LAP/torchlap/models/tv_inception.py

from typing import Iterable, Optional, Callable, List

import torch
from torch import nn, Tensor
import torchvision

from ..interpreting.interpretable import CamInterpretableModel
from ..interpreting.relcam.relprop import RPProvider, RelProp
from ..interpreting.relcam import modules as M
from .lap_inception import (
    InceptionA,
    InceptionC,
    InceptionE,
    BasicConv2d,
    InceptionAux as BaseInceptionAux,
)


class InceptionB(nn.Module):

    def __init__(
        self,
        in_channels: int,
        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.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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
        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)

        branch_pool = self.maxpool(x)

        outputs = [branch3x3, branch3x3dbl, branch_pool]
        return outputs

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


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

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

        x1 = RPProvider.get(self.module.maxpool)(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)

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

        return x1 + x2 + x3


class InceptionD(nn.Module):

    def __init__(
        self,
        in_channels: int,
        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.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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
        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)

        branch_pool = self.maxpool(x)
        outputs = [branch3x3, branch7x7x3, branch_pool]
        return outputs

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


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

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

        x1 = RPProvider.get(self.module.maxpool)(branch_pool, 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


class InceptionAux(BaseInceptionAux):

    def __init__(
        self,
        in_channels: int,
        num_classes: int,
        conv_block: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super().__init__(in_channels, num_classes, conv_block=conv_block)
        if conv_block is None:
            conv_block = BasicConv2d
        self.conv1 = conv_block(128, 768, kernel_size=5)
        self.conv1.stddev = 0.01  # type: ignore[assignment]


class Inception3(CamInterpretableModel, torchvision.models.Inception3):

    def __init__(self, aux_weight: float, n_classes=1):
        torchvision.models.Inception3.__init__(self, 
            transform_input=False, init_weights=False,
            inception_blocks = [
                BasicConv2d, InceptionA, InceptionB, InceptionC,
                InceptionD, InceptionE, InceptionAux
            ])

        self.fc = nn.Sequential(
            nn.Linear(2048, n_classes),
            nn.Sigmoid()
        )

        self.AuxLogits.fc = nn.Sequential(
            nn.Linear(768, n_classes),
            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)



@RPProvider.register(Inception3)
class Inception3Mo4RelProp(RelProp[Inception3]):

    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