github
/
minimind-v
mirror of https://github.com/jingyaogong/minimind-v.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[feat] update eval-vlm

pull/107/head
jingyaogong 2025-10-26 20:54:37 +08:00
parent aebc6fb191
commit 54fcbb04c8
1 changed files with 58 additions and 120 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

162
eval_vlm.py
View File

@ -1,135 +1,73 @@
import argparse import argparse
import os import os
import random
import numpy as np
import torch
import warnings import warnings
import torch
from PIL import Image from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
from model.model_vlm import MiniMindVLM, VLMConfig from model.model_vlm import MiniMindVLM, VLMConfig
from trainer.trainer_utils import setup_seed
warnings.filterwarnings('ignore') warnings.filterwarnings('ignore')
def init_model(args):
def count_parameters(model): tokenizer = AutoTokenizer.from_pretrained(args.load_from)
return sum(p.numel() for p in model.parameters() if p.requires_grad) if args.load_from == 'model':
moe_suffix = '_moe' if args.use_moe else ''
ckp = f'./{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth'
def init_model(lm_config, device): model = MiniMindVLM(
if args.load == 0: VLMConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe),
tokenizer = AutoTokenizer.from_pretrained('./model') vision_model_path="./model/vision_model/clip-vit-base-patch16"
moe_path = '_moe' if args.use_moe else '' )
modes = {0: 'pretrain_vlm', 1: 'sft_vlm', 2: 'sft_vlm_multi'} state_dict = torch.load(ckp, map_location=args.device)
ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.hidden_size}{moe_path}.pth'
model = MiniMindVLM(lm_config, vision_model_path="./model/vision_model/clip-vit-base-patch16")
state_dict = torch.load(ckp, map_location=device)
model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=False) model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=False)
else: else:
transformers_model_path = 'MiniMind2-Small-V' model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
model.vision_encoder, model.processor = MiniMindVLM.get_vision_model("./model/vision_model/clip-vit-base-patch16") model.vision_encoder, model.processor = MiniMindVLM.get_vision_model("./model/vision_model/clip-vit-base-patch16")
print(f'VLM参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') print(f'VLM模型参数: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f} M(illion)')
preprocess = model.processor
vision_model, preprocess = model.vision_encoder, model.processor return model.eval().to(args.device), tokenizer, preprocess
return model.eval().to(device), tokenizer, vision_model.eval().to(device), preprocess
def setup_seed(seed): def main():
random.seed(seed) parser = argparse.ArgumentParser(description="MiniMind-V Chat")
np.random.seed(seed) parser.add_argument('--load_from', default='model', type=str, help="模型加载路径（model=原生torch权重，其他路径=transformers格式）")
torch.manual_seed(seed) parser.add_argument('--save_dir', default='out', type=str, help="模型权重目录")
torch.cuda.manual_seed(seed) parser.add_argument('--weight', default='sft_vlm', type=str, help="权重名称前缀（pretrain_vlm, sft_vlm）")
torch.cuda.manual_seed_all(seed) parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度（512=Small-26M, 768=Base-104M）")
torch.backends.cudnn.deterministic = True parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量（Small=8, Base=16）")
torch.backends.cudnn.benchmark = False parser.add_argument('--use_moe', default=False, type=bool, help="是否使用MoE架构")
parser.add_argument('--max_new_tokens', default=512, type=int, help="最大生成长度")
parser.add_argument('--temperature', default=0.65, type=float, help="生成温度，控制随机性（0-1，越大越随机）")
if __name__ == "__main__": parser.add_argument('--top_p', default=0.85, type=float, help="nucleus采样阈值（0-1）")
parser = argparse.ArgumentParser(description="Chat with MiniMind") parser.add_argument('--image_dir', default='./dataset/eval_images/', type=str, help="测试图像目录")
parser.add_argument('--lora_name', default='None', type=str) parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备")
parser.add_argument('--out_dir', default='out', type=str)
