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""" AdaptFormer model configuration"""
from transformers import PretrainedConfig
class AdaptFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AdaptFormerForChangeDetection`].
It is used to instantiate an AdaptFormer 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 AdaptFormer
[deepang/adaptformer-LEVIR-CD](https://huggingface.co/deepang/adaptformer-LEVIR-CD)
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.
num_classes (`int`, *optional*, defaults to 2):
The number of classes.
embed_dims (`List[int]`, *optional*, defaults to `[64, 128, 256]`):
Dimension of each of the encoder blocks.
num_heads (`List[int]`, *optional*, defaults to `[1, 2, 4]`):
Number of attention heads for each attention layer in each block of the encoder.
mlp_ratios (`List[int]`, *optional*, defaults to `[4, 4, 4]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
depths (`List[int]`, *optional*, defaults to `[3, 3, 3]`):
The number of layers in each encoder block.
semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
The index that is ignored by the loss function of the semantic segmentation model.
semantic_loss_weight (`List[float]`, *optional*, defaults to `[0, 0, 0.8, 1]`):
The weight of the semantic segmentation loss.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import AutoModel, AutoConfig
>>> # Initializing a AdaptFormer
>>> configuration = AutoConfig.from_pretrained("deepang/adaptformer-LEVIR-CD")
>>> # Initializing a model from the deepang/adaptformer-LEVIR-CD style configuration
>>> model = AutoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "adaptformer"
def __init__(
self,
num_channels=3,
num_classes=2,
embed_dims=[64, 128, 256],
num_heads=[1, 2, 4],
mlp_ratios=[4, 4, 4],
depths=[3, 3, 3],
semantic_loss_ignore_index=255,
semantic_loss_weight=[0, 0, 0.5, 1],
initializer_range=0.02,
**kwargs,
):
self.num_channels = num_channels
self.embed_dims = embed_dims
self.num_heads = num_heads
self.num_heads = num_heads
self.mlp_ratios = mlp_ratios
self.depths = depths
self.num_classes = num_classes
self.semantic_loss_ignore_index = semantic_loss_ignore_index
self.semantic_loss_weight = semantic_loss_weight
self.initializer_range = initializer_range
super().__init__(**kwargs)
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