importtorchfromPILimportImagefromtransformersimportAutoModel,CLIPImageProcessormodel=AutoModel.from_pretrained('OpenGVLab/InternViT-6B-224px',torch_dtype=torch.bfloat16,low_cpu_mem_usage=True,trust_remote_code=True).cuda().eval()image=Image.open('./examples/image1.jpg').convert('RGB')image_processor=CLIPImageProcessor.from_pretrained('OpenGVLab/InternViT-6B-224px')pixel_values=image_processor(images=image,return_tensors='pt').pixel_valuespixel_values=pixel_values.to(torch.bfloat16).cuda()outputs=model(pixel_values)

importtorchfromPILimportImagefromtransformersimportAutoModel,CLIPImageProcessorfromtransformersimportAutoTokenizermodel=AutoModel.from_pretrained('OpenGVLab/InternVL-14B-224px',torch_dtype=torch.bfloat16,low_cpu_mem_usage=True,trust_remote_code=True).cuda().eval()image_processor=CLIPImageProcessor.from_pretrained('OpenGVLab/InternVL-14B-224px')tokenizer=AutoTokenizer.from_pretrained('OpenGVLab/InternVL-14B-224px',use_fast=False,add_eos_token=True)tokenizer.pad_token_id=0# set pad_token_id to 0images= [Image.open('./examples/image1.jpg').convert('RGB'),Image.open('./examples/image2.jpg').convert('RGB'),Image.open('./examples/image3.jpg').convert('RGB')]prefix='summarize:'texts= [prefix+'a photo of a red panda',# Englishprefix+'一张熊猫的照片',# Chineseprefix+'二匹の猫の写真'# Japanese]pixel_values=image_processor(images=images,return_tensors='pt').pixel_valuespixel_values=pixel_values.to(torch.bfloat16).cuda()input_ids=tokenizer(texts,return_tensors='pt',max_length=80,truncation=True,padding='max_length').input_ids.cuda()# InternVL-Clogits_per_image,logits_per_text=model(image=pixel_values,text=input_ids,mode='InternVL-C')probs=logits_per_image.softmax(dim=-1)# tensor([[9.9609e-01, 5.2185e-03, 6.0070e-08],#         [2.2949e-02, 9.7656e-01, 5.9903e-06],#         [3.2932e-06, 7.4863e-05, 1.0000e+00]], device='cuda:0',#        dtype=torch.bfloat16, grad_fn=<SoftmaxBackward0>)# InternVL-Glogits_per_image,logits_per_text=model(image=pixel_values,text=input_ids,mode='InternVL-G')probs=logits_per_image.softmax(dim=-1)# tensor([[9.9609e-01, 3.1738e-03, 3.6322e-08],#         [8.6060e-03, 9.9219e-01, 2.8759e-06],#         [1.7583e-06, 3.1233e-05, 1.0000e+00]], device='cuda:0',#        dtype=torch.bfloat16, grad_fn=<SoftmaxBackward0>)# please set add_eos_token to False for generationtokenizer.add_eos_token=Falseimage=Image.open('./examples/image1.jpg').convert('RGB')pixel_values=image_processor(images=image,return_tensors='pt').pixel_valuespixel_values=pixel_values.to(torch.bfloat16).cuda()tokenized=tokenizer("English caption:",return_tensors='pt')pred=model.generate(pixel_values=pixel_values,input_ids=tokenized.input_ids.cuda(),attention_mask=tokenized.attention_mask.cuda(),num_beams=5,min_new_tokens=8,)caption=tokenizer.decode(pred[0].cpu(),skip_special_tokens=True).strip()# English caption: a red panda sitting on top of a wooden platform

fromtransformersimportAutoTokenizer,AutoModelimporttorchimporttorchvision.transformsasTfromPILimportImagefromtorchvision.transforms.functionalimportInterpolationModeIMAGENET_MEAN= (0.485,0.456,0.406)IMAGENET_STD= (0.229,0.224,0.225)defbuild_transform(input_size):MEAN,STD=IMAGENET_MEAN,IMAGENET_STDtransform=T.Compose([T.Lambda(lambdaimg:img.convert('RGB')ifimg.mode!='RGB'elseimg),T.Resize((input_size,input_size),interpolation=InterpolationMode.BICUBIC),T.ToTensor(),T.Normalize(mean=MEAN,std=STD)    ])returntransformdeffind_closest_aspect_ratio(aspect_ratio,target_ratios,width,height,image_size):best_ratio_diff=float('inf')best_ratio= (1,1)area=width*heightforratiointarget_ratios:target_aspect_ratio=ratio[0]/ratio[1]ratio_diff=abs(aspect_ratio-target_aspect_ratio)ifratio_diff<best_ratio_diff:best_ratio_diff=ratio_diffbest_ratio=ratioelifratio_diff==best_ratio_diff:ifarea>0.5*image_size*image_size*ratio[0]*ratio[1]:best_ratio=ratioreturnbest_ratiodefdynamic_preprocess(image,min_num=1,max_num=6,image_size=448,use_thumbnail=False):orig_width,orig_height=image.sizeaspect_ratio=orig_width/orig_height# calculate the existing image aspect ratiotarget_ratios=set(        (i,j)forninrange(min_num,max_num+1)foriinrange(1,n+1)forjinrange(1,n+1)ifi*j<=max_numandi*j>=min_num)target_ratios=sorted(target_ratios,key=lambdax:x[0]*x[1])# find the closest aspect ratio to the targettarget_aspect_ratio=find_closest_aspect_ratio(aspect_ratio,target_ratios,orig_width,orig_height,image_size)# calculate the target width and heighttarget_width=image_size*target_aspect_ratio[0]target_height=image_size*target_aspect_ratio[1]blocks=target_aspect_ratio[0]*target_aspect_ratio[1]# resize the imageresized_img=image.resize((target_width,target_height))processed_images= []foriinrange(blocks):box= (            (i% (target_width//image_size))*image_size,            (i// (target_width//image_size))*image_size,            ((i% (target_width//image_size))+1)*image_size,            ((i// (target_width//image_size))+1)*image_size        )# split the imagesplit_img=resized_img.crop(box)processed_images.append(split_img)assertlen(processed_images)==blocksifuse_thumbnailandlen(processed_images)!=1:thumbnail_img=image.resize((image_size,image_size))processed_images.append(thumbnail_img)returnprocessed_imagesdefload_image(image_file,input_size=448,max_num=6):image=Image.open(image_file).convert('RGB')transform=build_transform(input_size=input_size)images=dynamic_preprocess(image,image_size=input_size,use_thumbnail=True,max_num=max_num)pixel_values= [transform(image)forimageinimages]pixel_values=torch.stack(pixel_values)returnpixel_valuespath="OpenGVLab/InternVL-Chat-V1-5"# If you have an 80G A100 GPU, you can put the entire model on a single GPU.model=AutoModel.from_pretrained(path,torch_dtype=torch.bfloat16,low_cpu_mem_usage=True,trust_remote_code=True).eval().cuda()# Otherwise, you need to set device_map='auto' to use multiple GPUs for inference.# model = AutoModel.from_pretrained(#     path,#     torch_dtype=torch.bfloat16,#     low_cpu_mem_usage=True,#     trust_remote_code=True,#     device_map='auto').eval()tokenizer=AutoTokenizer.from_pretrained(path,trust_remote_code=True)# set the max number of tiles in `max_num`pixel_values=load_image('./examples/image1.jpg',max_num=6).to(torch.bfloat16).cuda()generation_config=dict(num_beams=1,max_new_tokens=512,do_sample=False,)# single-round single-image conversationquestion="请详细描述图片"# Please describe the picture in detailresponse=model.chat(tokenizer,pixel_values,question,generation_config)print(question,response)# multi-round single-image conversationquestion="请详细描述图片"# Please describe the picture in detailresponse,history=model.chat(tokenizer,pixel_values,question,generation_config,history=None,return_history=True)print(question,response)question="请根据图片写一首诗"# Please write a poem according to the pictureresponse,history=model.chat(tokenizer,pixel_values,question,generation_config,history=history,return_history=True)print(question,response)# multi-round multi-image conversationpixel_values1=load_image('./examples/image1.jpg',max_num=6).to(torch.bfloat16).cuda()pixel_values2=load_image('./examples/image2.jpg',max_num=6).to(torch.bfloat16).cuda()pixel_values=torch.cat((pixel_values1,pixel_values2),dim=0)question="详细描述这两张图片"# Describe the two pictures in detailresponse,history=model.chat(tokenizer,pixel_values,question,generation_config,history=None,return_history=True)print(question,response)question="这两张图片的相同点和区别分别是什么"# What are the similarities and differences between these two picturesresponse,history=model.chat(tokenizer,pixel_values,question,generation_config,history=history,return_history=True)print(question,response)# batch inference (single image per sample)pixel_values1=load_image('./examples/image1.jpg',max_num=6).to(torch.bfloat16).cuda()pixel_values2=load_image('./examples/image2.jpg',max_num=6).to(torch.bfloat16).cuda()image_counts= [pixel_values1.size(0),pixel_values2.size(0)]pixel_values=torch.cat((pixel_values1,pixel_values2),dim=0)questions= ["Describe the image in detail."]*len(image_counts)responses=model.batch_chat(tokenizer,pixel_values,image_counts=image_counts,questions=questions,generation_config=generation_config)forquestion,responseinzip(questions,responses):print(question)print(response)