diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5e3ae1f --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +model/clip_model/clip-vit-base-patch32/ +out/ \ No newline at end of file diff --git a/2-sft_multi_vlm.py b/2-sft_multi_vlm.py new file mode 100644 index 0000000..0c1eb3a --- /dev/null +++ b/2-sft_multi_vlm.py @@ -0,0 +1,244 @@ +import os +import platform +import argparse +import time +import math +import warnings +import json + +import pandas as pd +import torch +import torch.nn.functional as F +import torch.distributed as dist +from contextlib import nullcontext + +from torch import optim +from torch.nn.parallel import DistributedDataParallel +from torch.utils.data import DataLoader, DistributedSampler +from transformers import AutoTokenizer, AutoModel +from model.model import Transformer +from model.LMConfig import LMConfig +from model.dataset import SFTDataset_multi +from model.vision_utils import get_vision_model, get_img_embedding + +warnings.filterwarnings('ignore') + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def Logger(content): + if not ddp or dist.get_rank() == 0: + print(content) + + +def get_lr(it, all): + warmup_iters = args.warmup_iters + lr_decay_iters = all + min_lr = args.learning_rate / 10 + + if it < warmup_iters: + return args.learning_rate * it / warmup_iters + if it > lr_decay_iters: + return min_lr + decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) + assert 0 <= decay_ratio <= 1 + coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) + return min_lr + coeff * (args.learning_rate - min_lr) + + +def train_epoch(epoch, wandb): + start_time = time.time() + for step, (X, Y, loss_mask, image_process) in enumerate(train_loader): + X = X.to(args.device) + Y = Y.to(args.device) + loss_mask = loss_mask.to(args.device) + image_process = image_process.to(args.device) + image_encoders = get_img_embedding(image_process, vision_model) # torch.Size([32, 4, 50, 768]) + lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + with ctx: + logits = model(X, Y, image_encoders=image_encoders).logits + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none') + loss_mask = loss_mask.view(-1) + loss = torch.sum(loss * loss_mask) / loss_mask.sum() + + scaler.scale(loss).backward() + + if (step + 1) % args.accumulation_steps == 0: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) + + scaler.step(optimizer) + scaler.update() + + optimizer.zero_grad(set_to_none=True) + + if step % args.log_interval == 0: + spend_time = time.time() - start_time + Logger( + 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( + epoch, + args.epochs, + step, + iter_per_epoch, + loss.item(), + optimizer.param_groups[-1]['lr'], + spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) + + if (wandb is not None) and (not ddp or dist.get_rank() == 0): + wandb.log({"loss": loss, + "lr": optimizer.param_groups[-1]['lr'], + "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) + + if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): + model.eval() + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_sft.pth' + + if isinstance(model, torch.nn.parallel.DistributedDataParallel): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + + torch.save(state_dict, ckp) + model.train() + + if args.save_last and (not ddp or dist.get_rank() == 0): + model.eval() + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_sft_multi.pth' + + if isinstance(model, torch.nn.parallel.DistributedDataParallel): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + + torch.save(state_dict, ckp) + model.train() + + +def init_model(lm_config): + tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') + moe_path = '_moe' if lm_config.use_moe else '' + # ckp = f'./out/{lm_config.dim}{moe_path}_vlm_pretrain.pth' + ckp = f'./out/{lm_config.dim}{moe_path}_vlm_sft.pth' + + model = Transformer(lm_config) + state_dict = torch.load(ckp, map_location=args.device) + + # 处理不需要的前缀 + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + + # 加载到模型中 + model.load_state_dict(state_dict, strict=False) + model = model.to(args.device) + + print(f'模型可学习参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)') + + (vision_model, preprocess) = get_vision_model() + vision_model = vision_model.to(args.device) + return model, tokenizer, (vision_model, preprocess) + + +def init_distributed_mode(): + if not ddp: return + global ddp_local_rank, DEVICE + + dist.init_process_group(backend="nccl") + ddp_rank = int(os.environ["RANK"]) + ddp_local_rank = int(os.environ["LOCAL_RANK"]) + ddp_world_size = int(os.environ["WORLD_SIZE"]) + DEVICE = f"cuda:{ddp_local_rank}" + torch.cuda.set_device(DEVICE) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="MiniMind-V-SFT") + parser.add_argument("--out_dir", type=str, default="out", help="Output directory") + parser.add_argument("--epochs", type=int, default=19, help="Number of epochs") + parser.add_argument("--batch_size", type=int, default=32, help="Batch size") + parser.add_argument("--learning_rate", type=float, default=1e-6, help="Learning rate") + parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", + help="Device to use") + parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type") + parser.add_argument("--use_wandb", default=False, action="store_true", help="Use Weights & Biases") + parser.add_argument("--wandb_project", type=str, default="MiniMind-V", help="Weights & Biases project name") + parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading") + parser.add_argument("--data_path", type=str, default="./dataset/sft_multi_images/output_cn.json", + help="Path to training data") + parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel") + parser.add_argument("--accumulation_steps", type=int, default=1, help="Gradient accumulation steps") + parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold") + parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations") + parser.add_argument("--log_interval", type=int, default=100, help="Logging interval") + parser.add_argument("--save_interval", type=int, default=100, help="Model saving interval") + parser.add_argument('--local_rank', type=int, default=-1, help='local rank for distributed training') + parser.add_argument('--save_last', type=bool, default=True, help='save last step model') + + args = parser.parse_args() + + lm_config = LMConfig() + max_seq_len = lm_config.max_seq_len + args.save_dir = os.path.join(args.out_dir) + os.makedirs(args.save_dir, exist_ok=True) + os.makedirs(args.out_dir, exist_ok=True) + tokens_per_iter = args.batch_size * max_seq_len + torch.manual_seed(1337) + device_type = "cuda" if "cuda" in args.device else "cpu" + + args.wandb_run_name = f"MiniMind-V-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" + + ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() + ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? + ddp_local_rank, DEVICE = 0, "cuda:0" + if ddp: + init_distributed_mode() + args.device = torch.device(DEVICE) + + if args.use_wandb and (not ddp or ddp_local_rank == 0): + import wandb + + wandb.init(project=args.wandb_project, name=args.wandb_run_name) + else: + wandb = None + + model, tokenizer, (vision_model, preprocess) = init_model(lm_config) + + use_version = 0 + train_ds = SFTDataset_multi(args.data_path, tokenizer, vision_model=(vision_model, preprocess), + image_special_token=lm_config.image_special_token, + max_length=max_seq_len) + train_sampler = DistributedSampler(train_ds) if ddp else None + train_loader = DataLoader( + train_ds, + batch_size=args.batch_size, + pin_memory=True, + drop_last=False, + shuffle=False, + num_workers=args.num_workers, + sampler=train_sampler + ) + + scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) + optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) + + if False and not lm_config.use_moe and platform.system() != 'Windows' and float( + torch.