parser.add_argument('--temperature', default=0.65, type=float)
parser.add_argument('--top_p', default=0.85, type=float)
parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str)
# MiniMind2-Small (26M)：(hidden_size=512, num_hidden_layers=8)
# MiniMind2 (104M)：(hidden_size=768, num_hidden_layers=16)
parser.add_argument('--hidden_size', default=512, type=int)
parser.add_argument('--num_hidden_layers', default=8, type=int)
parser.add_argument('--max_seq_len', default=8192, type=int)
parser.add_argument('--use_moe', default=False, type=bool)
# 默认单图推理，设置为2为多图推理
parser.add_argument('--use_multi', default=1, type=int)
parser.add_argument('--stream', default=True, type=bool)
parser.add_argument('--load', default=1, type=int, help="0: 原生torch权重，1: transformers加载")
parser.add_argument('--model_mode', default=1, type=int,
help="0: Pretrain模型，1: SFT模型，2: SFT-多图模型 (beta拓展)")
args = parser.parse_args() args = parser.parse_args()
lm_config = VLMConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, model, tokenizer, preprocess = init_model(args)
max_seq_len=args.max_seq_len, use_moe=args.use_moe)
model, tokenizer, vision_model, preprocess = init_model(lm_config, args.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
# 自动测试image_dir中的所有图像
prompt = "仔细看一下这张图：\n\n<image>\n\n描述一下这个图像的内容。"
for image_file in sorted(os.listdir(args.image_dir)):
if image_file.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
setup_seed(2026) # or setup_seed(random.randint(1, 10000))
image_path = os.path.join(args.image_dir, image_file)
image = Image.open(image_path).convert('RGB')
pixel_values = MiniMindVLM.image2tensor(image, preprocess).to(args.device).unsqueeze(0)
messages = [{"role": "user", "content": prompt.replace('<image>', model.params.image_special_token)}]
inputs_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(inputs_text, return_tensors="pt", truncation=True).to(args.device)
def chat_with_vlm(prompt, pixel_values, image_names): print(f'[图像]: {image_file}')
messages = [{"role": "user", "content": prompt}] print(f'👶: {prompt.replace('\n', '\\n')}')
new_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)[-args.max_seq_len + 1:]
inputs = tokenizer(
new_prompt,
return_tensors="pt",
truncation=True
).to(args.device)
print(f'[Image]: {image_names}')
print('🤖️: ', end='') print('🤖️: ', end='')
generated_ids = model.generate( model.generate(
inputs["input_ids"], inputs=inputs["input_ids"], attention_mask=inputs["attention_mask"],
max_new_tokens=args.max_seq_len, max_new_tokens=args.max_new_tokens, do_sample=True, streamer=streamer,
num_return_sequences=1, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id,
do_sample=True, top_p=args.top_p, temperature=args.temperature, pixel_values=pixel_values
attention_mask=inputs["attention_mask"],
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer,
top_p=args.top_p,
temperature=args.temperature,
pixel_values=pixel_values
) )
response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
messages.append({"role": "assistant", "content": response})
print('\n\n') print('\n\n')
if __name__ == "__main__":
# 单图推理：每1个图像单独推理 main()
if args.use_multi == 1:
image_dir = './dataset/eval_images/'
prompt = f"{model.params.image_special_token}\n描述一下这个图像的内容。"
for image_file in os.listdir(image_dir):
image = Image.open(os.path.join(image_dir, image_file)).convert('RGB')
pixel_tensors = MiniMindVLM.image2tensor(image, preprocess).to(args.device).unsqueeze(0)
chat_with_vlm(prompt, pixel_tensors, image_file)
# 2图推理：目录下的两个图像编码，一次性推理（power by ）
if args.use_multi == 2:
args.model_mode = 2
image_dir = './dataset/eval_multi_images/bird/'
prompt = (f"{lm_config.image_special_token}\n"
f"{lm_config.image_special_token}\n"
f"比较一下两张图像的异同点。")
pixel_tensors_multi = []
for image_file in os.listdir(image_dir):
image = Image.open(os.path.join(image_dir, image_file)).convert('RGB')
pixel_tensors_multi.append(MiniMindVLM.image2tensor(image, preprocess))
pixel_tensors = torch.cat(pixel_tensors_multi, dim=0).to(args.device).unsqueeze(0)
# 同样内容重复10次
for _ in range(10):
chat_with_vlm(prompt, pixel_tensors, (', '.join(os.listdir(image_dir))))