LAP/torchlap/models/imagenet/lap_resnet.py

from typing import Dict, List
import torch

from torchvision import transforms

from ...modules import LAP, AdaptiveLAP, ScaledSigmoid
from ..lap_resnet import LapBottleneck, LapResNet, PoolFactory


def pool_factory(channels, sigmoid_scale=1.0):
    return LAP(channels,
               hidden_channels=[1000],
               sigmoid_scale=sigmoid_scale,
               hidden_activation=ScaledSigmoid.get_factory(sigmoid_scale))


class ImagenetLAPResNet50(LapResNet):

    def __init__(self, pool_factory: PoolFactory = pool_factory, sigmoid_scale: float = 1.0):
        super().__init__(LapBottleneck, [3, 4, 6, 3],
                         pool_factory=pool_factory,
                         sigmoid_scale=sigmoid_scale,
                         lap_positions=[4])
        self.normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))

    def forward(self, x: torch.Tensor, y: torch.Tensor) -> Dict[str, torch.Tensor]:
        x = self.normalize(x)
        return super().forward(x, y)

    @property
    def attention_layers(self) -> Dict[str, List[LAP]]:
        return {
            '2_layer4': super().attention_layers['2_layer4'],
        }