LAP/torchlap/models/rsna/lap_resnet.py

from typing import Dict

import torch

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


def get_pool_factory(discriminative_attention=True):
    def pool_factory(channels, sigmoid_scale=1.0):
        return LAP(channels,
                hidden_channels=[8],
                sigmoid_scale=sigmoid_scale,
                discriminative_attention=discriminative_attention)
    return pool_factory


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


class RSNALAPResNet18(LapResNet):

    def __init__(self, pool_factory: PoolFactory = get_pool_factory(),
                 adaptive_factory: PoolFactory = get_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)
    
    def forward(self, x: torch.Tensor, y: torch.Tensor = None) -> Dict[str, torch.Tensor]:
        x = x.repeat_interleave(3, dim=1)  # B 3   224 224
        return super().forward(x, y)