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.avgpoolhandle2 = 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()