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"""Miscellaneous neural network functionality."""
import importlib
from pathlib import Path
from typing import Dict, 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: str, backbone_args: Dict) -> Type[nn.Module]:
"""Loads a backbone network."""
network_module = importlib.import_module("text_recognizer.networks")
backbone_ = getattr(network_module, backbone)
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
network_args = backbone_args
# Initializes the network with trained weights.
backbone = backbone_(**network_args)
backbone.load_state_dict(weights)
if freeze:
for params in backbone.parameters():
params.requires_grad = False
else:
backbone_ = getattr(network_module, backbone)
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
|
