def replaceone(model, inputs, pred, classes):
losses = torch.zeros(inputs.size()[0],inputs.size()[1])
for i in range(inputs.size()[1]):
tempinputs = inputs.clone()
tempinputs[:,i]=2
with torch.no_grad():
tempoutput = model(tempinputs)
losses[:,i] = F.nll_loss(tempoutput, pred, reduce=False)
return losses
def grad(model, inputs, pred, classes):
losses1 = torch.zeros(inputs.size()[0], inputs.size()[2])
dloss = torch.zeros(inputs.size()[0], inputs.size()[2])
inputs1 = inputs.clone()
inputs1.requires_grad_(True)
output = model(inputs1)
loss = F.nll_loss(output, pred)
loss.backward()
score = inputs1.grad.norm(2, dim=1)
return score
def temporaltail(model, inputs, pred, classes):
losses1 = torch.zeros(inputs.size()[0],inputs.size()[1])
dloss = torch.zeros(inputs.size()[0],inputs.size()[1])
for i in range(inputs.size()[1]):
tempinputs = inputs[:,i:]
with torch.no_grad():
tempoutput = torch.exp(model(tempinputs))
losses1[:,i] = tempoutput.gather(1,pred.view(-1,1)).view(-1)
dloss[:,-1] = losses1[:,-1] - 1.0/classes
for i in range(inputs.size()[1]-1):
dloss[:,i] = losses1[:,i] - losses1[:,i+1]
return dloss
def temporal(model, inputs, pred, classes):
losses1 = torch.zeros(inputs.size()[0], inputs.size()[2])
dloss = torch.zeros(inputs.size()[0], inputs.size()[2])
for i in range(inputs.size()[2]):
tempinputs = inputs.clone()
if i != inputs.size()[2] - 1:
tempinputs[:, :, i + 1:].zero_()
with torch.no_grad():
tempoutput = torch.exp(model(tempinputs))
losses1[:, i] = tempoutput.gather(1, pred.view(-1, 1)).view(-1)
dloss[:, 0] = losses1[:, 0] - 1.0 / classes
for i in range(1, inputs.size()[1]):
dloss[:, i] = losses1[:, i] - losses1[:, i - 1]
return dloss
def grad(model, inputs, pred, classes):
losses1 = torch.zeros(inputs.size()[0],inputs.size()[1])
dloss = torch.zeros(inputs.size()[0],inputs.size()[1])
if isinstance(model,torch.nn.DataParallel):
model = model.module
model.train()
embd,output = model(inputs, returnembd = True)
# embd.retain_grad()
loss = F.nll_loss(output,pred)
loss.backward()
score = (inputs<=2).float()
score = -score
score = embd.grad.norm(2,dim=2) + score * 1e9
return score