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import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = [
"forward_hook",
"Clone",
"Add",
"Cat",
"ReLU",
"GELU",
"Dropout",
"BatchNorm2d",
"Linear",
"MaxPool2d",
"AdaptiveAvgPool2d",
"AvgPool2d",
"Conv2d",
"Sequential",
"safe_divide",
"einsum",
"Softmax",
"IndexSelect",
"LayerNorm",
"AddEye",
"Tanh",
"MatMul",
"Mul",
]
def safe_divide(a, b):
den = b.clamp(min=1e-9) + b.clamp(max=1e-9)
den = den + den.eq(0).type(den.type()) * 1e-9
return a / den * b.ne(0).type(b.type())
def forward_hook(self, input, output):
if type(input[0]) in (list, tuple):
self.X = []
for i in input[0]:
x = i.detach()
x.requires_grad = True
self.X.append(x)
else:
self.X = input[0].detach()
self.X.requires_grad = True
self.Y = output
def backward_hook(self, grad_input, grad_output):
self.grad_input = grad_input
self.grad_output = grad_output
class RelProp(nn.Module):
def __init__(self):
super(RelProp, self).__init__()
# if not self.training:
self.register_forward_hook(forward_hook)
def gradprop(self, Z, X, S):
C = torch.autograd.grad(Z, X, S, retain_graph=True)
return C
def relprop(self, R, alpha):
return R
class RelPropSimple(RelProp):
def relprop(self, R, alpha):
Z = self.forward(self.X)
S = safe_divide(R, Z)
C = self.gradprop(Z, self.X, S)
if torch.is_tensor(self.X) == False:
outputs = []
outputs.append(self.X[0] * C[0])
outputs.append(self.X[1] * C[1])
else:
outputs = self.X * (C[0])
return outputs
class AddEye(RelPropSimple):
# input of shape B, C, seq_len, seq_len
def forward(self, input):
return input + torch.eye(input.shape[2]).expand_as(input).to(input.device)
class ReLU(nn.ReLU, RelProp):
pass
class Tanh(nn.Tanh, RelProp):
pass
class GELU(nn.GELU, RelProp):
pass
class Softmax(nn.Softmax, RelProp):
pass
class LayerNorm(nn.LayerNorm, RelProp):
pass
class Dropout(nn.Dropout, RelProp):
pass
class MaxPool2d(nn.MaxPool2d, RelPropSimple):
pass
class LayerNorm(nn.LayerNorm, RelProp):
pass
class AdaptiveAvgPool2d(nn.AdaptiveAvgPool2d, RelPropSimple):
pass
class MatMul(RelPropSimple):
def forward(self, inputs):
return torch.matmul(*inputs)
class Mul(RelPropSimple):
def forward(self, inputs):
return torch.mul(*inputs)
class AvgPool2d(nn.AvgPool2d, RelPropSimple):
pass
class Add(RelPropSimple):
def forward(self, inputs):
return torch.add(*inputs)
class einsum(RelPropSimple):
def __init__(self, equation):
super().__init__()
self.equation = equation
def forward(self, *operands):
return torch.einsum(self.equation, *operands)
class IndexSelect(RelProp):
def forward(self, inputs, dim, indices):
self.__setattr__("dim", dim)
self.__setattr__("indices", indices)
return torch.index_select(inputs, dim, indices)
def relprop(self, R, alpha):
Z = self.forward(self.X, self.dim, self.indices)
S = safe_divide(R, Z)
C = self.gradprop(Z, self.X, S)
if torch.is_tensor(self.X) == False:
outputs = []
outputs.append(self.X[0] * C[0])
outputs.append(self.X[1] * C[1])
else:
outputs = self.X * (C[0])
return outputs
class Clone(RelProp):
def forward(self, input, num):
self.__setattr__("num", num)
outputs = []
for _ in range(num):
outputs.append(input)
return outputs
def relprop(self, R, alpha):
Z = []
for _ in range(self.num):
Z.append(self.X)
S = [safe_divide(r, z) for r, z in zip(R, Z)]
