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import json
import os
# 定义配置参数
config_data = {
"hidden_size": 768,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"intermediate_size": 3072,
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"image_size": 224,
"image_channels": 3,
"patch_size": 16,
"max_position_embeddings": 512,
"vocab_size": 30522,
"type_vocab_size": 2,
"audio_sample_rate": 16000,
"audio_frame_size": 1024,
"audio_hop_size": 512,
"enable_vqa": True,
"enable_caption": True,
"enable_retrieval": True,
"enable_asr": True,
"enable_realtime_asr": True,
"batch_size": 32,
"learning_rate": 0.0001,
"weight_decay": 0.01,
"warmup_steps": 10000,
"max_steps": 100000
}
# 文件路径
config_path = r"C:\Users\baby7\Desktop\zero_sg-pytorch-zero-v4\config.json"
# 保存配置文件
os.makedirs(os.path.dirname(config_path), exist_ok=True)
with open(config_path, "w") as f:
json.dump(config_data, f, indent=4)
print(f"配置文件已保存到: {config_path}")
from transformers import BertTokenizer
import os
# 初始化分词器
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
# 保存分词器到目标路径
tokenizer_path = r"C:\Users\baby7\Desktop\zero_sg-pytorch-zero-v4\tokenizer"
os.makedirs(tokenizer_path, exist_ok=True)
tokenizer.save_pretrained(tokenizer_path)
print(f"分词器已保存到: {tokenizer_path}")
#### **加载配置文件**
from model import Config # 假设您有Config类
config_file = r"C:\Users\baby7\Desktop\zero_sg-pytorch-zero-v4\config.json"
config = Config(config_file)
print("加载的配置: ", config.__dict__)
from transformers import BertTokenizer
tokenizer_path = r"C:\Users\baby7\Desktop\zero_sg-pytorch-zero-v4\tokenizer"
tokenizer = BertTokenizer.from_pretrained(tokenizer_path)
text = "Hello, how are you?"
encoded_input = tokenizer(text, return_tensors="pt", max_length=512, padding="max_length", truncation=True)
print("分词器输出: ", encoded_input["input_ids"].shape)
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