Problem with inputs using Integrated Gradients and model with embedding layers

I’m wondering how to use the Integrated Gradients with the following model as it has the embedding layers?

class TabularModel(nn.Module):
    def __init__(self, embedding_sizes, n_cont):
        super().__init__()
        self.embeddings = nn.ModuleList([nn.Embedding(categories, size) for categories, size in embedding_sizes])
        n_emb = sum(e.embedding_dim for e in self.embeddings)
        self.n_emb, self.n_cont = n_emb, n_cont
        self.lin1 = nn.Linear(self.n_emb + self.n_cont, 100)
        self.lin2 = nn.Linear(100, 50)
        self.lin3 = nn.Linear(50, 5)
        self.bn1 = nn.BatchNorm1d(self.n_cont)
        self.bn2 = nn.BatchNorm1d(100)
        self.bn3 = nn.BatchNorm1d(50)
        self.emb_drop = nn.Dropout(0.2)
        self.drops = nn.Dropout(0.1)

    def forward(self, x_cat, x_cont):
        x = [e(x_cat[:, i]) for i, e in enumerate(self.embeddings)]
        x = torch.cat(x, 1)
        x = self.emb_drop(x)
        x2 = self.bn1(x_cont)
        x = torch.cat([x, x2], 1)
        x = F.relu(self.lin1(x))
        x = self.drops(x)
        x = self.bn2(x)
        x = F.relu(self.lin2(x))
        x = self.drops(x)
        x = self.bn3(x)
        x = self.lin3(x)
        return x

The main issue is in passing the inputs to

ig = IntegratedGradients(model)
ig.attribute(inputs)

after which I get the error:

AssertionError: Baseline can be provided as a tensor for just one input and broadcasted to the batch or input and baseline must have the same shape or the baseline corresponding to each input tensor must be a scalar. Found baseline: tensor([[8.0000e+00, 1.0138e+03, 8.2027e+01,  ..., 1.4000e+01, 0.0000e+00,
         0.0000e+00],
        [8.0000e+00, 1.0161e+03, 8.7000e+01,  ..., 6.6700e+01, 0.0000e+00,
         0.0000e+00],
        [1.0000e+00, 1.0226e+03, 4.8000e+01,  ..., 2.4700e+01, 0.0000e+00,
         0.0000e+00],
        ...,
        [0.0000e+00, 1.0208e+03, 8.2000e+01,  ..., 1.2400e+01, 0.0000e+00,
         0.0000e+00],
        [7.0000e+00, 1.0142e+03, 9.8000e+01,  ..., 1.1000e+00, 1.0000e+00,
         0.0000e+00],
        [0.0000e+00, 1.0230e+03, 7.6000e+01,  ..., 3.6900e+01, 0.0000e+00,
         0.0000e+00]]) and input: tensor([[ 4,  8,  0,  3],
        [ 5,  8,  1, 15],
        [ 2, 13,  0, 29],
        ...,
        [ 5,  1,  0, 21],
        [ 0, 23,  0,  5],
        [ 5,  5,  4, 11]])

[karppmik]

Hi @vivekmig, thanks a lot for the reply! I was able to do the attributions with that approach. How would I go about visualising the results?

Referring to this tutorial, https://captum.ai/tutorials/House_Prices_Regression_Interpret, I don’t quite understand what the following lines do:

ig_attr_test_sum = ig_attr_test.detach().numpy().sum(0)
ig_attr_test_norm_sum = ig_attr_test_sum / np.linalg.norm(ig_attr_test_sum, ord=1)

In my case, ig_attr_test = ig.attribute(inputs=(embeddings_cat, test_set.features_cont), target=0) returns a tuple, and I’m not sure how to deal with that.

[JavierPerez21]

I am experiencing the same problem when trying to generate the attributions of the PyTorch ResNet model from https://pytorch.org/vision/stable/models.html. I have been trying to figure out if the forward function from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py needs to be changed but I think that the gradients can be computed for all its operations. Here’s the code for the ResNet class:

class ResNet(nn.Module):

    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]

    def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
                    stride: int = 1, dilate: bool = False) -> nn.Sequential:
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x: Tensor) -> Tensor:
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x: Tensor) -> Tensor:
        return self._forward_impl(x)

and I am trying to calculate the attributions with:

def attribute(attr_method, x, y, model, **kwargs):
    model.zero_grad()
    tensor_attributions = attr_method.attribute(inputs=x, target=y, **kwargs)
    return tensor_attributions

lig = ca.IntegratedGradients(nominal_model)
attributions = attribute(ig, x, y, nominal_model, baselines=toch.zeros_like(x), n_steps=20

which returns:

/usr/local/lib/python3.7/dist-packages/captum/_utils/common.py in _validate_input(inputs, baselines, draw_baseline_from_distrib)
                      " same shape or the baseline corresponding to each input tensor"
                      " must be a scalar. Found baseline: {} and input: {}".format(
                            baseline, input
                    )
                 )

AssertionError: Baseline can be provided as a tensor for just one input and broadcasted to the batch or input and baseline must have the same shape or the baseline corresponding to each input tensor must be a scalar.

Any ideas on how to fix this?