__version__.split('.')[0]) >= 2: + Logger("compiling the model... (takes a ~minute)") + unoptimized_model = model + model = torch.compile(model) + + if ddp: + model._ddp_params_and_buffers_to_ignore = {"pos_cis"} + model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) + + iter_per_epoch = len(train_loader) + for epoch in range(args.epochs): + train_epoch(epoch, wandb) diff --git a/3-eval_chat_multi.py b/3-eval_chat_multi.py new file mode 100644 index 0000000..f1d0346 --- /dev/null +++ b/3-eval_chat_multi.py @@ -0,0 +1,170 @@ +import os +import random +import time + +import numpy as np +import torch +import warnings + +from PIL import Image +from transformers import AutoTokenizer, AutoModelForCausalLM +from model.model import Transformer +from model.LMConfig import LMConfig +from model.vision_utils import get_vision_model, get_img_process, get_img_embedding + +warnings.filterwarnings('ignore') + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def init_model(lm_config): + tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') + model_from = 1 # 1从权重，2用transformers + + if model_from == 1: + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'./out/{lm_config.dim}{moe_path}_vlm_sft_multi.pth' + + model = Transformer(lm_config) + state_dict = torch.load(ckp, map_location=device) + + # 处理不需要的前缀 + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + + # 加载到模型中 + model.load_state_dict(state_dict, strict=False) + else: + model = AutoModelForCausalLM.from_pretrained('minimind-v-v1-small', + trust_remote_code=True) + + model = model.to(device) + + print(f'模型参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)') + + (vision_model, preprocess) = get_vision_model() + vision_model = vision_model.to(device) + return model, tokenizer, (vision_model, preprocess) + + +def setup_seed(seed): + random.seed(seed) # 设置 Python 的随机种子 + np.random.seed(seed) # 设置 NumPy 的随机种子 + torch.manual_seed(seed) # 设置 PyTorch 的随机种子 + torch.cuda.manual_seed(seed) # 为当前 GPU 设置随机种子（如果有） + torch.cuda.manual_seed_all(seed) # 为所有 GPU 设置随机种子（如果有） + torch.backends.cudnn.deterministic = True # 确保每次返回的卷积算法是确定的 + torch.backends.cudnn.benchmark = False # 关闭 cuDNN 的自动调优，避免不确定性 + + +if __name__ == "__main__": + # ----------------------------------------------------------------------------- + out_dir = 'out' + start = "" + temperature = 0.5 + top_k = 8 + setup_seed(1337) + # device = 'cpu' + device = 'cuda:0' if torch.cuda.is_available() else 'cpu' + dtype = 'bfloat16' + max_seq_len = 1 * 1024 + lm_config = LMConfig() + lm_config.max_seq_len = max_seq_len + # ----------------------------------------------------------------------------- + + model, tokenizer, (vision_model, preprocess) = init_model(lm_config) + + model = model.eval() + # 推送到huggingface + # model.push_to_hub("minimind") + # tokenizer.push_to_hub("minimind") + + stream = True + + # prompt = lm_config.image_special_token + '\n这个图片描述的是什么内容？' + + i = 0 + # 获取图像文件列表 + image_dir = './dataset/eval_multi_images/' + image_files = sorted(os.listdir(image_dir)) + + image_count = len(image_files) + special_tokens = lm_config.image_special_token * image_count # 根据图片数量重复特殊标记 + + # 构建提示 + prompt = f"{special_tokens}\n这几张图片是否是同一个类别？" + + # 遍历图像文件 + image_encoders = [] + for i, image_file in enumerate(image_files): + image_path = os.path.join(image_dir, image_file) + + # 打开图像并转换为RGB格式 + image = Image.open(image_path).convert('RGB') + image_process = get_img_process(image, preprocess).to(vision_model.device) + + # 对图像进行编码 + image_encoder = get_img_embedding(image_process, vision_model).unsqueeze(0) + print(image_encoder.shape) + image_encoders.append(image_encoder) + print(f'[Image]: {image_file}') + image_encoders = torch.cat(image_encoders, dim=0).unsqueeze(0) + print(image_encoders.shape) + + # 准备问题和消息 + prompt_ = prompt.replace("\n", "") + print(f'[Q]: {prompt_}') + + messages = [{"role": "user", "content": prompt}] + + new_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + )[-(max_seq_len - 1):] + + x = tokenizer(new_prompt).data['input_ids'] + x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...]) + + with torch.no_grad(): + res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature, + top_k=top_k, stream=stream, image_encoders=image_encoders) + + print('[A]: ', end='') + try: + y = next(res_y) + except StopIteration: + print("No answer") + exit() + + history_idx = 0 + while y is not None: + answer = tokenizer.decode(y[0].tolist()) + if answer and answer[-1] == '�': + try: + y = next(res_y) + except StopIteration: + break + continue + if not len(answer): + try: + y = next(res_y) + except StopIteration: + break + continue + + print(answer[history_idx:], end='', flush=True) + try: + y = next(res_y) + except StopIteration: + break + history_idx = len(answer) + + if not stream: + break + + print('\n') \ No newline at end of file diff --git a/dataset/eval_multi_images/两头鹿.png b/dataset/eval_multi_images/两头鹿.png new file mode 100644 index 0000000..14bc9ba Binary files /dev/null and b/dataset/eval_multi_images/两头鹿.png differ diff --git a/dataset/eval_multi_images/两朵红花.png b/dataset/eval_multi_images/两朵红花.png new file mode 100644 index 0000000..738f8d3 Binary files /dev/null and b/dataset/eval_multi_images/两朵红花.png differ diff --git a/dataset/eval_multi_images/老虎在水里.png b/dataset/eval_multi_images/老虎在水里.png new file mode 100644 index 0000000..9f52900 Binary files /dev/null and b/dataset/eval_multi_images/老虎在水里.png differ diff --git a/dataset/eval_multi_images/豹子在悬崖.png b/dataset/eval_multi_images/豹子在悬崖.png new file mode 100644 index 0000000..84c7ffa Binary files /dev/null and b/dataset/eval_multi_images/豹子在悬崖.png differ diff --git a/full.json b/full.json new file mode 100644 index 0000000..ab4ca26 --- /dev/null +++ b/full.json @@ -0,0 +1,36022 @@ +{ + "metadata": { + "dataset": "MIT-States_StateCoherence", + "split": "full", + "num_sample": 2000, + "task_instruction": [ + "I will provide you with two sets of pictures, each of which shows an object in the opposite state. Can you tell me if the states of these two sets of pictures are the same? You must choose your answer from the Choice List. ", + "I have two sets of pictures that show an object in opposite states. Can you tell me if the states of these two sets of pictures are the same? You must choose your answer from the Choice List. ", + "I will give you two sets of pictures, each of which depicts an object in its opposite state. Can you tell me if the states of these two sets of pictures are the same? You must choose your answer from the Choice List. ", + "I have two sets of pictures that depict an object in its opposite state. Can you tell me if the states of these two sets of pictures are the same? You must choose your answer from the Choice List. ", + "I will show you two sets of pictures, each showing an object in its opposite state. Can you tell me if the states of these two sets of pictures are the same? You must choose your answer from the Choice List. ", + "I have two sets of pictures that show an object in its opposite state. Can you tell me if the states of these two sets of pictures are identical? You must choose your answer from the Choice List. ", + "I will provide you with two sets of pictures, each showing an object in its opposite state. Can you tell me if the states of these two sets of pictures are identical? You must choose your answer from the Choice List. ", + "I have two sets of pictures that depict an object in its opposite state. Can you tell me if the states of these two sets of pictures are identical? You must choose your answer from the Choice List. ", + "I