pytorch怎么抽取中间的特征或者梯度

for i, (input, target) in enumerate(trainloader):

# measure data loading time
data_time.update(time.time() - end)

input, target = input.cuda(), target.cuda()
if i==2:
def for_hook(module,input, output):
print('output values:',output)
handle2 = model.module.conv1.register_forward_hook(for_hook)

# compute output
output = model(input)
# output = output1*2

if i==2:
def variable_hook(grad):
print('grad:',grad)
hook_handle = output.register_hook(variable_hook)

# output2 = 2*output_
# output = 0.5*output2
loss = criterion(output, target)

 

 

# measure accuracy and record loss
prec = accuracy(output, target)[0]
losses.update(loss.item(), input.size(0))
top1.update(prec.item(), input.size(0))

# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
# print('output.grad:',output1.grad)

# print('input.grad:',input.grad)
# print('input.is_leaf:',input.is_leaf)
# output.register_hook(print)
# zz.backward()

 

# print("output.grad:",output.grad)
optimizer.step()

# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()

if i==2:
# print('the input is :', input)
print('the output is :', output)
hook_handle.remove()
handle2.remove()
# print('the target is :', target)
# print('parameters:',optimizer.param_groups)

if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec {top1.val:.3f}% ({top1.avg:.3f}%)'.format(
epoch, i, len(trainloader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1))

 

解释:

#定义构前向函数
def for_hook(module,input, output):
    print('output values:',output)
#什么要抽取的层 model.module.avgpool
handle2 = model.module.avgpool.register_forward_hook(for_hook) 
#前向，为勾函数准备
output = model(input)
#删除勾函数
handle2.remove()