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import logging |
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from typing import Literal |
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from transformers import PretrainedConfig |
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logger = logging.getLogger(__name__) |
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class ResnetConfig(PretrainedConfig): |
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"""The config class for custom resnet.""" |
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model_type: str = "custom-resnet" |
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"""The unique model type used to write meta on HuggingFace and register auto class.""" |
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def __init__( |
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self, |
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block_type: Literal['bottleneck', 'basic'] = "bottleneck", |
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layers: tuple[int] = (3, 4, 6, 3), |
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num_classes: int = 1000, |
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input_channels: int = 3, |
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cardinality: int = 1, |
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base_width: int = 64, |
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stem_width: int = 64, |
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stem_type: Literal['', 'deep', 'deep-tiered'] = "", |
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avg_down: bool = False, |
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load_pretrained_state: bool = True, |
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pretrained_state_source: Literal['timm'] = "timm", |
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**kwargs |
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): |
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if block_type not in ["basic", "bottleneck"]: |
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raise ValueError(f"`block_type` must be 'basic' or 'bottleneck', got {block_type!r}.") |
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if stem_type not in ["", "deep", "deep-tiered"]: |
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raise ValueError(f"`stem_type` must be '', 'deep' or 'deep_tiered', got {stem_type!r}") |
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self.block_type = block_type |
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self.layers = layers |
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self.num_classes = num_classes |
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self.input_channels = input_channels |
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self.cardinality = cardinality |
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self.base_width = base_width |
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self.stem_width = stem_width |
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self.stem_type = stem_type |
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self.avg_down = avg_down |
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self.load_pretrained_state = load_pretrained_state |
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self.pretrained_state_source = pretrained_state_source |
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super().__init__(**kwargs) |
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