will show you two sets of pictures, each depicting an object in its opposite state. Can you tell me if the states of these two sets of pictures are identical? You must choose your answer from the Choice List. ", + "I have two sets of pictures that depict an object in its opposite state. Can you tell me if the states of these two sets of pictures are the same? You must choose your answer from the Choice List. " + ], + "question_type": "multi-choice" + }, + "data": [ + { + "sample_id": 0, + "task_instruction_id": 4, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "0.jpg", + "1.jpg", + "2.jpg", + "3.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "True" + }, + { + "sample_id": 1, + "task_instruction_id": 1, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "4.jpg", + "5.jpg", + "6.jpg", + "7.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "False" + }, + { + "sample_id": 2, + "task_instruction_id": 7, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "8.jpg", + "9.jpg", + "10.jpg", + "11.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "False" + }, + { + "sample_id": 3, + "task_instruction_id": 0, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ 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"{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "7984.jpg", + "7985.jpg", + "7986.jpg", + "7987.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "True" + }, + { + "sample_id": 1997, + "task_instruction_id": 7, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "7988.jpg", + "7989.jpg", + "7990.jpg", + "7991.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "True" + }, + { + "sample_id": 1998, + "task_instruction_id": 5, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "7992.jpg", + "7993.jpg", + "7994.jpg", + "7995.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "True" + }, + { + "sample_id": 1999, + "task_instruction_id": 5, + "task_instance": { + "context": "{image#1}{image#2}{image#3}{image#4}", + "images_path": [ + "7996.jpg", + "7997.jpg", + "7998.jpg", + "7999.jpg" + ], + "choice_list": [ + "True", + "False" + ] + }, + "response": "False" + } + ] +} \ No newline at end of file diff --git a/model/LMConfig.py b/model/LMConfig.py index 4a1c349..eb52cbf 100644 --- a/model/LMConfig.py +++ b/model/LMConfig.py @@ -15,7 +15,7 @@ class LMConfig(PretrainedConfig): hidden_dim: int = None, multiple_of: int = 64, norm_eps: float = 1e-5, - max_seq_len: int = 200, + max_seq_len: int = 512, dropout: float = 0.0, flash_attn: bool = True, image_special_token: str = '<' * 25 + '>' * 25, diff --git a/model/__pycache__/LMConfig.cpython-310.pyc b/model/__pycache__/LMConfig.cpython-310.pyc new file mode 100644 index 0000000..aba4920 Binary files /dev/null and b/model/__pycache__/LMConfig.cpython-310.pyc differ diff --git a/model/__pycache__/dataset.cpython-310.pyc b/model/__pycache__/dataset.cpython-310.pyc new file mode 100644 index 0000000..59d8608 Binary files /dev/null and b/model/__pycache__/dataset.cpython-310.pyc differ diff --git a/model/__pycache__/model.cpython-310.pyc b/model/__pycache__/model.cpython-310.pyc new file mode 100644 index 0000000..b66c89a Binary files /dev/null and b/model/__pycache__/model.cpython-310.pyc differ diff --git a/model/__pycache__/vision_utils.cpython-310.pyc b/model/__pycache__/vision_utils.cpython-310.pyc new file mode 100644 index 0000000..7f81526 Binary files /dev/null and b/model/__pycache__/vision_utils.cpython-310.pyc differ diff --git a/model/dataset.py b/model/dataset.py index 8b81836..dbca877 100644 --- a/model/dataset.py +++ b/model/dataset.py @@ -178,7 +178,101 @@ class SFTDataset(Dataset): image_encoders = get_img_process(image, self.preprocess) return X_tensor, Y_tensor, loss_mask_tensor, image_encoders + +class SFTDataset_multi(Dataset): + def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024, + prompt_max_len=512, + answer_max_len=256, + image_special_token='<' * 25 + '>' * 25): -if __name__ == "__main__": - pass + super().