C = self.gradprop(Z, self.X, S)[0]
R = self.X * C
return R
class Cat(RelProp):
def forward(self, inputs, dim):
self.__setattr__("dim", dim)
return torch.cat(inputs, dim)
def relprop(self, R, alpha):
Z = self.forward(self.X, self.dim)
S = safe_divide(R, Z)
C = self.gradprop(Z, self.X, S)
outputs = []
for x, c in zip(self.X, C):
outputs.append(x * c)
return outputs
class Sequential(nn.Sequential):
def relprop(self, R, alpha):
for m in reversed(self._modules.values()):
R = m.relprop(R, alpha)
return R
class BatchNorm2d(nn.BatchNorm2d, RelProp):
def relprop(self, R, alpha):
X = self.X
beta = 1 - alpha
weight = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3) / (
(
self.running_var.unsqueeze(0).unsqueeze(2).unsqueeze(3).pow(2)
+ self.eps
).pow(0.5)
)
Z = X * weight + 1e-9
S = R / Z
Ca = S * weight
R = self.X * (Ca)
return R
class Linear(nn.Linear, RelProp):
def relprop(self, R, alpha):
beta = alpha - 1
pw = torch.clamp(self.weight, min=0)
nw = torch.clamp(self.weight, max=0)
px = torch.clamp(self.X, min=0)
nx = torch.clamp(self.X, max=0)
def f(w1, w2, x1, x2):
Z1 = F.linear(x1, w1)
Z2 = F.linear(x2, w2)
S1 = safe_divide(R, Z1)
S2 = safe_divide(R, Z2)
C1 = x1 * torch.autograd.grad(Z1, x1, S1)[0]
C2 = x2 * torch.autograd.grad(Z2, x2, S2)[0]
return C1 + C2
activator_relevances = f(pw, nw, px, nx)
inhibitor_relevances = f(nw, pw, px, nx)
R = alpha * activator_relevances - beta * inhibitor_relevances
return R
class Conv2d(nn.Conv2d, RelProp):
def gradprop2(self, DY, weight):
Z = self.forward(self.X)
output_padding = self.X.size()[2] - (
(Z.size()[2] - 1) * self.stride[0]
- 2 * self.padding[0]
+ self.kernel_size[0]
)
return F.conv_transpose2d(
DY,
weight,
stride=self.stride,
padding=self.padding,
output_padding=output_padding,
)
def relprop(self, R, alpha):
if self.X.shape[1] == 3:
pw = torch.clamp(self.weight, min=0)
nw = torch.clamp(self.weight, max=0)
X = self.X
L = (
self.X * 0
+ torch.min(
torch.min(
torch.min(self.X, dim=1, keepdim=True)[0], dim=2, keepdim=True
)[0],
dim=3,
keepdim=True,
)[0]
)
H = (
self.X * 0
+ torch.max(
torch.max(
torch.max(self.X, dim=1, keepdim=True)[0], dim=2, keepdim=True
)[0],
dim=3,
keepdim=True,
)[0]
)
Za = (
torch.conv2d(
X, self.weight, bias=None, stride=self.stride, padding=self.padding
)
- torch.conv2d(
L, pw, bias=None, stride=self.stride, padding=self.padding
)
- torch.conv2d(
H, nw, bias=None, stride=self.stride, padding=self.padding
)
+ 1e-9
)
S = R / Za
C = (
X * self.gradprop2(S, self.weight)
- L * self.gradprop2(S, pw)
- H * self.gradprop2(S, nw)
)
R = C
else:
beta = alpha - 1
pw = torch.clamp(self.weight, min=0)
nw = torch.clamp(self.weight, max=0)
px = torch.clamp(self.X, min=0)
nx = torch.clamp(self.X, max=0)
def f(w1, w2, x1, x2):
Z1 = F.conv2d(
x1, w1, bias=None, stride=self.stride, padding=self.padding
)
Z2 = F.conv2d(
x2, w2, bias=None, stride=self.stride, padding=self.padding
)
S1 = safe_divide(R, Z1)
S2 = safe_divide(R, Z2)
C1 = x1 * self.gradprop(Z1, x1, S1)[0]
C2 = x2 * self.gradprop(Z2, x2, S2)[0]
return C1 + C2
activator_relevances = f(pw, nw, px, nx)
inhibitor_relevances = f(nw, pw, px, nx)
R = alpha * activator_relevances - beta * inhibitor_relevances
return R
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