| from transformers import PretrainedConfig |
| from typing import List |
|
|
|
|
| class ResnetConfig(PretrainedConfig): |
| model_type = "resnet" |
|
|
| 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) |
|
|
| resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", |
| avg_down=True) |
| resnet50d_config.save_pretrained("custom-resnet") |
|
|
| resnet50d_config = ResnetConfig.from_pretrained("custom-resnet") |
|
|
