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from transformers import PretrainedConfig
class ResnetConfig(PretrainedConfig):
model_type = "custom_resnet50d"
def __init__(
self,
block_type="bottleneck",
layers: list[int] = [3, 4, 6, 3],
num_classes: int = 1000,
input_channels: int = 3,
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = "",
avg_down: bool = False,
**kwargs,
):
if block_type not in ["basic", "bottleneck"]:
raise ValueError(
f"block_type should be either 'basic' or 'bottleneck', but is {block_type}"
)
if stem_type not in ["", "deep", "deep-tiered"]:
raise ValueError(
f"stem_type should be either '', 'deep' or 'deep-tiered', but is {stem_type}"
)
self.block_type = block_type
self.layers = layers
self.num_classes = num_classes
self.input_channels = input_channels
self.cardinality = cardinality
self.base_width = base_width
self.stem_width = stem_width
self.stem_type = stem_type
self.avg_down = avg_down
super().__init__(**kwargs)
__all__ = [
"ResnetConfig",
]
if __name__ == "__main__":
resnet50d_config = ResnetConfig(
block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True
)
print(resnet50d_config)
resnet50d_config.save_pretrained("resnet50d_config")
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