LAP/torchlap/experiments/rsna/bb_lap_resnet.py

from collections import OrderedDict
from typing import Tuple

from ...utils.output_modifier import OutputModifier
from ...utils.aux_output import AuxOutput
from ...model_evaluation.multieval_evaluator import MultiEvaluatorEvaluator
from ...model_evaluation.binary_evaluator import BinaryEvaluator
from ...model_evaluation.binary_fortelling import BinForetellerEvaluator
from ...model_evaluation.binary_faithfulness import BinFaithfulnessEvaluator
from ...model_evaluation.loss_evaluator import LossEvaluator
from ...criteria.bb_supervised import BBLoss
from ...configs.rsna_configs import RSNAConfigs
from ...models.model import Model
from ..entrypoint import BaseEntrypoint
from ...models.rsna.lap_resnet import RSNALAPResNet18, get_adaptive_pool_factory, get_pool_factory


class EntryPoint(BaseEntrypoint):
    
    def _get_conf_model(self) -> Tuple[RSNAConfigs, Model]:
        config = RSNAConfigs('RSNA_ResNet_BB', 1, 224, self.phase_type)
        model = RSNALAPResNet18(
            pool_factory=get_pool_factory(discriminative_attention=False),
            adaptive_factory=get_adaptive_pool_factory(discriminative_attention=False),
            sigmoid_scale=0.1,
        )

        bb_losses = BBLoss(
                'BB', model, {
                    'att2': model.layer2[0].pool.attention_layer,
                    'att3': model.layer3[0].pool.attention_layer,
                    'att4': model.layer4[0].pool.attention_layer,
                    'att5': model.avgpool[0].attention_layer,
                }
                , 1, 
                'interpretations', 1, 0.5)
        bb_losses.configure(config)

        config.evaluator_cls = MultiEvaluatorEvaluator.create_standard_multi_evaluator_evaluator_maker(OrderedDict({
            'b': BinaryEvaluator,
            'l': LossEvaluator,
            'f': BinForetellerEvaluator.standard_creator('foretell'),
            'bf': BinFaithfulnessEvaluator.standard_creator('foretell'),
        }))
        config.title_of_reference_metric_to_choose_best_epoch = 'b_BAcc'

        ###################################
        ########### Foreteller ############
        ###################################

        aux = AuxOutput(model, dict(
            foretell_pool2=model.layer2[0].pool.attention_layer,
            foretell_pool3=model.layer3[0].pool.attention_layer,
            foretell_pool4=model.layer4[0].pool.attention_layer,
            foretell_avgpool=model.avgpool[0].attention_layer,
        ))
        aux.configure(config)

        output_modifier = OutputModifier(model,
            lambda x: x.flatten(1).max(dim=-1).values,
            'foretell_pool2',
            'foretell_pool3',
            'foretell_pool4',
            'foretell_avgpool',
        )
        output_modifier.configure(config)


        return config, model