Update README.md

README.md

@@ -99,229 +99,7 @@ Hugging Face Hub 本身不能自动运行上传的模型，但通过 `Spaces`
 通过这些方式，您可以让模型仓库既支持在线运行，也便于用户离线部署。
 
 ## Uses
-```python
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import os
-# 配置类定义
-class Config:
-    def __init__(self):
-        # 模型架构参数
-        self.hidden_size = 768
-        self.num_attention_heads = 12
-        self.num_hidden_layers = 12
-        self.intermediate_size = 3072
-        self.hidden_dropout_prob = 0.1
-        self.attention_probs_dropout_prob = 0.1
-
-        # 图像相关
-        self.image_size = 224
-        self.image_channels = 3
-        self.patch_size = 16
-
-        # 文本相关
-        self.max_position_embeddings = 512
-        self.vocab_size = 30522
-        self.type_vocab_size = 2
-
-        # 语音相关
-        self.audio_sample_rate = 16000
-        self.audio_frame_size = 1024
-        self.audio_hop_size = 512
-
-        # 任务相关
-        self.enable_vqa = True
-        self.enable_caption = True
-        self.enable_retrieval = True
-        self.enable_asr = True  # 语音识别
-        self.enable_realtime_asr = True  # 实时语音识别
-
-        # 训练相关
-        self.batch_size = 32
-        self.learning_rate = 1e-4
-        self.weight_decay = 0.01
-        self.warmup_steps = 10000
-        self.max_steps = 100000
-
-# 模型相关类定义
-class ImageEncoder(nn.Module):
-    def __init__(self, config):
-        super(ImageEncoder, self).__init__()
-        self.config = config
-        self.encoder_layer = nn.Sequential(
-            nn.Conv2d(3, 64, kernel_size=3),
-            nn.ReLU(),
-            nn.MaxPool2d(2, 2),
-            nn.Flatten(),
-            nn.Linear(64 * 111 * 111, config.hidden_size)
-        )
-
-    def forward(self, image):
-        image_features = self.encoder_layer(image)
-        return image_features
-
-class TextEncoder(nn.Module):
-    def __init__(self, config):
-        super(TextEncoder, self).__init__()
-        self.config = config
-        self.transformer_layer = nn.TransformerEncoderLayer(
-            d_model=config.hidden_size, 
-            nhead=config.num_attention_heads,
-            batch_first=True
-        )
-        self.transformer_encoder = nn.TransformerEncoder(
-            self.transformer_layer, 
-            num_layers=config.num_hidden_layers
-        )
-
-    def forward(self, text):
-        text_features = self.transformer_encoder(text).mean(dim=1)
-        return text_features
-
-class AudioEncoder(nn.Module):
-    def __init__(self, config):
-        super(AudioEncoder, self).__init__()
-        self.config = config
-        self.encoder_layer = nn.Sequential(
-            nn.Linear(config.audio_sample_rate, config.hidden_size),
-            nn.ReLU(),
-            nn.Linear(config.hidden_size, config.hidden_size)
-        )
-
-    def forward(self, audio):
-        audio_features = self.encoder_layer(audio)
-        return audio_features
-
-class FusionLayer(nn.Module):
-    def __init__(self, config):
-        super(FusionLayer, self).__init__()
-        self.config = config
-        self.fusion_layer = nn.Linear(config.hidden_size * 3, config.hidden_size)
-
-    def forward(self, image_features, text_features, audio_features):
-        fused_features = torch.cat((image_features, text_features, audio_features), dim=1)
-        fused_features = self.fusion_layer(fused_features)
-        return fused_features
-
-class VQALayer(nn.Module):
-    def __init__(self, config):
-        super(VQALayer, self).__init__()
-        self.config = config
-        self.vqa_layer = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(self, fused_features):
-        vqa_output = self.vqa_layer(fused_features)
-        return vqa_output
-
-class CaptionLayer(nn.Module):
-    def __init__(self, config):
-        super(CaptionLayer, self).__init__()
-        self.config = config
-        self.caption_layer = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(self, fused_features):
