LAP/torchlap/models/lap_inception.py

from typing import List, Optional, Callable, Any, Iterable, Dict

import torch
from torch import Tensor, nn
import torchvision

from ..modules.lap import LAP
from ..interpreting.attention_interpreter import AttentionInterpretableModel
from ..interpreting.interpretable import CamInterpretableModel
from ..interpreting.relcam.relprop import RPProvider, RelProp
from ..interpreting.relcam import modules as M


class InceptionB(nn.Module):

    def __init__(
        self,
        in_channels: int,
        pool_factory,
        conv_block: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(InceptionB, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        #self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
        self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=1, padding=1)

        self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
        self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
        #self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
        self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=1, padding=1)

        self.pool = pool_factory(384 + 96 + in_channels)

        self.cat = M.Cat()

    def _forward(self, x: Tensor) -> List[Tensor]:

        branch3x3 = self.branch3x3(x)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        outputs = [branch3x3, branch3x3dbl, x]
        return outputs

    def forward(self, x: Tensor) -> Tensor:
        outputs = self._forward(x)
        return self.pool(self.cat(outputs, 1))


@RPProvider.register(InceptionB)
class InceptionBRelProp(RelProp[InceptionB]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        R = RPProvider.get(self.module.pool)(R, alpha=alpha)
        branch3x3, branch3x3dbl, x1 = RPProvider.get(self.module.cat)(R, alpha=alpha)

        branch3x3dbl = RPProvider.get(self.module.branch3x3dbl_3)(branch3x3dbl, alpha=alpha)
        branch3x3dbl = RPProvider.get(self.module.branch3x3dbl_2)(branch3x3dbl, alpha=alpha)
        x2 = RPProvider.get(self.module.branch3x3dbl_1)(branch3x3dbl, alpha=alpha)

        x3 = RPProvider.get(self.module.branch3x3)(branch3x3, alpha=alpha)

        return x1 + x2 + x3


class InceptionD(nn.Module):

    def __init__(
        self,
        in_channels: int,
        pool_factory,
        conv_block: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(InceptionD, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d

        self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
        #self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
        self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=1, padding=1)
        #self.branch3x3_2_stride = get_pooler(pooler_cls, 320, 2, pooler_hidden_layers)

        self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
        self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
        self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
        #self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
        self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=1, padding=1)
        #self.branch7x7x3_4_stride = get_pooler(pooler_cls, 192, 2, pooler_hidden_layers)

        self.pool = pool_factory(320 + 192 + in_channels)

        self.cat = M.Cat()

    def _forward(self, x: Tensor) -> List[Tensor]:

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)

        branch7x7x3 = self.branch7x7x3_1(x)
        branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
        branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
        branch7x7x3 = self.branch7x7x3_4(branch7x7x3)

        outputs = [branch3x3, branch7x7x3, x]
        return outputs

    def forward(self, x: Tensor) -> Tensor:
        outputs = self._forward(x)
        return self.pool(self.cat(outputs, 1))


@RPProvider.register(InceptionD)
class InceptionDRelProp(RelProp[InceptionD]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        R = RPProvider.get(self.module.pool)(R, alpha=alpha)
        branch3x3, branch7x7x3, x1 = RPProvider.get(self.module.cat)(R, alpha=alpha)

        branch7x7x3 = RPProvider.get(self.module.branch7x7x3_4)(branch7x7x3, alpha=alpha)
        branch7x7x3 = RPProvider.get(self.module.branch7x7x3_3)(branch7x7x3, alpha=alpha)
        branch7x7x3 = RPProvider.get(self.module.branch7x7x3_2)(branch7x7x3, alpha=alpha)
        x2 = RPProvider.get(self.module.branch7x7x3_1)(branch7x7x3, alpha=alpha)

        branch3x3 = RPProvider.get(self.module.branch3x3_2)(branch3x3, alpha=alpha)
        x3 = RPProvider.get(self.module.branch3x3_1)(branch3x3, alpha=alpha)

