| from transformers import PretrainedConfig | |
| from typing import List | |
| class CetaceanClassifierConfig(PretrainedConfig): | |
| model_type = "cetaceanet" | |
| def __init__( | |
| self, | |
| # block_type="bottleneck", | |
| # layers: List[int] = [3, 4, 6, 3], | |
| # num_classes: int = 1000, | |
| # input_channels: int = 3, | |
| # cardinality: int = 1, | |
| # base_width: int = 64, | |
| # stem_width: int = 64, | |
| # stem_type: str = "", | |
| # avg_down: bool = False, | |
| **kwargs, | |
| ): | |
| # if block_type not in ["basic", "bottleneck"]: | |
| # raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.") | |
| # if stem_type not in ["", "deep", "deep-tiered"]: | |
| # raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.") | |
| # self.block_type = block_type | |
| # self.layers = layers | |
| # self.num_classes = num_classes | |
| # self.input_channels = input_channels | |
| # self.cardinality = cardinality | |
| # self.base_width = base_width | |
| # self.stem_width = stem_width | |
| # self.stem_type = stem_type | |
| # self.avg_down = avg_down | |
| super().__init__(**kwargs) | |