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"""Miscellaneous neural network functionality."""
import importlib
from pathlib import Path
from typing import Dict, NamedTuple, Union, Type
from loguru import logger
import torch
from torch import nn
def activation_function(activation: str) -> Type[nn.Module]:
"""Returns the callable activation function."""
activation_fns = nn.ModuleDict(
[
["elu", nn.ELU(inplace=True)],
["gelu", nn.GELU()],
["glu", nn.GLU()],
["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
["none", nn.Identity()],
["relu", nn.ReLU(inplace=True)],
["selu", nn.SELU(inplace=True)],
]
)
return activation_fns[activation.lower()]
# def configure_backbone(backbone: Union[OmegaConf, NamedTuple]) -> Type[nn.Module]:
# """Loads a backbone network."""
# network_module = importlib.import_module("text_recognizer.networks")
# backbone_class = getattr(network_module, backbone.type)
#
# if "pretrained" in backbone.args:
# logger.info("Loading pretrained backbone.")
# checkpoint_file = Path(__file__).resolve().parents[2] / backbone.args.pop(
# "pretrained"
# )
#
# # Loading state directory.
# state_dict = torch.load(checkpoint_file)
# network_args = state_dict["network_args"]
# weights = state_dict["model_state"]
#
# freeze = False
# if "freeze" in backbone.args and backbone.args["freeze"] is True:
# backbone.args.pop("freeze")
# freeze = True
#
# # Initializes the network with trained weights.
# backbone_ = backbone_(**backbone.args)
# backbone_.load_state_dict(weights)
# if freeze:
# for params in backbone_.parameters():
# params.requires_grad = False
# else:
# backbone_ = getattr(network_module, backbone.type)
# backbone_ = backbone_(**backbone.args)
#
# if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
# backbone = nn.Sequential(
# *list(backbone.children())[:][: -backbone_args["remove_layers"]]
# )
#
# return backbone
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