        return x1 + x2 + x3


def inception_b_maker(pool_factory):
    return (
        lambda in_channels, conv_block=None:
        InceptionB(in_channels, pool_factory, conv_block))


def inception_d_maker(pool_factory):
    return (
        lambda in_channels, conv_block=None:
        InceptionD(in_channels, pool_factory, conv_block))


class InceptionA(nn.Module):

    def __init__(
        self,
        in_channels: int,
        pool_features: int,
        conv_block: Optional[Callable[..., nn.Module]] = None,
    ) -> None:
        super(InceptionA, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)

        self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
        self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)

        self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
        self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
        self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)

        self.avg_pool = torch.nn.AvgPool2d(kernel_size=3, stride=1, padding=1)
        self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
        self.cat = M.Cat()

    def _forward(self, x: Tensor) -> List[Tensor]:

        branch1x1 = self.branch1x1(x)

        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        branch_pool = self.avg_pool(x)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
        return outputs

    def forward(self, x: Tensor) -> Tensor:
        outputs = self._forward(x)
        return self.cat(outputs, 1)


@RPProvider.register(InceptionA)
class InceptionARelProp(RelProp[InceptionA]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        branch1x1, branch5x5, branch3x3dbl, branch_pool = RPProvider.get(self.module.cat)(R, alpha=alpha)

        branch_pool = RPProvider.get(self.module.branch_pool)(branch_pool, alpha=alpha)
        x1 = RPProvider.get(self.module.avg_pool)(branch_pool, alpha=alpha)

        branch3x3dbl = RPProvider.get(self.module.branch3x3dbl_3)(branch3x3dbl, alpha=alpha)
        branch3x3dbl = RPProvider.get(self.module.branch3x3dbl_2)(branch3x3dbl, alpha=alpha)
        x2 = RPProvider.get(self.module.branch3x3dbl_1)(branch3x3dbl, alpha=alpha)

        branch5x5 = RPProvider.get(self.module.branch5x5_2)(branch5x5, alpha=alpha)
        x3 = RPProvider.get(self.module.branch5x5_1)(branch5x5, alpha=alpha)

        x4 = RPProvider.get(self.module.branch1x1)(branch1x1, alpha=alpha)

        return x1 + x2 + x3 + x4


class InceptionC(nn.Module):

    def __init__(
        self,
        in_channels: int,
        channels_7x7: int,
        conv_block: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(InceptionC, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)

        c7 = channels_7x7
        self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
        self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
        self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))

        self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
        self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
        self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
        self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
        self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))

        self.avg_pool = torch.nn.AvgPool2d(kernel_size=3, stride=1, padding=1)
        self.branch_pool = conv_block(in_channels, 192, kernel_size=1)

        self.cat = M.Cat()

    def _forward(self, x: Tensor) -> List[Tensor]:

        branch1x1 = self.branch1x1(x)

        branch7x7 = self.branch7x7_1(x)
        branch7x7 = self.branch7x7_2(branch7x7)
        branch7x7 = self.branch7x7_3(branch7x7)

        branch7x7dbl = self.branch7x7dbl_1(x)
        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)

        branch_pool = self.avg_pool(x)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
        return outputs

    def forward(self, x: Tensor) -> Tensor:
        outputs = self._forward(x)
        return self.cat(outputs, 1)


