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from transformers import PretrainedConfig
from typing import List, Optional, Tuple
class PneumoniaConfig(PretrainedConfig):
model_type = "pneumonia"
def __init__(
self,
backbone: str = "tf_efficientnetv2_s",
feature_dim: int = 256,
seg_dropout: float = 0.1,
cls_dropout: float = 0.1,
seg_num_classes: int = 1,
cls_num_classes: int = 1,
in_chans: int = 1,
img_size: Tuple[int, int] = (512, 512), # height, width
decoder_n_blocks: int = 5,
decoder_channels: List[int] = [256, 128, 64, 32, 16],
encoder_channels: List[int] = [24, 48, 64, 160, 256],
decoder_center_block: bool = False,
decoder_norm_layer: str = "bn",
decoder_attention_type: Optional[str] = None,
**kwargs,
):
self.backbone = backbone
self.feature_dim = feature_dim
self.seg_dropout = seg_dropout
self.cls_dropout = cls_dropout
self.seg_num_classes = seg_num_classes
self.cls_num_classes = cls_num_classes
self.in_chans = in_chans
self.img_size = img_size
self.decoder_n_blocks = decoder_n_blocks
self.decoder_channels = decoder_channels
self.encoder_channels = encoder_channels
self.decoder_center_block = decoder_center_block
self.decoder_norm_layer = decoder_norm_layer
self.decoder_attention_type = decoder_attention_type
super().__init__(**kwargs)
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