LAP/torchlap/models/celeba/lap_resnet.py

from collections import OrderedDict
from typing import Dict, List
import typing

import torch

from ...modules import LAP, AdaptiveLAP
from ..lap_resnet import LapBasicBlock, LapResNet, PoolFactory


def pool_factory(channels, sigmoid_scale=1.0):
    return LAP(channels,
                sigmoid_scale=sigmoid_scale,
                n_attention_heads=3,
                discriminative_attention=True)


def adaptive_pool_factory(channels, sigmoid_scale=1.0):
    return AdaptiveLAP(channels,
                       sigmoid_scale=sigmoid_scale,
                       n_attention_heads=3,
                       discriminative_attention=True)


class CelebALAPResNet18(LapResNet):

    def __init__(self, tag: str,
                 pool_factory: PoolFactory = pool_factory,
                 adaptive_factory: PoolFactory = adaptive_pool_factory,
                 sigmoid_scale: float = 1.0):
        super().__init__(LapBasicBlock, [2, 2, 2, 2],
                         pool_factory=pool_factory,
                         adaptive_factory=adaptive_factory,
                         sigmoid_scale=sigmoid_scale,
                         binary=True)
        self._tag = tag

    @property
    def additional_kwargs(self) -> typing.OrderedDict[str, bool]:
        r""" Returns a dictionary from additional `kwargs` names to their optionality """
        return OrderedDict({
            f'{self._tag}': True,
        })

    def forward(self, x: torch.Tensor, **gts: torch.Tensor) -> Dict[str, torch.Tensor]:
        y = gts[f'{self._tag}']
        return super().forward(x, y)

    @property
    def attention_layers(self) -> Dict[str, List[LAP]]:
        res = super().attention_layers
        res.pop('4_overall')
        return res