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图像编码成特征向量

news 2026/6/30 13:32:49

siglip-so400m-patch14-384

下载地址：

推理示例：

提取特征测试代码：

图像编码成特征向量

siglip-so400m-patch14-384

名称：siglip-so400m-patch14-384
系列：CLIP（Contrastive Language–Image Pretraining）
架构：Vision Transformer (ViT)
- Patch size: 14×14
- 输入图像尺寸: 384×384
目标：将图像编码成特征向量（embedding），用于与文本 embedding 对齐。

下载地址：

https://huggingface.co/google/siglip-so400m-patch14-384

推理示例：

from PIL import Image import requests from transformers import AutoProcessor, AutoModel import torch model = AutoModel.from_pretrained("google/siglip-so400m-patch14-384") processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-384") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = ["a photo of 2 cats", "a photo of 2 dogs"] inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) logits_per_image = outputs.logits_per_image probs = torch.sigmoid(logits_per_image) # these are the probabilities print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")

提取特征测试代码：

import torch from PIL import Image import requests from io import BytesIO from transformers import AutoProcessor, AutoModel # 1. 加载本地模型和处理器 LOCAL_MODEL_PATH = "/data/lbg/models/textoon/ComfyUI/models/clip/siglip-so400m-patch14-384" LOCAL_MODEL_PATH = r"D:\data\models\siglip-so400m-patch14-384" print(f"正在从本地路径加载模型: {LOCAL_MODEL_PATH}") try: clip_processor = AutoProcessor.from_pretrained(LOCAL_MODEL_PATH) clip_model = AutoModel.from_pretrained( LOCAL_MODEL_PATH, trust_remote_code=True # SigLIP 模型可能需要此参数 ) clip_model = clip_model.vision_model print("✅ 模型加载成功") except Exception as e: print(f"❌ 加载失败: {e}") exit(1) clip_model.eval() clip_model.requires_grad_(False) clip_model.to("cuda") local_image_path ="D:\soft\mm03.png" test_image = Image.open(local_image_path).convert("RGB") try: inputs = clip_processor( images=test_image, return_tensors="pt", padding=True ) pixel_values = inputs.pixel_values.to("cuda") print(f"✅ 图像预处理成功，输入形状: {pixel_values.shape}") except Exception as e: print(f"❌ 预处理失败: {e}") exit(1) DEVICE = "cuda" target_hidden_state = [] # 定义钩子函数 def hook_fn(module, input, output): # output 通常是 (hidden_state,) 元组 target_hidden_state.append(output[0] if isinstance(output, tuple) else output) # 注册钩子到倒数第二层编码器层 # 假设 clip_model 是 vision_model，且其编码器有 layers 属性 target_layer = clip_model.encoder.layers[-2] # 获取倒数第二层 hook_handle = target_layer.register_forward_hook(hook_fn) with torch.amp.autocast_mode.autocast('cuda', enabled=True): # 正常前向传播，钩子会自动捕获目标层输出 vision_outputs = clip_model(pixel_values=pixel_values) # 从列表中获取捕获的特征 if target_hidden_state: image_features = target_hidden_state[0] # 这就是倒数第二层的输出 else: # 备选方案 image_features = vision_outputs.last_hidden_state # 非常重要：移除钩子，避免内存泄漏 hook_handle.remove() if 0: try: with torch.amp.autocast_mode.autocast(DEVICE, enabled=True): # 执行前向传播 vision_outputs = clip_model( pixel_values=pixel_values, output_hidden_states=True ) if vision_outputs.hidden_states is None: print('vision_outputs.hidden_states is None') exit(52) image_features = vision_outputs.last_hidden_state[-2] last_hidden_state = vision_outputs.last_hidden_state print("✅ 推理成功完成!") print(f" 图像特征形状 (倒数第二层): {image_features.shape}") print(f" 最后隐藏状态形状: {last_hidden_state.shape}") print(f" 特征数据类型: {image_features.dtype}") print(f" 特征值范围: [{image_features.min():.4f}, {image_features.max():.4f}]") # 验证输出是否合理 if torch.isnan(image_features).any(): print("⚠️ 警告: 输出中包含NaN值!") else: print("✅ 输出检查: 无NaN值") except torch.cuda.OutOfMemoryError: print("❌ CUDA内存不足! 尝试减小批处理大小或图像尺寸") except RuntimeError as e: print(f"❌ 运行时错误: {e}") except Exception as e: print(f"❌ 推理过程中出错: {e}") # 6. 附加测试：计算相似度（可选） def test_similarity(features): """测试特征向量的相似度计算""" try: # 归一化特征 (dim=-1 在最后一个维度，即特征维度1152上归一化) normalized_features = torch.nn.functional.normalize(features, dim=-1) # 计算相似度矩阵 similarity = torch.mm(normalized_features, normalized_features.t()) print(f"\n相似度矩阵形状: {similarity.shape}") print(f"相似度矩阵:\n{similarity}") # 检查对角线是否接近1.0（对于归一化后的向量，自己与自己的点积应为1） diag_values = similarity.diag() print(f"对角线值 (应接近1.0): {diag_values}") # 验证：对角线元素是否都非常接近1（允许微小浮点误差） if torch.allclose(diag_values, torch.ones_like(diag_values), rtol=1e-3): print("✅ 自相似度测试通过 (对角线≈1)") else: print("⚠️ 自相似度测试未通过") return True except Exception as e: print(f"相似度测试失败: {e}") return False # 执行相似度测试（使用倒数第二层特征） print("\n执行附加测试...") test_similarity(image_features[:, 0, :]) # 使用[CLS] token特征 # 7. 清理和总结 torch.cuda.empty_cache() print(f"\n{'=' * 50}") print("测试总结:") print(f" 模型路径: {LOCAL_MODEL_PATH}") print(f" 设备: {DEVICE}") print(f" 输入尺寸: {pixel_values.shape}") print(f" 输出尺寸: {image_features.shape}") print(f" 模型参数量: {sum(p.numel() for p in clip_model.parameters()):,}") print("=" * 50)

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