import os os.environ["TOKENIZERS_PARALLELISM"] = "true" from PIL import Image from tqdm import tqdm import numpy as np import torch import wandb from models import Showo, MAGVITv2 from prompting_utils import UniversalPrompting, create_attention_mask_for_mmu, create_attention_mask_for_mmu_vit from training.utils import get_config, flatten_omega_conf, image_transform from transformers import AutoTokenizer from models.clip_encoder import CLIPVisionTower from transformers import CLIPImageProcessor # import.training.conversation as conversation_lib from training import conversation as conversation_lib conversation_lib.default_conversation = conversation_lib.conv_templates["phi1.5"] SYSTEM_PROMPT = "A chat between a curious user and an artificial intelligence assistant. " \ "The assistant gives helpful, detailed, and polite answers to the user's questions." SYSTEM_PROMPT_LEN = 28 def get_vq_model_class(model_type): if model_type == "magvitv2": return MAGVITv2 else: raise ValueError(f"model_type {model_type} not supported.") if __name__ == '__main__': config = get_config() resume_wandb_run = config.wandb.resume run_id = config.wandb.get("run_id", None) if run_id is None: resume_wandb_run = False run_id = wandb.util.generate_id() config.wandb.run_id = run_id wandb_config = {k: v for k, v in flatten_omega_conf(config, resolve=True)} wandb.init( project="demo", name=config.experiment.name + '_mmu', config=wandb_config, ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = AutoTokenizer.from_pretrained(config.model.showo.llm_model_path, padding_side="left") uni_prompting = UniversalPrompting(tokenizer, max_text_len=config.dataset.preprocessing.max_seq_length, special_tokens=("<|soi|>", "<|eoi|>", "<|sov|>", "<|eov|>", "<|t2i|>", "<|mmu|>", "<|t2v|>", "<|v2v|>", "<|lvg|>"), ignore_id=-100, cond_dropout_prob=config.training.cond_dropout_prob) vq_model = get_vq_model_class(config.model.vq_model.type) vq_model = vq_model.from_pretrained(config.model.vq_model.vq_model_name).to(device) vq_model.requires_grad_(False) vq_model.eval() vision_tower_name = "openai/clip-vit-large-patch14-336" vision_tower = CLIPVisionTower(vision_tower_name).to(device) clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower_name) model = Showo.from_pretrained(config.model.showo.pretrained_model_path).to(device) model.eval() temperature = 0.8 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions top_k = 1 # retain only the top_k most likely tokens, clamp others to have 0 probability file_list = os.listdir(config.mmu_image_root) responses = ['' for i in range(len(file_list))] images = [] config.question = config.question.split(' *** ') for i, file_name in enumerate(tqdm(file_list)): image_path = os.path.join(config.mmu_image_root, file_name) image_ori = Image.open(image_path).convert("RGB") image = image_transform(image_ori, resolution=config.dataset.params.resolution).to(device) image = image.unsqueeze(0) images.append(image) pixel_values = clip_image_processor.preprocess(image_ori, return_tensors="pt")["pixel_values"][0] image_tokens = vq_model.get_code(image) + len(uni_prompting.text_tokenizer) batch_size = 1 for question in config.question: if config.model.showo.w_clip_vit: conv = conversation_lib.default_conversation.copy() conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt_question = conv.get_prompt() question_input = [] question_input.append(prompt_question.strip()) input_ids_system = [uni_prompting.text_tokenizer(SYSTEM_PROMPT, return_tensors="pt", padding="longest").input_ids for _ in range(batch_size)] input_ids_system = torch.stack(input_ids_system, dim=0) assert input_ids_system.shape[-1] == 28 input_ids_system = input_ids_system.to(device) input_ids_system = input_ids_system[0] input_ids = [uni_prompting.text_tokenizer(prompt, return_tensors="pt", padding="longest").input_ids for prompt in question_input] input_ids = torch.stack(input_ids) input_ids = torch.nn.utils.rnn.pad_sequence( input_ids, batch_first=True, padding_value=uni_prompting.text_tokenizer.pad_token_id ) input_ids = torch.tensor(input_ids).to(device).squeeze(0) # import pdb; pdb.set_trace() input_ids_llava = torch.cat([ (torch.ones(input_ids.shape[0], 1) *uni_prompting.sptids_dict['<|mmu|>']).to(device), input_ids_system, (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|soi|>']).to(device), # place your img embedding here (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|eoi|>']).to(device), input_ids, ], dim=1).long() images_embeddings = vision_tower(pixel_values[None]) images_embeddings = model.mm_projector(images_embeddings) text_embeddings = model.showo.model.embed_tokens(input_ids_llava) # Full input seq part1 = text_embeddings[:, :2 + SYSTEM_PROMPT_LEN, :] part2 = text_embeddings[:, 2 + SYSTEM_PROMPT_LEN:, :] input_embeddings = torch.cat((part1, images_embeddings, part2), dim=1) attention_mask_llava = create_attention_mask_for_mmu_vit(input_embeddings, system_prompt_len=SYSTEM_PROMPT_LEN) cont_toks_list = model.mmu_generate(input_embeddings=input_embeddings, attention_mask=attention_mask_llava[0].unsqueeze(0), max_new_tokens=100, top_k=top_k, eot_token=uni_prompting.sptids_dict['<|eot|>'] ) else: input_ids = uni_prompting.text_tokenizer(['USER: \n' + question + ' ASSISTANT:'])[ 'input_ids'] input_ids = torch.tensor(input_ids).to(device) input_ids = torch.cat([ (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|mmu|>']).to(device), (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|soi|>']).to(device), image_tokens, (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|eoi|>']).to(device), (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|sot|>']).to(device), input_ids ], dim=1).long() attention_mask = create_attention_mask_for_mmu(input_ids.to(device), eoi_id=int(uni_prompting.sptids_dict['<|eoi|>'])) cont_toks_list = model.mmu_generate(input_ids, attention_mask=attention_mask, max_new_tokens=100, top_k=top_k, eot_token=uni_prompting.sptids_dict['<|eot|>']) cont_toks_list = torch.stack(cont_toks_list).squeeze()[None] text = uni_prompting.text_tokenizer.batch_decode(cont_toks_list, skip_special_tokens=True) print(text) responses[i] += f'User: ' + question + f'\n Answer : ' + text[0] + '\n' images = torch.cat(images, dim=0) images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0) images *= 255.0 images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8) pil_images = [Image.fromarray(image) for image in images] wandb_images = [wandb.Image(image, caption=responses[i]) for i, image in enumerate(pil_images)] wandb.log({"multimodal understanding": wandb_images}, step=0)