File size: 2,709 Bytes
f18109c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Copyright 2024 SatMAE++ Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
"""SatMAE++ image feature extraction pipeline."""
from typing import Any, Union
from transformers.pipelines.base import GenericTensor, build_pipeline_init_args
from transformers.pipelines.image_feature_extraction import ImageFeatureExtractionPipeline
from transformers.utils import add_end_docstrings, is_vision_available
if is_vision_available():
from transformers.image_utils import load_image
@add_end_docstrings(
build_pipeline_init_args(has_image_processor=True),
"""
pool (`bool`, *optional*, defaults to `False`):
Whether or not to return the pooled output. If `False`, the model will return the raw hidden states.
""",
)
class SatMAEppImageFeatureExtractionPipeline(ImageFeatureExtractionPipeline):
"""
SatMAE++ image feature extraction pipeline.
This pipeline wraps [`SatMAEppModel`] for RGB and multispectral satellite feature extraction.
Example:
```python
>>> from transformers import pipeline
>>> pipe = pipeline(
... task="image-feature-extraction",
... model="/path/to/satmae-pp-vit-large-patch16-fmow-rgb-finetune",
... trust_remote_code=True,
... )
>>> features = pipe(image_array, pool=True, return_tensors=True)
```
"""
def _sanitize_parameters(
self,
image_processor_kwargs=None,
return_tensors=None,
pool=None,
**kwargs,
):
preprocess_params = {} if image_processor_kwargs is None else dict(image_processor_kwargs)
if "timeout" in kwargs:
preprocess_params["timeout"] = kwargs["timeout"]
postprocess_params = {}
if pool is not None:
postprocess_params["pool"] = pool
if return_tensors is not None:
postprocess_params["return_tensors"] = return_tensors
return preprocess_params, {}, postprocess_params
def preprocess(self, image, timeout=None, **image_processor_kwargs) -> dict[str, GenericTensor]:
if not isinstance(image, (list, tuple)) and not hasattr(image, "shape"):
image = load_image(image, timeout=timeout)
model_inputs = self.image_processor(image, return_tensors="pt", **image_processor_kwargs)
model_inputs = model_inputs.to(self.dtype)
return model_inputs
def __call__(
self,
*args: Union[str, Any, list[Any]],
**kwargs: Any,
) -> list[Any]:
return super().__call__(*args, **kwargs)
__all__ = ["SatMAEppImageFeatureExtractionPipeline"]
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