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configuration_resnet.py

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+from transformers import PretrainedConfig
+from typing import List
+from pprint import pprint
+
+
+class ResnetConfig(PretrainedConfig):
+    model_type = "faen_resnet"
+
+    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` must be 'basic' or bottleneck', got {block_type}.")
+        if stem_type not in ["", "deep", "deep-tiered"]:
+            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {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)
+
+if __name__ == "__main__":
+    resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
+    print("init a ResnetConfig, it is:\n")
+    pprint(resnet50d_config)
+    resnet50d_config.save_pretrained("./")
+    resnet50d_config = ResnetConfig.from_pretrained("./")
+    print("\n")
+    print("saved to file config.json and reload it from config.json and it is:\n")
+    pprint(resnet50d_config)