@RPProvider.register(InceptionC)
class InceptionCRelProp(RelProp[InceptionC]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        branch1x1, branch7x7, branch7x7dbl, branch_pool = RPProvider.get(self.module.cat)(R, alpha=alpha)

        branch_pool = RPProvider.get(self.module.branch_pool)(branch_pool, alpha=alpha)
        x1 = RPProvider.get(self.module.avg_pool)(branch_pool, alpha=alpha)

        branch7x7dbl = RPProvider.get(self.module.branch7x7dbl_5)(branch7x7dbl, alpha=alpha)
        branch7x7dbl = RPProvider.get(self.module.branch7x7dbl_4)(branch7x7dbl, alpha=alpha)
        branch7x7dbl = RPProvider.get(self.module.branch7x7dbl_3)(branch7x7dbl, alpha=alpha)
        branch7x7dbl = RPProvider.get(self.module.branch7x7dbl_2)(branch7x7dbl, alpha=alpha)
        x2 = RPProvider.get(self.module.branch7x7dbl_1)(branch7x7dbl, alpha=alpha)

        branch7x7 = RPProvider.get(self.module.branch7x7_3)(branch7x7, alpha=alpha)
        branch7x7 = RPProvider.get(self.module.branch7x7_2)(branch7x7, alpha=alpha)
        x3 = RPProvider.get(self.module.branch7x7_1)(branch7x7, alpha=alpha)

        x4 = RPProvider.get(self.module.branch1x1)(branch1x1, alpha=alpha)

        return x1 + x2 + x3 + x4


class InceptionE(nn.Module):

    def __init__(
        self,
        in_channels: int,
        conv_block: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(InceptionE, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)

        self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
        self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
        self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))

        self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
        self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
        self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
        self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))

        self.avg_pool = torch.nn.AvgPool2d(kernel_size=3, stride=1, padding=1)
        self.branch_pool = conv_block(in_channels, 192, kernel_size=1)

        self.cat1 = M.Cat()
        self.cat2 = M.Cat()
        self.cat3 = M.Cat()

    def _forward(self, x: Tensor) -> List[Tensor]:

        branch1x1 = self.branch1x1(x)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = [
            self.branch3x3_2a(branch3x3),
            self.branch3x3_2b(branch3x3),
        ]
        branch3x3 = self.cat1(branch3x3, 1)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = [
            self.branch3x3dbl_3a(branch3x3dbl),
            self.branch3x3dbl_3b(branch3x3dbl),
        ]
        branch3x3dbl = self.cat2(branch3x3dbl, 1)

        branch_pool = self.avg_pool(x)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
        return outputs

    def forward(self, x: Tensor) -> Tensor:
        outputs = self._forward(x)
        return self.cat3(outputs, 1)


@RPProvider.register(InceptionE)
class InceptionERelProp(RelProp[InceptionE]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        branch1x1, branch3x3, branch3x3dbl, branch_pool = RPProvider.get(self.module.cat3)(R, alpha=alpha)

        branch_pool = RPProvider.get(self.module.branch_pool)(branch_pool, alpha=alpha)
        x1 = RPProvider.get(self.module.avg_pool)(branch_pool, alpha=alpha)

        branch3x3dbl_3a, branch3x3dbl_3b = RPProvider.get(self.module.cat2)(branch3x3dbl, alpha=alpha)
        branch3x3dbl_1 = RPProvider.get(self.module.branch3x3dbl_3a)(branch3x3dbl_3a, alpha=alpha)
        branch3x3dbl_2 = RPProvider.get(self.module.branch3x3dbl_3b)(branch3x3dbl_3b, alpha=alpha)
        branch3x3dbl = branch3x3dbl_1 + branch3x3dbl_2
        branch3x3dbl = RPProvider.get(self.module.branch3x3dbl_2)(branch3x3dbl, alpha=alpha)
        x2 = RPProvider.get(self.module.branch3x3dbl_1)(branch3x3dbl, alpha=alpha)

        branch3x3_2a, branch3x3_2b = RPProvider.get(self.module.cat1)(branch3x3, alpha=alpha)
        branch3x3_1 = RPProvider.get(self.module.branch3x3_2a)(branch3x3_2a, alpha=alpha)
        branch3x3_2 = RPProvider.get(self.module.branch3x3_2b)(branch3x3_2b, alpha=alpha)
        branch3x3 = branch3x3_1 + branch3x3_2
        x3 = RPProvider.get(self.module.branch3x3_1)(branch3x3, alpha=alpha)

