:0") + image_features = mm_projector(image_features.to(torch.float32)) + print(f"image_features.shape: {image_features.shape}") + +# process prompt +system = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:" +user_image = " " + "" * 256 + " " +''' +# shikra (Table 9 REC): +In the given , could you find and tell me the coordinates of ? +I need the coordinates of in , can you please assist me with that? +Locate in and provide its coordinates, please +# others: +Help me to locate in and give me its bounding boxes, please. +Can you point out in the image and provide the bounding boxes of its location? +Help me to locate in and give me its bounding boxes, please. +Provide a detailed description of the image using around 100-500 words, including the objects, attributes, and spatial locations depicted in the picture. +''' + +# prompt = "Can you point out in the image and provide the bounding boxes of its location?" +prompt = "Locate in and provide its coordinates, please" + +# load category for in prompt +expr_path = '../BrainHub/bbox/coco_bbox_categorized.json' +with open(expr_path, 'r') as f: + expr_dict = json.load(f) + +rec_result = {} +for cur_image_idx in range(image_features.shape[0]): + + expr_list = list(expr_dict[str(cur_image_idx)].keys()) + for expr in expr_list: + user_prompt = prompt.replace('', expr) + if '' in user_prompt: + user_prompt = user_prompt.replace('', user_image) + input_text = system + user_prompt + " ASSISTANT:" + else: + input_text = system + user_image + user_prompt + " ASSISTANT:" + + input_ids = tokenizer(input_text, return_tensors="pt").input_ids.cuda() + inputs_embeds = model.model.embed_tokens(input_ids) + + new_input_embeds = [] + for cur_input_ids, cur_input_embeds in zip(input_ids, inputs_embeds): + cur_image_features = image_features[cur_image_idx] + num_patches = cur_image_features.shape[0] + image_start_tokens = torch.where(cur_input_ids == 32001)[0] + for image_start_token_pos in image_start_tokens: + cur_image_features = image_features[cur_image_idx].to(device=cur_input_embeds.device) + num_patches = cur_image_features.shape[0] + if cur_input_ids[image_start_token_pos + num_patches + 1] != 32002: + raise ValueError("The image end token should follow the image start token.") + + cur_new_input_embeds = torch.cat((cur_input_embeds[:image_start_token_pos + 1], cur_image_features, + cur_input_embeds[image_start_token_pos + num_patches + 1:]), dim=0) + new_input_embeds.append(cur_new_input_embeds) + inputs_embeds = torch.stack(new_input_embeds, dim=0) + + st_time = time.time() + with torch.inference_mode(): + with torch.autocast(dtype=torch.float16, device_type='cuda'): + output_ids = model.generate(inputs_embeds=inputs_embeds.float(), **gen_kwargs) + print(f"done generated in {time.time() - st_time} seconds") + + # input_token_len = input_ids.shape[-1] + # input_text = tokenizer.batch_decode(input_ids)[0] + # response = tokenizer.batch_decode(output_ids[:, input_token_len:])[0] + response = tokenizer.batch_decode(output_ids)[0] + + # print(f"input: {input_text}") + print(f"response for {expr} in image {cur_image_idx}: {response}") + + # save response in a txt file + with open(os.path.join(result_dir, 'rec_response.txt'), 'a') as f: + f.write(f'response_{expr}_{cur_image_idx}: ') # \n') + f.write(response + '\n') + + # save result to a dict: {"0": {"umbrella": [[],[]], "carrot": [[]]}} + if str(cur_image_idx) not in rec_result: + rec_result[str(cur_image_idx)] = {} + rec_result[str(cur_image_idx)][expr] = extract_boxes(response) + # print(f"rec_result: {rec_result}") + + # save processed image (only for bbox tasks) + if save_image: + _, processed_image = postprocess(response, image=ToPILImage()(image_list[cur_image_idx][0]), width=5) + if processed_image is not None: + output_path = os.path.join(result_dir, f'{cur_image_idx}_{expr}_prompt.png') + processed_image.save(output_path) + +# save result to a json file +with open(os.path.join(result_dir, 'rec_response.json'), 'w') as f: + json.dump(rec_result, f, indent=4) \ No newline at end of file diff --git a/umbrae/model.py b/umbrae/model.py new file mode 100644 index 0000000..bd5d57a --- /dev/null +++ b/umbrae/model.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : model.py +@Time : 2024/01/14 18:25:30 +@Author : Weihao Xia +@Version : 2.0 +@Desc : None +''' + +from functools import partial + +from transformers import CLIPVisionModel +from perceiver import PerceiverResampler + +import torch +from torch import nn +from torchvision import transforms + + +class BrainEncoder(nn.Module): + def __init__(self): + super().