-        caption_output = self.caption_layer(fused_features)
-        return caption_output
-
-class RetrievalLayer(nn.Module):
-    def __init__(self, config):
-        super(RetrievalLayer, self).__init__()
-        self.config = config
-        self.retrieval_layer = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(self, fused_features):
-        retrieval_output = self.retrieval_layer(fused_features)
-        return retrieval_output
-
-class ASRLayer(nn.Module):
-    def __init__(self, config):
-        super(ASRLayer, self).__init__()
-        self.config = config
-        self.asr_layer = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(self, fused_features):
-        asr_output = self.asr_layer(fused_features)
-        return asr_output
-
-class RealtimeASRLayer(nn.Module):
-    def __init__(self, config):
-        super(RealtimeASRLayer, self).__init__()
-        self.config = config
-        self.realtime_asr_layer = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(self, fused_features):
-        realtime_asr_output = self.realtime_asr_layer(fused_features)
-        return realtime_asr_output
-
-class TextOutputLayer(nn.Module):
-    def __init__(self, config):
-        super(TextOutputLayer, self).__init__()
-        self.config = config
-        self.text_output_layer = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(self, fused_features):
-        text_output = self.text_output_layer(fused_features)
-        return text_output
-
-# 主模型定义
-class AutoModel(nn.Module):
-    def __init__(self, config):
-        super(AutoModel, self).__init__()
-        self.config = config
-        self.image_encoder = ImageEncoder(config)
-        self.text_encoder = TextEncoder(config)
-        self.audio_encoder = AudioEncoder(config)
-        self.fusion_layer = FusionLayer(config)
-        self.vqa_layer = VQALayer(config)
-        self.caption_layer = CaptionLayer(config)
-        self.retrieval_layer = RetrievalLayer(config)
-        self.asr_layer = ASRLayer(config)
-        self.realtime_asr_layer = RealtimeASRLayer(config)
-        self.text_output_layer = TextOutputLayer(config)
-
-    def forward(self, image, text, audio):
-        image_features = self.image_encoder(image)
-        text_features = self.text_encoder(text)
-        audio_features = self.audio_encoder(audio)
-        fused_features = self.fusion_layer(image_features, text_features, audio_features)
-        vqa_output = self.vqa_layer(fused_features)
-        caption_output = self.caption_layer(fused_features)
-        retrieval_output = self.retrieval_layer(fused_features)
-        asr_output = self.asr_layer(fused_features)
-        realtime_asr_output = self.realtime_asr_layer(fused_features)
-        text_output = self.text_output_layer(fused_features)
-        return vqa_output, caption_output, retrieval_output, asr_output, realtime_asr_output, text_output
-
-# 测试代码
-config = Config()
-model = AutoModel(config)
-image = torch.randn(1, 3, 224, 224)
-text = torch.randn(1, config.max_position_embeddings, config.hidden_size)
-audio = torch.randn(1, config.audio_sample_rate)
-vqa_output, caption_output, retrieval_output, asr_output, realtime_asr_output, text_output = model(image, text, audio)
-
-# 输出结果
-print("VQA output shape:", vqa_output.shape)
-print("Caption output shape:", caption_output.shape)
-print("Retrieval output shape:", retrieval_output.shape)
-print("ASR output shape:", asr_output.shape)
-print("Realtime ASR output shape:", realtime_asr_output.shape)
-print("Text output shape:", text_output.shape)
-
-# 打印总参数数量
-total_params = sum(p.numel() for p in model.parameters())
-print(f"\n总参数数量: {total_params}")
-
-
-
-# 保存模型权重
-save_path = "save.pth"
-torch.save(model.state_dict(), save_path)
-print(f"模型权重已保存到: {save_path}")
-
-
-```
+
 
 ### Direct Use
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->