        x4 = RPProvider.get(self.module.branch1x1)(branch1x1, alpha=alpha)

        return x1 + x2 + x3 + x4


class InceptionAux(nn.Module):

    def __init__(
        self,
        in_channels: int,
        num_classes: int,
        conv_block: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(InceptionAux, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d

        self.avgpool1 = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv0 = conv_block(in_channels, 128, kernel_size=1)
        self.conv1 = conv_block(128, 768, kernel_size=5, padding=2)
        self.conv1.stddev = 0.01  # type: ignore[assignment]
        self.avgpool2 = nn.AdaptiveAvgPool2d((1, 1))
        self.flatten = nn.Flatten()
        self.fc = nn.Linear(768, num_classes)
        self.fc.stddev = 0.001  # type: ignore[assignment]

    def forward(self, x: Tensor) -> Tensor:
        # N x 768 x 17 x 17
        x = self.avgpool1(x)
        # N x 768 x 5 x 5
        x = self.conv0(x)
        # N x 128 x 5 x 5
        x = self.conv1(x)
        # N x 768 x 1 x 1
        # Adaptive average pooling
        x = self.avgpool2(x)
        # N x 768 x 1 x 1
        x = self.flatten(x)
        # N x 768
        x = self.fc(x)
        # N x 1000
        return x


@RPProvider.register(InceptionAux)
class InceptionAuxRelProp(RelProp[InceptionAux]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        R = RPProvider.get(self.module.fc)(R, alpha=alpha)
        R = RPProvider.get(self.module.flatten)(R, alpha=alpha)
        R = RPProvider.get(self.module.avgpool2)(R, alpha=alpha)
        R = RPProvider.get(self.module.conv1)(R, alpha=alpha)
        R = RPProvider.get(self.module.conv0)(R, alpha=alpha)
        R = RPProvider.get(self.module.avgpool1)(R, alpha=alpha)
        return R


class BasicConv2d(nn.Module):

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        **kwargs: Any
    ) -> None:
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
        self.act = torch.nn.ReLU()

    def forward(self, x: Tensor) -> Tensor:
        x = self.conv(x)
        x = self.bn(x)
        return self.act(x)


@RPProvider.register(BasicConv2d)
class BasicConv2dRelProp(RelProp[BasicConv2d]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        R = RPProvider.get(self.module.act)(R, alpha=alpha)
        R = RPProvider.get(self.module.bn)(R, alpha=alpha)
        R = RPProvider.get(self.module.conv)(R, alpha=alpha)
        return R


class LAPInception(AttentionInterpretableModel, CamInterpretableModel, torchvision.models.Inception3):
   
    def __init__(self, aux_weight: float, n_classes, pool_factory, adaptive_pool_factory):
        torchvision.models.Inception3.__init__(
            self,
            transform_input=False, init_weights=False,
            inception_blocks=[
                BasicConv2d, InceptionA, inception_b_maker(pool_factory),
                InceptionC, inception_d_maker(pool_factory), InceptionE, InceptionAux
            ])

        self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2, padding=1)
        self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3, padding=1)
        self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
        self.maxpool1 = pool_factory(64)
        self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1,)
        self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3, padding=1)
        self.maxpool2 = pool_factory(192)

        if adaptive_pool_factory is not None:
            self.avgpool = nn.Sequential(
                adaptive_pool_factory(2048))

        self.fc = nn.Sequential(
            nn.Linear(2048, 1),
            nn.Sigmoid()
        )
        self.AuxLogits.fc = nn.Sequential(
            nn.Linear(768, 1),
            nn.Sigmoid()
        )
        self.aux_weight = aux_weight
    
