| from typing import List | |
| from transformers import PretrainedConfig | |
| """ | |
| The configuration of a model is an object that | |
| will contain all the necessary information to build the model. | |
| The three important things to remember when writing you own configuration are the following: | |
| - you have to inherit from PretrainedConfig, | |
| - the __init__ of your PretrainedConfig must accept any kwargs, | |
| - those kwargs need to be passed to the superclass __init__. | |
| """ | |
| class ResnetConfig(PretrainedConfig): | |
| """ | |
| Defining a model_type for your configuration (here model_type="resnet") is not mandatory, | |
| unless you want to register your model with the auto classes (see last section).""" | |
| model_type = "rgbdsod-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) | |
| if __name__ == "__main__": | |
| """ | |
| With this done, you can easily create and save your configuration like | |
| you would do with any other model config of the library. | |
| Here is how we can create a resnet50d config and save it: | |
| """ | |
| resnet50d_config = ResnetConfig( | |
| block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True | |
| ) | |
| resnet50d_config.save_pretrained("custom-resnet") | |
| """ | |
| This will save a file named config.json inside the folder custom-resnet. | |
| You can then reload your config with the from_pretrained method: | |
| """ | |
| resnet50d_config = ResnetConfig.from_pretrained("custom-resnet") | |
| """ | |
| You can also use any other method of the PretrainedConfig class, | |
| like push_to_hub() to directly upload your config to the Hub. | |
| """ | |