o Zh2@sddlZddlmZddlmZmZddlmZmZm Z m Z m Z m Z ddl mZmZe r9ddlmZdd lmZe rFddlZdd lmZerUdd lmZdd lmZe eZeed dGdddeZdS)N)UserDict)ListUnion)add_end_docstringsis_tf_availableis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES)9TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES)stable_softmaxT)Zhas_image_processorcsteZdZdZfddZddeeeededfffdd Zdd d Z   dd d Z ddZ ddZ Z S)#ZeroShotImageClassificationPipelineaL Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you provide an image and a set of `candidate_labels`. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="google/siglip-so400m-patch14-384") >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["animals", "humans", "landscape"], ... ) [{'score': 0.965, 'label': 'animals'}, {'score': 0.03, 'label': 'humans'}, {'score': 0.005, 'label': 'landscape'}] >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["black and white", "photorealist", "painting"], ... ) [{'score': 0.996, 'label': 'black and white'}, {'score': 0.003, 'label': 'photorealist'}, {'score': 0.0, 'label': 'painting'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-image-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-image-classification). c s>tjdi|t|d||jdkrtdStdS)NZvisiontf)super__init__r Zcheck_model_type frameworkrr)selfkwargs __class__rd/var/www/auris/lib/python3.10/site-packages/transformers/pipelines/zero_shot_image_classification.pyrCs  z,ZeroShotImageClassificationPipeline.__init__Nimagerc s6d|vr |d}|durtdtj|fi|S)a Assign labels to the image(s) passed as inputs. Args: image (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly candidate_labels (`List[str]`): The candidate labels for this image. They will be formatted using *hypothesis_template*. hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`): The format used in conjunction with *candidate_labels* to attempt the image classification by replacing the placeholder with the candidate_labels. Pass "{}" if *candidate_labels* are already formatted. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A list of dictionaries containing one entry per proposed label. Each dictionary contains the following keys: - **label** (`str`) -- One of the suggested *candidate_labels*. - **score** (`float`) -- The score attributed by the model to that label. 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