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69 lines
2.2 KiB
69 lines
2.2 KiB
#!/usr/bin/env python
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from dataclasses import dataclass
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from typing import Callable
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__all__ = ['ModelZooRegistry', 'ModelAttribute', 'model_zoo']
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@dataclass
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class ModelAttribute:
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"""
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Attributes of a model.
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Args:
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has_control_flow (bool): Whether the model contains branching in its forward method.
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has_stochastic_depth_prob (bool): Whether the model contains stochastic depth probability. Often seen in the torchvision models.
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"""
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has_control_flow: bool = False
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has_stochastic_depth_prob: bool = False
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class ModelZooRegistry(dict):
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"""
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A registry to map model names to model and data generation functions.
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"""
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def register(self,
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name: str,
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model_fn: Callable,
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data_gen_fn: Callable,
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output_transform_fn: Callable,
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model_attribute: ModelAttribute = None):
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"""
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Register a model and data generation function.
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Examples:
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>>> # Register
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>>> model_zoo = ModelZooRegistry()
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>>> model_zoo.register('resnet18', resnet18, resnet18_data_gen)
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>>> # Run the model
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>>> data = resnet18_data_gen() # do not input any argument
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>>> model = resnet18() # do not input any argument
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>>> out = model(**data)
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Args:
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name (str): Name of the model.
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model_fn (callable): A function that returns a model. **It must not contain any arguments.**
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output_transform_fn (callable): A function that transforms the output of the model into Dict.
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data_gen_fn (callable): A function that returns a data sample in the form of Dict. **It must not contain any arguments.**
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model_attribute (ModelAttribute): Attributes of the model. Defaults to None.
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"""
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self[name] = (model_fn, data_gen_fn, output_transform_fn, model_attribute)
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def get_sub_registry(self, keyword: str):
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"""
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Get a sub registry with models that contain the keyword.
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Args:
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keyword (str): Keyword to filter models.
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"""
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new_dict = dict()
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for k, v in self.items():
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if keyword in k:
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new_dict[k] = v
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return new_dict
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model_zoo = ModelZooRegistry()
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