__init__() + self.clip = CLIPVisionModel.from_pretrained('openai/clip-vit-large-patch14') + self.clip_size = (224, 224) + preproc = transforms.Compose([ + transforms.Resize(size=self.clip_size[0], interpolation=transforms.InterpolationMode.BICUBIC, antialias=True), + transforms.CenterCrop(size=self.clip_size), + # transforms.ToTensor(), # only for debug + transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)) + ]) + self.preprocess = preproc + + for param in self.clip.parameters(): + param.requires_grad = False + # param.data = param.data.half() + + self.clip_width = self.clip.vision_model.embeddings.patch_embedding.out_channels + + self.conv1 = nn.ModuleDict() + self.position_embedding = nn.ParameterDict() + self.modals = ['image', 'fmri'] + for modal in self.modals: + if modal =='image': + modal_tokens = 256 + 1 + pass + elif modal == 'fmri': + modal_tokens = 8 + 1 + self.conv1[modal] = nn.Linear(15724, 8192) + self.position_embedding[modal] = nn.Embedding(modal_tokens, self.clip_width) + + def clip_encode_image(self, x, modal='image'): + x = x.reshape(x.shape[0], x.shape[1], -1) + x = x.permute(0, 2, 1) + + x = torch.cat([self.clip.vision_model.embeddings.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, + x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) + + pos_embedding = self.clip.vision_model.embeddings.position_embedding # Embedding(257, 1024) + if modal == 'fmri': + pos_embedding = self.position_embedding[modal] + + modal_tokens = 257 if modal == 'image' else 9 + position_ids = torch.arange(0, modal_tokens).unsqueeze(0).to(x.device) + + x = x + pos_embedding(position_ids) + x = self.clip.vision_model.pre_layrnorm(x) + x = self.clip.vision_model.encoder(x, output_hidden_states=True) + + select_hidden_state_layer = -2 + select_hidden_state = x.hidden_states[select_hidden_state_layer] # torch.Size([1, 257, 1024]) + image_features = select_hidden_state[:, 1:] # torch.Size([1, 256, 1024] + + return image_features + + def encode_image(self, x, modal='image'): + if modal in ['image']: + x = self.preprocess(x) + x = self.clip.vision_model.embeddings.patch_embedding(x) # conv1, shape = [*, width, grid, grid] + elif modal == 'fmri': + x = self.conv1[modal](x) + x = x.reshape(x.size(0), self.clip_width, -1) + + image_feats = self.clip_encode_image(x, modal=modal) + + return image_feats + + +class Perceiver(nn.Module): + def __init__(self, patch_embed_dim=1024, hidden_size=4096, num_latents=256): + super().__init__() + + self.ln_vision = nn.LayerNorm(patch_embed_dim) + self.llm_proj = nn.Linear( + patch_embed_dim, hidden_size + ) + + self.perceiver = PerceiverResampler( + dim = patch_embed_dim, + dim_head = 96, + depth = 6, + heads = 16, + num_latents = num_latents, + num_media_embeds = 1 + ) + + def forward(self, image_features): + image_features = self.ln_vision(image_features) + inputs_llm = self.perceiver(image_features) + return self.llm_proj(inputs_llm) + +class BrainX(nn.Module): + def __init__(self, hidden_dim=1024, out_dim=1024, num_latents=256, use_token=False, use_norm=False, act_first=False): + super().__init__() + self.subs = [1, 2, 5, 7] + self.num_voxels = {1: 15724, 2: 14278, 3: 15226, 4: 13153, 5: 13039, 6: 17907, 7: 12682, 8: 14386} + self.use_token = use_token + + norm_func = partial(nn.LayerNorm, normalized_shape=num_latents) + act_fn = nn.GELU + act_and_norm = (act_fn, norm_func) if act_first else (norm_func, act_fn) + + self.lin1 = nn.ModuleDict() + self.lin2 = nn.ModuleDict() + self.token = nn.ParameterDict() + for sub in self.subs: + if not use_norm: + self.lin1[f'fmri{sub}'] = nn.Linear(1, num_latents) + else: + self.lin1[f'fmri{sub}'] = nn.Sequential( + nn.Linear(1, num_latents), + *[item() for item in act_and_norm], + nn.Dropout(0.5), + ) + self.lin2[f'fmri{sub}'] = nn.Linear(self.num_voxels.get(sub), hidden_dim) + + if self.use_token: + self.token[f'fmri{sub}'] = nn.Parameter( + torch.empty([1, 5, hidden_dim])) # learnable tokens dim=5 + nn.init.normal_(self.token[f'fmri{sub}'], std=0.02) + + self.perceiver = Perceiver(patch_embed_dim=hidden_dim, hidden_size=out_dim, num_latents=num_latents) + + def forward(self, x, modal='fmri1'): + x = x.unsqueeze(1) + x = x.transpose(1, 2) + x = self.lin1[modal](x) + + x = x.transpose(1, 2) + x = self.lin2[modal](x) + if self.use_token: + token = self.token[modal].repeat(x.size(0), 1, 1) + x = torch.cat([x, token], dim=1) + x = self.perceiver(x) + return x \ No newline at end of file diff --git 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