Implementation of the paper "Shapley Explanation Networks" at ICLR 2021. Note that this repo heavily uses the experimental feature of named tensors in PyTorch. As it was really confusing to implement the ideas for the authors, we find it tremendously easier to use this feature.
For running only ShapNets, one would mostly only need PyTorch, NumPy, and SciPy.
import torch
import torch.nn as nn
from ShapNet.utils import ModuleDimensions
from ShapNet import ShapleyModule
b_size = 3
features = 4
out = 1
dims = ModuleDimensions(
features=features,
in_channel=1,
out_channel=out
)
sm = ShapleyModule(
inner_function=nn.Linear(features, out),
dimensions=dims
)
sm(torch.randn(b_size, features), explain=True)import torch
import torch.nn as nn
from ShapNet.utils import ModuleDimensions
from ShapNet import ShapleyModule, OverlappingShallowShapleyNetwork
batch_size = 32
class_num = 10
dim = 32
overlapping_modules = [
ShapleyModule(
inner_function=nn.Sequential(nn.Linear(2, class_num)),
dimensions=ModuleDimensions(
features=2, in_channel=1, out_channel=class_num
),
) for _ in range(dim * (dim - 1) // 2)
]
shallow_shapnet = OverlappingShallowShapleyNetwork(
list_modules=overlapping_modules
)
inputs = torch.randn(batch_size, dim, ), )
shallow_shapnet(torch.randn(batch_size, dim, ), )
output, bias = shallow_shapnet(inputs, explain=True, )import torch
import torch.nn as nn
from ShapNet.utils import ModuleDimensions
from ShapNet import ShapleyModule, ShallowShapleyNetwork, DeepShapleyNetwork
dim = 32
dim_input_channels = 1
class_num = 10
inputs = torch.randn(32, dim, ), )
dims = ModuleDimensions(
features=dim,
in_channel=dim_input_channels,
out_channel=class_num
)
deep_shapnet = DeepShapleyNetwork(
list_shapnets=[
ShallowShapleyNetwork(
module_dict=nn.ModuleDict({
"(0, 2)": ShapleyModule(
inner_function=nn.Linear(2, class_num),
dimensions=ModuleDimensions(
features=2, in_channel=1, out_channel=class_num
)
)},
),
dimensions=ModuleDimensions(dim, 1, class_num)
),
],
)
deep_shapnet(inputs)
outputs = deep_shapnet(inputs, explain=True, )import numpy as np
import torch
import torch.nn as nn
# =============================================================================
# Imports {\sc ShapNet}
# =============================================================================
from ShapNet import DeepConvShapNet, ShallowConvShapleyNetwork, ShapleyModule
from ShapNet.utils import ModuleDimensions, NAME_HEIGHT, NAME_WIDTH, \
process_list_sizes
num_channels = 3
num_classes = 10
height = 32
width = 32
list_channels = [3, 16, 10]
pruning = [0.2, 0.]
kernel_sizes = process_list_sizes([2, (1, 3), ])
dilations = process_list_sizes([1, 2])
paddings = process_list_sizes([0, 0])
strides = process_list_sizes([1, 1])
args = {
"list_shapnets": [
ShallowConvShapleyNetwork(
shapley_module=ShapleyModule(
inner_function=nn.Sequential(
nn.Linear(
np.prod(kernel_sizes[i]) * list_channels[i],
list_channels[i + 1]),
nn.LeakyReLU()
),
dimensions=ModuleDimensions(
features=int(np.prod(kernel_sizes[i])),
in_channel=list_channels[i],
out_channel=list_channels[i + 1])
),
reference_values=None,
kernel_size=kernel_sizes[i],
dilation=dilations[i],
padding=paddings[i],
stride=strides[i]
) for i in range(len(list_channels) - 1)
],
"reference_values": None,
"residual": False,
"named_output": False,
"pruning": pruning
}
dcs = DeepConvShapNet(**args)If this is useful, you could cite our work as
@inproceedings{
wang2021shapley,
title={Shapley Explanation Networks},
author={Rui Wang and Xiaoqian Wang and David I. Inouye},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=vsU0efpivw}
}