| """MVANet model configuration.""" |
|
|
| from typing import List |
|
|
| from transformers import PretrainedConfig |
|
|
|
|
| class MVANetConfig(PretrainedConfig): |
| """ |
| Configuration class for MVANet model. |
| |
| This is the configuration class to store the configuration of a |
| :class:`~mvanet.transformers.MVANetForImageSegmentation`. |
| It is used to instantiate a MVANet model according to the specified arguments, |
| defining the model architecture. |
| |
| Configuration objects inherit from :class:`~transformers.PretrainedConfig` and |
| can be used to control the model outputs. Read the documentation from |
| :class:`~transformers.PretrainedConfig` for more information. |
| |
| Args: |
| embedding_dim (:obj:`int`, `optional`, defaults to 128): |
| The embedding dimension used throughout the model. |
| backbone_type (:obj:`str`, `optional`, defaults to :obj:`"swinb"`): |
| Type of backbone to use. Currently only "swinb" (Swin Transformer Base) is supported. |
| backbone_pretrained (:obj:`bool`, `optional`, defaults to :obj:`True`): |
| Whether to use pretrained weights for the backbone. |
| backbone_out_channels (:obj:`List[int]`, `optional`, defaults to :obj:`[128, 128, 256, 512, 1024]`): |
| Output channel dimensions for each backbone level (SwinB specific). |
| mclm_num_heads (:obj:`int`, `optional`, defaults to 1): |
| Number of attention heads in Multi-field Cross Localization Module (MCLM). |
| mclm_pool_ratios (:obj:`List[int]`, `optional`, defaults to :obj:`[1, 4, 8]`): |
| Pool ratios for MCLM multi-scale attention. |
| mcrm_num_heads (:obj:`int`, `optional`, defaults to 1): |
| Number of attention heads in Multi-crop Refinement Module (MCRM). |
| mcrm_pool_ratios (:obj:`List[int]`, `optional`, defaults to :obj:`[2, 4, 8]`): |
| Pool ratios for MCRM multi-scale attention. |
| insmask_hidden_dim (:obj:`int`, `optional`, defaults to 384): |
| Hidden dimension in the instance mask head. |
| global_view_scale (:obj:`float`, `optional`, defaults to 0.5): |
| Scale factor for creating the global view (downsampled version of input). |
| num_patches (:obj:`int`, `optional`, defaults to 4): |
| Number of local patches (currently only 4 for 2x2 grid is supported). |
| image_size (:obj:`int`, `optional`, defaults to 1024): |
| Input image size the model was trained on. |
| num_channels (:obj:`int`, `optional`, defaults to 3): |
| Number of input channels (3 for RGB images). |
| num_labels (:obj:`int`, `optional`, defaults to 1): |
| Number of output labels (1 for binary segmentation). |
| |
| Example:: |
| |
| >>> from mvanet.transformers import MVANetConfig, MVANetForImageSegmentation |
| |
| >>> # Initializing a MVANet configuration |
| >>> configuration = MVANetConfig() |
| |
| >>> # Initializing a model from the configuration |
| >>> model = MVANetForImageSegmentation(configuration) |
| |
| >>> # Accessing the model configuration |
| >>> configuration = model.config |
| """ |
|
|
| model_type = "mvanet" |
|
|
| def __init__( |
| self, |
| embedding_dim: int = 128, |
| backbone_type: str = "swinb", |
| backbone_pretrained: bool = True, |
| backbone_out_channels: List[int] | None = None, |
| mclm_num_heads: int = 1, |
| mclm_pool_ratios: List[int] | None = None, |
| mcrm_num_heads: int = 1, |
| mcrm_pool_ratios: List[int] | None = None, |
| insmask_hidden_dim: int = 384, |
| global_view_scale: float = 0.5, |
| num_patches: int = 4, |
| image_size: int = 1024, |
| num_channels: int = 3, |
| num_labels: int = 1, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
|
|
| self.embedding_dim = embedding_dim |
| self.backbone_type = backbone_type |
| self.backbone_pretrained = backbone_pretrained |
| |
| self.backbone_out_channels = ( |
| backbone_out_channels |
| if backbone_out_channels is not None |
| else [128, 128, 256, 512, 1024] |
| ) |
| self.mclm_num_heads = mclm_num_heads |
| self.mclm_pool_ratios = ( |
| mclm_pool_ratios if mclm_pool_ratios is not None else [1, 4, 8] |
| ) |
| self.mcrm_num_heads = mcrm_num_heads |
| self.mcrm_pool_ratios = ( |
| mcrm_pool_ratios if mcrm_pool_ratios is not None else [2, 4, 8] |
| ) |
| self.insmask_hidden_dim = insmask_hidden_dim |
| self.global_view_scale = global_view_scale |
| self.num_patches = num_patches |
| self.image_size = image_size |
| self.num_channels = num_channels |
| self.num_labels = num_labels |
|
|
