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| from transformers import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class ClipSegMultiClassConfig(PretrainedConfig): | |
| model_type = "clipseg-multiclass" | |
| is_composition = False | |
| def __init__( | |
| self, | |
| class_labels=None, | |
| label2color=None, | |
| model="CIDAS/clipseg-rd64-refined", | |
| image_size=352, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.class_labels = class_labels or [] | |
| self.num_classes = len(self.class_labels) | |
| self.label2color = label2color or { | |
| i: [ | |
| int(255 * (i / max(1, self.num_classes - 1))), | |
| 0, | |
| 255 - int(255 * (i / max(1, self.num_classes - 1))) | |
| ] | |
| for i in range(self.num_classes) | |
| } | |
| self.model = model | |
| self.image_size = image_size | |