    @property
    def target_conv_layers(self) -> List[nn.Module]:
        return [
            self.Mixed_7c.branch1x1,
            self.Mixed_7c.branch3x3_2a, self.Mixed_7c.branch3x3_2b,
            self.Mixed_7c.branch3x3dbl_3a, self.Mixed_7c.branch3x3dbl_3b,
            self.Mixed_7c.branch_pool
        ]

    @property
    def ordered_placeholder_names_to_be_interpreted(self) -> Iterable[str]:
        return ['x']

    def get_categorical_probabilities(self, *inputs, **kwargs) -> torch.Tensor:
        p = self.forward(*inputs, **kwargs)['positive_class_probability']
        return torch.stack([1 - p, p], dim=1)

    @property
    def attention_layers(self) -> Dict[str, List[LAP]]:
        attention_groups = {
            '0_layer2': [self.maxpool2],
            '1_layer6': [self.Mixed_6a.pool],
            '2_layer7': [self.Mixed_7a.pool],
            '3_avgpool': [self.avgpool[0]],
            '4_all': [
                self.maxpool2,
                self.Mixed_6a.pool,
                self.Mixed_7a.pool,
                self.avgpool[0]],
        }

        return attention_groups


@RPProvider.register(LAPInception)
class Inception3Mo4RelProp(RelProp[LAPInception]):

    def rel(self, R: torch.Tensor, alpha: float = 1) -> torch.Tensor:
        if RPProvider.get(self.module.fc).Y.shape[1] == 1:
            R = R[:, -1:]
        R = RPProvider.get(self.module.fc)(R, alpha=alpha)              # B 2048
        R = R.reshape_as(RPProvider.get(self.module.dropout).Y)         # B 2048    1   1
        R = RPProvider.get(self.module.dropout)(R, alpha=alpha)         # B 2048    1   1
        R = RPProvider.get(self.module.avgpool)(R, alpha=alpha)         # B 2048    8   8

        R = RPProvider.get(self.module.Mixed_7c)(R, alpha=alpha)        # B 2048    8   8
        R = RPProvider.get(self.module.Mixed_7b)(R, alpha=alpha)        # B 1280    8   8
        R = RPProvider.get(self.module.Mixed_7a)(R, alpha=alpha)        # B 768     17  17

        R = RPProvider.get(self.module.Mixed_6e)(R, alpha=alpha)        # B 768     17  17
        R = RPProvider.get(self.module.Mixed_6d)(R, alpha=alpha)        # B 768     17  17
        R = RPProvider.get(self.module.Mixed_6c)(R, alpha=alpha)        # B 768     17  17
        R = RPProvider.get(self.module.Mixed_6b)(R, alpha=alpha)        # B 768     17  17
        R = RPProvider.get(self.module.Mixed_6a)(R, alpha=alpha)        # B 288     35  35

        R = RPProvider.get(self.module.Mixed_5d)(R, alpha=alpha)        # B 288     35  35
        R = RPProvider.get(self.module.Mixed_5c)(R, alpha=alpha)        # B 256     35  35
        R = RPProvider.get(self.module.Mixed_5b)(R, alpha=alpha)        # B 192     35  35

        R = RPProvider.get(self.module.maxpool2)(R, alpha=alpha)        # B 192     71  71
        R = RPProvider.get(self.module.Conv2d_4a_3x3)(R, alpha=alpha)   # B 80      73  73
        R = RPProvider.get(self.module.Conv2d_3b_1x1)(R, alpha=alpha)   # B 64      73  73

        R = RPProvider.get(self.module.maxpool1)(R, alpha=alpha)        # B 64      147 147
        R = RPProvider.get(self.module.Conv2d_2b_3x3)(R, alpha=alpha)   # B 32      147 147
        R = RPProvider.get(self.module.Conv2d_2a_3x3)(R, alpha=alpha)   # B 32      149 149
        R = RPProvider.get(self.module.Conv2d_1a_3x3)(R, alpha=alpha)   # B 3       299 299
        return R