fromtorchvision.datasets import CIFAR100 # Load the model device = "cuda" if torch.cuda.is_available() else "cpu" model,preprocess= clip.load('ViT-B/32', device) # Download the dataset cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False) # Prepare the...
VISION_CONFIG = "vision_config" class CLIPVisionModelOnnxConfig(VisionOnnxConfig): NORMALIZED_CONFIG_CLASS = NormalizedVisionConfig @property def inputs(self) -> Dict[str, Dict[int, str]]: return {"pixel_values": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"}} @...
fxmartymerged 2 commits intohuggingface:mainfromfxmarty:support-clip-vision-model Jul 11, 2024 +21−0 Conversation3Commits2Checks122Files changed3 Contributor fxmartycommentedJun 25, 2024 Merge branch 'master' into support-clip-vision-model ...
System.Net.Http SystemConfiguration UserNotifications VideoSubscriberAccount VideoToolbox Vision WebKitLearn Xamarin .NET API browser AppKit C# Save Add to Collections Add to Plan Share via Facebook x.com LinkedIn Email Print NSClipView ClassReference...
vision.faceapi.models com.microsoft.azure.elasticdb.core.commons.transientfaulthandling com.microsoft.azure.elasticdb.query.exception com.microsoft.azure.elasticdb.query.logging com.microsoft.azure.elasticdb.query.multishard com.microsoft.azure.elasticdb.shard.base com.microsoft.azure.elasticdb.shard.map...
6.构建CLIP模型:这里根据传入的参数,拼接了视觉模型和文本模型的配置文件路径,然后加载并解析了视觉模型和文本模型的配置文件,将其存储在model_info中,最后将args.use_flash_attention设置到model_info中。最后构建了CLIP模型实例,并传入了model_info中的参数 vision_model_config_file = Path(__file__).parent.pare...
vision.faceapi.models com.microsoft.azure.elasticdb.core.commons.transientfaulthandling com.microsoft.azure.elasticdb.query.exception com.microsoft.azure.elasticdb.query.logging com.microsoft.azure.elasticdb.query.multishard com.microsoft.azure.elasticdb.shard.base com.microsoft.azure.elasticdb.shard.map...
This can be used as the input to the model The model returned by clip.load() supports the following methods: model.encode_image(image: Tensor) Given a batch of images, returns the image features encoded by the vision portion of the CLIP model. model.encode_text(text: Tensor) Given a ...
安装并导入CLIP及其相关库后,我们加载所需的模型和torchvision转换流水线。文本编码器是一个Transformer,而图像编码器可以是Vision Transformer(ViT)或ResNet50等ResNet变体。你可以使用命令clip.available_models()查看可用的图像编码器。 print( clip.available_models() ) ...
"model_type": string"clip_vision_model" "no_repeat_ngram_size": int0 "num_attention_heads": int16 "num_beam_groups": int1 "num_beams": int1 "num_hidden_layers": int24 "num_return_sequences": int1 "output_attentions": boolfalse ...