40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
#######################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#######################################################
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# Use module in xlayers to construct different models #
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#######################################################
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from typing import List, Text, Dict, Any
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import torch
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__all__ = ["get_model"]
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from xlayers.super_core import SuperSequential
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from xlayers.super_core import SuperLinear
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from xlayers.super_core import super_name2norm
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from xlayers.super_core import super_name2activation
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def get_model(config: Dict[Text, Any], **kwargs):
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model_type = config.get("model_type", "simple_mlp")
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if model_type == "simple_mlp":
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act_cls = super_name2activation[kwargs["act_cls"]]
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norm_cls = super_name2norm[kwargs["norm_cls"]]
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mean, std = kwargs.get("mean", None), kwargs.get("std", None)
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if "hidden_dim" in kwargs:
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hidden_dim1 = kwargs.get("hidden_dim")
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hidden_dim2 = kwargs.get("hidden_dim")
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else:
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hidden_dim1 = kwargs.get("hidden_dim1", 200)
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hidden_dim2 = kwargs.get("hidden_dim2", 100)
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model = SuperSequential(
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norm_cls(mean=mean, std=std),
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SuperLinear(kwargs["input_dim"], hidden_dim1),
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act_cls(),
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SuperLinear(hidden_dim1, hidden_dim2),
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act_cls(),
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SuperLinear(hidden_dim2, kwargs["output_dim"]),
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)
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else:
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raise TypeError("Unkonwn model type: {:}".format(model_type))
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return model |