128 lines
4.8 KiB
Python
128 lines
4.8 KiB
Python
from typing import List, Union
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from ..utils import (
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add_end_docstrings,
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is_tf_available,
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is_torch_available,
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is_vision_available,
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logging,
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requires_backends,
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)
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from .base import PIPELINE_INIT_ARGS, Pipeline
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if is_vision_available():
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from PIL import Image
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from ..image_utils import load_image
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if is_tf_available():
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import tensorflow as tf
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from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
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from ..tf_utils import stable_softmax
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if is_torch_available():
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from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
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logger = logging.get_logger(__name__)
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@add_end_docstrings(PIPELINE_INIT_ARGS)
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class ImageClassificationPipeline(Pipeline):
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"""
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Image classification pipeline using any `AutoModelForImageClassification`. This pipeline predicts the class of an
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image.
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Example:
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```python
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>>> from transformers import pipeline
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>>> classifier = pipeline(model="microsoft/beit-base-patch16-224-pt22k-ft22k")
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>>> classifier("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
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[{'score': 0.442, 'label': 'macaw'}, {'score': 0.088, 'label': 'popinjay'}, {'score': 0.075, 'label': 'parrot'}, {'score': 0.073, 'label': 'parodist, lampooner'}, {'score': 0.046, 'label': 'poll, poll_parrot'}]
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```
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Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
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This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
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`"image-classification"`.
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See the list of available models on
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[huggingface.co/models](https://huggingface.co/models?filter=image-classification).
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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requires_backends(self, "vision")
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self.check_model_type(
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TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
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if self.framework == "tf"
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else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
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)
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def _sanitize_parameters(self, top_k=None):
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postprocess_params = {}
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if top_k is not None:
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postprocess_params["top_k"] = top_k
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return {}, {}, postprocess_params
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def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
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"""
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Assign labels to the image(s) passed as inputs.
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Args:
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images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
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The pipeline handles three types of images:
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- A string containing a http link pointing to an image
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- A string containing a local path to an image
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- An image loaded in PIL directly
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The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
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Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
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images.
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top_k (`int`, *optional*, defaults to 5):
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The number of top labels that will be returned by the pipeline. If the provided number is higher than
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the number of labels available in the model configuration, it will default to the number of labels.
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Return:
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A dictionary or a list of dictionaries containing result. If the input is a single image, will return a
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dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to
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the images.
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The dictionaries contain the following keys:
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- **label** (`str`) -- The label identified by the model.
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- **score** (`int`) -- The score attributed by the model for that label.
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"""
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return super().__call__(images, **kwargs)
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def preprocess(self, image):
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image = load_image(image)
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model_inputs = self.image_processor(images=image, return_tensors=self.framework)
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return model_inputs
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def _forward(self, model_inputs):
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model_outputs = self.model(**model_inputs)
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return model_outputs
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def postprocess(self, model_outputs, top_k=5):
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if top_k > self.model.config.num_labels:
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top_k = self.model.config.num_labels
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if self.framework == "pt":
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probs = model_outputs.logits.softmax(-1)[0]
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scores, ids = probs.topk(top_k)
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elif self.framework == "tf":
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probs = stable_softmax(model_outputs.logits, axis=-1)[0]
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topk = tf.math.top_k(probs, k=top_k)
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scores, ids = topk.values.numpy(), topk.indices.numpy()
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else:
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raise ValueError(f"Unsupported framework: {self.framework}")
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scores = scores.tolist()
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ids = ids.tolist()
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return [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
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