__init__() + with open(json_path, 'r', encoding='utf-8') as f: + self.data = json.load(f) + + self.max_length = max_length + self.prompt_max_len = prompt_max_len + self.answer_max_len = answer_max_len + self.tokenizer = tokenizer + self.vision_model, self.preprocess = vision_model + self.padding = 0 + self.bos_id = self.tokenizer('assistant').data['input_ids'] + self.dataset_path = './dataset/sft2_images/' + self.image_special_token = image_special_token + + def __len__(self): + return len(self.data) + + def find_sublist_index(self, main_list, sub_list) -> int: + last_index = -1 + for i in range(len(main_list) - len(sub_list) + 1): + if main_list[i:i + len(sub_list)] == sub_list: + last_index = i + return last_index + + def safe_eval(self, s): + try: + res = eval(s) + except Exception as e: + return [] + return res + + def __getitem__(self, index: int): + sample = self.data[index] + image_names = sample['image'].split(', ') + conversation = sample['conversations'] + + messages = [] + for i in range(0, 1): + if i + 1 < len(conversation): + q = conversation[i]['value'].replace('', self.image_special_token) + a = conversation[i + 1]['value'] + + if q and a: + messages.append({"role": "user", "content": q}) + messages.append({"role": "assistant", "content": a}) + + # print(messages) # [{'role': 'user', 'content': '<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\nAre the two images below that resemble each other described by the same term? You must choose your answer from the Choice List. Choice_List: True, False.'}, {'role': 'assistant', 'content': 'False'}][{'role': 'user', 'content': '<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\nDo these four pictures fall into the same category? You must choose your answer from the Choice List. Choice_List: True, False.'}, {'role': 'assistant', 'content': 'False'}] + new_prompt = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length] + # print(len(input_id)) # 165 or 263 or 259 + + question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id) + padding_len = self.max_length - len(input_id) + input_id = input_id + [self.padding] * padding_len + mask_len = len(input_id) - question_length - padding_len + loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len + + input_id = np.array(input_id) + + X = np.array(input_id[:-1]).astype(np.int64) + Y = np.array(input_id[1:]).astype(np.int64) + loss_mask = np.array(loss_mask[1:]).astype(np.int64) + + X_tensor = torch.from_numpy(X) + Y_tensor = torch.from_numpy(Y) + loss_mask_tensor = torch.from_numpy(loss_mask) + + # 读取多张图像 + image_encoders = [] + for image_name in image_names: + image = Image.open(f'{self.dataset_path}{image_name.strip()}') # 去掉可能的空格 + image_encoders.append(get_img_process(image, self.preprocess)['pixel_values']) + + # 确定目标形状 + max_images = 4 # 根据你的需求设置 一次性最大输入几张图片 + target_shape = (max_images, 3, 224, 224) + + # 创建填充张量 + padded_image_encoders = torch.zeros(target_shape, dtype=torch.float32) + + # 填充图像编码 + for i, img_enc in enumerate(image_encoders): + if i < max_images: + padded_image_encoders[i] = img_enc.squeeze(0) + + return X_tensor, Y_tensor, loss_mask_tensor, padded_image_encoders \ No newline at end of file diff --git a/model/model.py b/model/model.py index 17918e4..10c9fb0 100644 --- a/model/model.py +++ b/model/model.py @@ -372,29 +372,38 @@ class Transformer(PreTrainedModel): # 查找token中片段的索引，为了替换做准备 def find_indices(tokens, image_ids): image_ids_tensor = torch.tensor(image_ids).to(tokens.device) - return [ - [i, i + len(image_ids) - 1] - for batch_idx in range(tokens.size(0)) - for i in range(tokens.size(1) - len(image_ids) + 1) - if torch.equal(tokens[batch_idx, i:i + len(image_ids)], image_ids_tensor) - ] or None + indices = [] - image_indices = find_indices(tokens, self.image_ids) + for batch_idx in range(tokens.size(0)): + for i in range(tokens.size(1) - len(image_ids) + 1): + if torch.equal(tokens[batch_idx, i:i + len(image_ids)], image_ids_tensor): + indices.append([batch_idx, i, i + len(image_ids) - 1]) # 返回batch_idx和开始结束索引 + + return indices if indices else None + + image_indices = find_indices(tokens, self.image_ids) # [0, 4, 53], [0, 54, 103], [0, 104, 153], [0, 154, 203] or [1, 4, 53], [1, 54, 103] # 如果此时有图像编码 if image_encoders is not None: - vision_proj = self.vision_proj(image_encoders) + vision_proj = self.vision_proj(image_encoders) # torch.Size([32, 4, 50, 512]) if image_indices is not None: # 创建一个新的张量来存储拼接后的结果 new_h = [] + for i in range(h.size(0)): - before = h[i, :image_indices[i][0], :] - after = h[i, image_indices[i][1] + 1:, :] - # 拼接 before, vision_proj, after - new_h_i = torch.cat((before, vision_proj[i], after), dim=0)[:seqlen] - new_h.append(new_h_i) + current_h = h[i] + img_idx = 0 + for _, (batch_idx, start_idx, end_idx) in enumerate(image_indices): + if batch_idx == i: + # 插入vision_proj特征 + before = current_h[:start_idx, :] + after = current_h[end_idx + 1:, :] + # 拼接 before, vision_proj, after + current_h = torch.cat((before, vision_proj[i][img_idx], after), dim=0)[:seqlen] + img_idx += 1 + new_h.append(current_h) # 将所有拼接后的结果堆叠起来 - new_h = torch.stack(new_h, dim=0) + new_h = torch.stack(new_h, dim=0) # torch.Size([32, 511, 512]) return new_h return h diff --git a/model/vision_utils.py b/model/vision_utils.py index 6966985..56d70fe 100644 --- a/model/vision_utils.py +++ b/model/vision_utils.py @@ -3,6 +3,7 @@ from transformers import CLIPProcessor, CLIPModel from PIL import Image import requests import torch +import transformers warnings.filterwarnings('ignore') @@ -31,7 +32,10 @@ def get_img_embedding(batch_encoding, vision_model): embeddings.append(output.last_hidden_state) # 从 BatchEncoding 中提取图像张量 - image_tensor = batch_encoding['pixel_values'] # 假设图像张量在 'pixel_values' 键下 + if isinstance(batch_encoding, transformers.tokenization_utils_base.BatchEncoding): + image_tensor = batch_encoding['pixel_values'] + else: + image_tensor = batch_encoding # torch.Size([32, 4, 3, 224, 224]) # 如果图像张量的形状是5维，则无需添加额外维度 if len(image_tensor.shape) == 4: @@ -54,5 +58,5 @@ def get_img_embedding(batch_encoding, vision_model): hook.remove() # 拼接所有特征向量成为一个张量 - all_embeddings = torch.stack(embeddings, dim=0).squeeze() + all_embeddings = torch.stack(embeddings, dim=0).squeeze() # torch.Size([32, 4, 50, 768]) return all_embeddings diff --git a/trans_json.py b/trans_json.py new file mode 100644 index 0000000..7d580e2 --- /dev/null +++ b/trans_json.py @@ -0,0 +1,53 @@ +import json + +# 加载现有JSON数据 +with open('full.json', 'r') as f: + data = json.load(f) + +# 创建新的格式 +new_data = [] + +# 定义选择列表的翻译字典 +translation_dict = { + "True": "是的", + "False": "不是", + "painted": "涂鸦", + "ripe": "成熟" +} + +for item in data['data']: + sample_id = str(item['sample_id']).zfill(12) # 格式化为12位数字 + images = [f"" for _ in item['task_instance']['images_path']] + + # 直接从task_instance中获取choice_list + choice_list = item['task_instance']['choice_list'] + + # 判断是否需要翻译 + if all(choice in translation_dict for choice in choice_list): + choice_list = [translation_dict[choice] for choice in choice_list] + response = translation_dict[item['response']] + else: + response = item['response'] + + # 构造新的对话格式 + conversation = [ + { + "from": "human", + "value": f"{''.join(images)}\n{data['metadata']['task_instruction'][item['task_instruction_id']]} 选择列表: {', '.join(choice_list)}." + }, + { + "from": "gpt", + "value": response + } + ] + + # 添加到新数据中 + new_data.append({ + "id": sample_id, + "image": ', '.join(item['task_instance']['images_path']), + "conversations": conversation + }) + +# 保存为新的JSON格式 +with open('output.json', 'w') as f: + json.dump(new_data, f, ensure_ascii=False, indent=2)