# import numpy as np import requests import torch from PIL import Image from torch.nn.functional import cosine_similarity from transformers import AutoImageProcessor, AutoModel from transformers import ViTImageProcessor, ViTModel from transformers import pipeline # import transformers # # print(transformers.__version__) # # img_urls = ["https://img0.baidu.com/it/u=3704428154,2884159591&fm=253&fmt=auto&app=138&f=JPEG?w=889&h=500", # "https://img0.baidu.com/it/u=3704428154,2884159591&fm=253&fmt=auto&app=138&f=JPEG?w=889&h=500"] # # image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB") # image_gen = Image.open(requests.get(img_urls[1], stream=True).raw).convert("RGB") # # # DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') DEVICE = torch.device('cpu') # pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-384", device=DEVICE, pool=True) # # # 1提取图片特征向量 # outputs = pipe([image_real, image_gen]) # # # get the length of a single output # print(len(outputs[0][0])) # # show outputs # print(outputs) # # # 768 # # [[[-0.03909236937761307, 0.43381670117378235, -0.06913255900144577, # # # 2计算图片相似度 # similarity_score = cosine_similarity(torch.Tensor(outputs[0]), # torch.Tensor(outputs[1]), dim=1) # # print(similarity_score) # tensor([0.6043]) # pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224", device=DEVICE) # output = pipe(image_real) # # # 其中第一个维度是批量大小，最后两个维度是嵌入形状。 # print(np.array(outputs).shape) # # (1, 197, 768) # 第二种方式推理图片相似度 # processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") # model = AutoModel.from_pretrained("google/vit-base-patch16-224").to(DEVICE) # processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") # model = AutoModel.from_pretrained("google/vit-base-patch16-224-in21k").to(DEVICE) # processor = AutoImageProcessor.from_pretrained("chanhua/autotrain-izefx-v3qh0") # model = AutoModel.from_pretrained("chanhua/autotrain-izefx-v3qh0").to(DEVICE) # processor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k') # model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k') # processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k') # model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k') # tensor([0.6061], device='cuda:0', grad_fn=) # pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-384", device=DEVICE, pool=True) # pipe = pipeline(task="image-feature-extraction", model_name="chanhua/autotrain-izefx-v3qh0", device=DEVICE, pool=True) # pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224", device=DEVICE, pool=True, revision="29e7a1e183") # 推理 def infer4(url1, url2): try: pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224-in21k", device=DEVICE, pool=True) print("进入推理") print("打开图片1") # image_real = Image.open(requests.get(url1, stream=True).raw).convert("RGB") image_real = Image.open(url1).convert('RGB') print("打开图片2") # image_gen = Image.open(requests.get(url2, stream=True).raw).convert("RGB") image_gen = Image.open(url2).convert('RGB') print("利用模型获取图片特征向量") outputs = pipe([image_real, image_gen]) print(f"得到图片特征向量计算相似度: {outputs}") similarity_score = cosine_similarity(torch.Tensor(outputs[0]), torch.Tensor(outputs[1]), dim=1) print(f"得到图片相似度: {similarity_score}") t_cpu = similarity_score.cpu() # 然后提取这个值 return t_cpu.item() except Exception as e: print(f"发生了一个错误: {e}") return 0.0 finally: # 无论是否发生异常，都会执行此代码块 print("这是finally块") # 推理 def infer2(url): processor = AutoImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k') model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k') # image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB") # image = Image.open(requests.get(url, stream=True).raw).convert("RGB") image = Image.open(url).convert('RGB') inputs = processor(images=image, return_tensors="pt").to(DEVICE) outputs = model(**inputs) # last_hidden_states = outputs.last_hidden_state return outputs.pooler_output # 计算相似度 def infer1(image1, image2): try: embed_real = infer2(image1) embed_gen = infer2(image2) similarity_score = cosine_similarity(embed_real, embed_gen, dim=1) print(similarity_score) # 如果你想在CPU上操作这个值，你需要先将tensor移动到CPU t_cpu = similarity_score.cpu() # 然后提取这个值 return t_cpu.item() except Exception as e: print(f"发生了一个错误: {e}") return 0.0 finally: # 无论是否发生异常，都会执行此代码块 print("这是finally块") # 输出图片向量 def similarity_cpu(image1, image2): try: embed_real = xl_infer(image1) embed_gen = xl_infer(image2) similarity_score = cosine_similarity(embed_real, embed_gen, dim=1) print(similarity_score) # 如果你想在CPU上操作这个值，你需要先将tensor移动到CPU t_cpu = similarity_score.cpu() # 然后提取这个值 return t_cpu.item() except Exception as e: print(f"发生了一个错误: {e}") return '异常'+ str(e) finally: # 无论是否发生异常，都会执行此代码块 print("这是finally块") # 推理 def xl_infer(url): image = url.convert('RGB') # processor = AutoImageProcessor.from_pretrained('facebook/dinov2-giant') # model = AutoModel.from_pretrained('facebook/dinov2-giant') processor = AutoImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k') # model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k') model = AutoModel.from_pretrained('google/vit-large-patch16-224-in21k') inputs = processor(images=image, return_tensors="pt").to(DEVICE) outputs = model(**inputs) return outputs.pooler_output