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| """ BiT model configuration""" |
|
|
| from ...configuration_utils import PretrainedConfig |
| from ...utils import logging |
| from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
| BIT_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
| "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json", |
| } |
|
|
|
|
| class BitConfig(BackboneConfigMixin, PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT |
| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
| defaults will yield a similar configuration to that of the BiT |
| [google/bit-50](https://huggingface.co/google/bit-50) architecture. |
| |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
| documentation from [`PretrainedConfig`] for more information. |
| |
| Args: |
| num_channels (`int`, *optional*, defaults to 3): |
| The number of input channels. |
| embedding_size (`int`, *optional*, defaults to 64): |
| Dimensionality (hidden size) for the embedding layer. |
| hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): |
| Dimensionality (hidden size) at each stage. |
| depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): |
| Depth (number of layers) for each stage. |
| layer_type (`str`, *optional*, defaults to `"preactivation"`): |
| The layer to use, it can be either `"preactivation"` or `"bottleneck"`. |
| hidden_act (`str`, *optional*, defaults to `"relu"`): |
| The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` |
| are supported. |
| global_padding (`str`, *optional*): |
| Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`. |
| num_groups (`int`, *optional*, defaults to 32): |
| Number of groups used for the `BitGroupNormActivation` layers. |
| drop_path_rate (`float`, *optional*, defaults to 0.0): |
| The drop path rate for the stochastic depth. |
| embedding_dynamic_padding (`bool`, *optional*, defaults to `False`): |
| Whether or not to make use of dynamic padding for the embedding layer. |
| output_stride (`int`, *optional*, defaults to 32): |
| The output stride of the model. |
| width_factor (`int`, *optional*, defaults to 1): |
| The width factor for the model. |
| out_features (`List[str]`, *optional*): |
| If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. |
| (depending on how many stages the model has). If unset and `out_indices` is set, will default to the |
| corresponding stages. If unset and `out_indices` is unset, will default to the last stage. |
| out_indices (`List[int]`, *optional*): |
| If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how |
| many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. |
| If unset and `out_features` is unset, will default to the last stage. |
| |
| Example: |
| ```python |
| >>> from transformers import BitConfig, BitModel |
| |
| >>> # Initializing a BiT bit-50 style configuration |
| >>> configuration = BitConfig() |
| |
| >>> # Initializing a model (with random weights) from the bit-50 style configuration |
| >>> model = BitModel(configuration) |
| |
| >>> # Accessing the model configuration |
| >>> configuration = model.config |
| ``` |
| """ |
| model_type = "bit" |
| layer_types = ["preactivation", "bottleneck"] |
| supported_padding = ["SAME", "VALID"] |
|
|
| def __init__( |
| self, |
| num_channels=3, |
| embedding_size=64, |
| hidden_sizes=[256, 512, 1024, 2048], |
| depths=[3, 4, 6, 3], |
| layer_type="preactivation", |
| hidden_act="relu", |
| global_padding=None, |
| num_groups=32, |
| drop_path_rate=0.0, |
| embedding_dynamic_padding=False, |
| output_stride=32, |
| width_factor=1, |
| out_features=None, |
| out_indices=None, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
| if layer_type not in self.layer_types: |
| raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}") |
| if global_padding is not None: |
| if global_padding.upper() in self.supported_padding: |
| global_padding = global_padding.upper() |
| else: |
| raise ValueError(f"Padding strategy {global_padding} not supported") |
| self.num_channels = num_channels |
| self.embedding_size = embedding_size |
| self.hidden_sizes = hidden_sizes |
| self.depths = depths |
| self.layer_type = layer_type |
| self.hidden_act = hidden_act |
| self.global_padding = global_padding |
| self.num_groups = num_groups |
| self.drop_path_rate = drop_path_rate |
| self.embedding_dynamic_padding = embedding_dynamic_padding |
| self.output_stride = output_stride |
| self.width_factor = width_factor |
|
|
| self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)] |
| self._out_features, self._out_indices = get_aligned_output_features_output_indices( |
| out_features=out_features, out_indices=out_indices, stage_names=self.stage_names |
| ) |
|
|
