def f(inputs, params, stats, mode):
if opt.gamma:
y_s, y_t_auto, g_s = f_s(inputs, params, stats, mode,
'student.')
y_t, g_t = f_t(inputs, params, stats, False, 'teacher.')
return y_s, y_t_auto, y_t, [
at_loss(x, y) for x, y in zip(g_s, g_t)
]
else:
y_s, g_s = f_s(inputs, params, stats, mode, 'student.')
y_t, g_t = f_t(inputs, params, stats, False, 'teacher.')
return y_s, y_t, [at_loss(x, y) for x, y in zip(g_s, g_t)]
def train_student(net, teacher, optimizer, criterion, scheduler):
net.train()
pbar = tqdm(train_loader)
for images, labels in pbar:
images = Variable(images.to(device, dtype=torch.float32))
labels = Variable(labels.to(device, dtype=torch.long))
outputs_student, ints_student = net(images)
outputs_teacher, ints_teacher = teacher(images)
if opts.loss_type == 'both' or opts.loss_type == 'kd':
loss = utils.distillation(outputs_student, outputs_teacher, labels, opts.temperature, opts.alpha)
else:
loss = criterion(outputs_student, labels)
if opts.loss_type == 'both' or opts.loss_type == 'at' and opts.at_type != 'none':
adjusted_beta = (opts.beta*3)/len(ints_student)
for i in range(len(ints_student)):
if ints_teacher[i].shape[2] != ints_student[i].shape[2]:
ints_teacher[i] = F.interpolate(ints_teacher[i], size=ints_student[i].shape[2:], mode='bilinear', align_corners=False)
loss += adjusted_beta * utils.at_loss(ints_student[i], ints_teacher[i])
preds = outputs_student.detach().max(dim=1)[1].cpu().numpy()
targets = labels.cpu().numpy()
metrics.update(targets, preds)
score = metrics.get_results()
pbar.set_postfix({"IoU": score["Mean IoU"]})
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
def f(inputs, params, mode):
y_s, g_s = f_s(inputs, params, mode, 'student.')
with torch.no_grad():
y_t, g_t = f_t(inputs, params, False, 'teacher.')
return y_s, y_t, [
utils.at_loss(x, y) for x, y in zip(g_s, g_t)
]
def f(inputs, params, mode):
#y_s：学生网络最后的输出
#g_s：每个组输出的tuple
y_s, g_s = f_s(inputs, params, mode, 'student.')
with torch.no_grad():
#y_t：教师网络最后的输出
#g_t:教师网络每个组输出的tuple
y_t, g_t = f_t(inputs, params, False, 'teacher.')
#返回的是学生网络和教师网络最后的输出
#第三部分是师生网络attention的attention map结果
return y_s, y_t, [utils.at_loss(x, y) for x, y in zip(g_s, g_t)]
def f(inputs, params, stats, mode):
y_s, g_s = f_s(inputs, params, stats, mode, 'student.')
y_t, g_t = f_t(inputs, params, 'teacher.')
return y_s, y_t, [at_loss(x, y) for x, y in zip(g_s, g_t)]
