|
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'] |
|
|
|
|
|
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__() |
|
|
|
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): |
|
|
|
def forward(self, input): |
|
return input + torch.eye(input.shape[2]).expand_as(input).to(input.device) |
|
|
|
class ReLU(nn.ReLU, 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 AvgPool2d(nn.AvgPool2d, RelPropSimple): |
|
pass |
|
|
|
|
|
class Add(RelPropSimple): |
|
def forward(self, inputs): |
|
return torch.add(*inputs) |
|
|
|
def relprop(self, R, alpha): |
|
Z = self.forward(self.X) |
|
S = safe_divide(R, Z) |
|
C = self.gradprop(Z, self.X, S) |
|
|
|
a = self.X[0] * C[0] |
|
b = self.X[1] * C[1] |
|
|
|
a_sum = a.sum() |
|
b_sum = b.sum() |
|
|
|
a_fact = safe_divide(a_sum.abs(), a_sum.abs() + b_sum.abs()) * R.sum() |
|
b_fact = safe_divide(b_sum.abs(), a_sum.abs() + b_sum.abs()) * R.sum() |
|
|
|
a = a * safe_divide(a_fact, a.sum()) |
|
b = b * safe_divide(b_fact, b.sum()) |
|
|
|
outputs = [a, b] |
|
|
|
return outputs |
|
|
|
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 + Z2) |
|
S2 = safe_divide(R, Z1 + 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 |
