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from transformers import PretrainedConfig
from typing import List, Tuple
class MammoConfig(PretrainedConfig):
model_type = "mammo"
def __init__(
self,
backbone: str = "tf_efficientnetv2_s",
feature_dim: int = 1280,
dropout: float = 0.1,
num_classes: int = 5,
in_chans: int = 1,
num_models: int = 3,
image_sizes: List[Tuple[int, int]] = [(2048, 1024), (1920, 1280), (1536, 1536)],
pad_to_aspect_ratio: List[bool] = [True, True, False],
**kwargs,
):
self.backbone = backbone
self.feature_dim = feature_dim
self.dropout = dropout
self.num_classes = num_classes
self.in_chans = in_chans
self.num_models = num_models
assert len(image_sizes) == len(pad_to_aspect_ratio) == num_models, (
f"length of `image_sizes` [{len(image_sizes)}] and `pad_to_aspect_ratio` "
f"[{len(pad_to_aspect_ratio)}] must be equal to `num_models` [{num_models}]."
)
self.image_sizes = image_sizes
self.pad_to_aspect_ratio = pad_to_aspect_ratio
super().__init__(**kwargs)
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