def validate(val_loader, net):
top1 = [AverageMeter(), AverageMeter()]
top5 = [AverageMeter(), AverageMeter()]
# switch to evaluate mode
net.eval()
prefetcher = DataPrefetcher(val_loader)
inputs, labels = prefetcher.next()
with torch.no_grad():
while inputs is not None:
inputs = inputs.float().cuda()
labels = labels.cuda()
stu_outputs, tea_outputs = net(inputs)
pred_s = accuracy(stu_outputs[-1], labels, topk=(1, 5))
pred_t = accuracy(tea_outputs[-1], labels, topk=(1, 5))
top1[0].update(pred_s[0].item(), inputs.size(0))
top5[0].update(pred_s[1].item(), inputs.size(0))
top1[1].update(pred_t[0].item(), inputs.size(0))
top5[1].update(pred_t[1].item(), inputs.size(0))
inputs, labels = prefetcher.next()
return top1[0].avg, top5[0].avg, top1[1].avg, top5[1].avg
def cross_validation(x_train, t_train, x_val, t_val, x_test, t_test, iter_max,
random_state, alpha_vals):
K = t_train.shape[1] # number of classes
n = x_train.shape[1] # number of features
# initialize arrays
train_error = []
val_error = []
i = 0
# try different values of alpha
for alpha in alpha_vals:
print('--> alpha = %f' % alpha)
# initialize weights_list
weights = initialize_weights(K, n, random_state)
# gradient descent
gradient_descent(weights=weights,
x=x_train,
t=t_train,
iter_max=iter_max,
alpha=alpha)
train_error.append(cross_entropy(weights, x_train, t_train, alpha))
val_error.append(cross_entropy(weights, x_val, t_val, 0))
if i == 0: # initial optimal values
alpha_optimal = alpha
weights_optimal = weights
if len(train_error) != 1: # skip first alpha
if val_error[i - 1] < val_error[i]:
break
else:
alpha_optimal = alpha
weights_optimal = weights
i += 1
print('\n --> Training set size: ' + str(x_train.shape))
print('=== After applying logistic regression: ===')
print('Optimal alpha = ' + str(alpha_optimal))
print('Train: Error = ' +
str(cross_entropy(weights_optimal, x_train, t_train, alpha_optimal)))
print('Val: Error = ' +
str(cross_entropy(weights_optimal, x_val, t_val, 0)))
print('Val: Accuracy = ' + str(
accuracy(t_val.argmax(axis=1), predictions(x_val, weights_optimal))))
print('Test: Error = ' +
str(cross_entropy(weights_optimal, x_test, t_test, 0)))
print('Test: Accuracy = ' + str(
accuracy(t_test.argmax(axis=1), predictions(x_test, weights_optimal))))
return [weights_optimal, train_error, val_error]
def cross_validation(x_train, t_train, x_val, t_val, x_test, t_test, iter_max,
seed, alpha_vals, labels):
"""
Apply cross validation to find the optimal regularization parameter.
"""
# initialize arrays
train_error = []
val_error = []
i = 0
# try different values of alpha
for alpha in alpha_vals:
print('--> alpha = %f' % alpha)
# initialize weights_list
weights = initialize_weights(x_train.shape[1], seed)
# gradient descent
gradient_descent(x_train, t_train, weights, iter_max, alpha)
train_error.append(cross_entropy(weights, x_train, t_train, alpha))
val_error.append(cross_entropy(weights, x_val, t_val, 0))
if i == 0: # initial optimal values
alpha_optimal = alpha
weights_optimal = weights
if len(train_error) != 1: # skip first alpha
if val_error[i - 1] < val_error[i]:
break
#True
else:
alpha_optimal = alpha
weights_optimal = weights
i += 1
print('\n --> Training set size: ' + str(x_train.shape))
print('=== After applying logistic regression: ===')
print('Error: Optimal alpha = ' + str(alpha_optimal))
print('Train: Error = ' +
str(cross_entropy(weights_optimal, x_train, t_train, alpha_optimal)))
print('Val: Error = ' +
str(cross_entropy(weights_optimal, x_val, t_val, 0)))
print('Val: Accuracy = ' +
str(accuracy(t_val, predictions(x_val, weights_optimal))))
print('Test: Error = ' +
str(cross_entropy(weights_optimal, x_test, t_test, 0)))
print('Test: Accuracy = ' +
str(accuracy(t_test, predictions(x_test, weights_optimal))))
return [weights_optimal, train_error, val_error]
def accumulate_acc(output, target, task, meter):
if 'All' in output.keys(): # Single-headed model
meter.update(accuracy(output['All'], target), len(target))
else: # outputs from multi-headed (multi-task) model
for t, t_out in output.items():
inds = [i for i in range(len(task)) if task[i] == t] # The index of inputs that matched specific task
if len(inds) > 0:
t_out = t_out[inds]
t_target = target[inds]
meter.update(accuracy(t_out, t_target), len(inds))
return meter
def valid_epoch(self, epoch):
self.model.eval()
val_loss = []
val_acc = []
val_acc5 = []
with torch.no_grad():
for batch_idx, (sentences,
gt_topics) in enumerate(self.valid_loader):
sentences, gt_topics = sentences.to(self.device), gt_topics.to(
self.device)
pred_topics = self.model(sentences)
loss = self.cls_loss(pred_topics, gt_topics)
acc = accuracy(pred_topics, gt_topics)
val_loss.append(loss.item())
val_acc.append(acc[0].item())
val_acc5.append(acc[1].item())
self.writer.set_step(epoch, mode='val')
self.val_metrics.update('loss', np.mean(val_loss))
self.val_metrics.update('acc_1', np.mean(val_acc))
self.val_metrics.update('acc_5', np.mean(val_acc5))
return self.val_metrics.result()
def train_epoch(self, epoch):
self.model.train()
for batch_idx, (sentences, gt_topics) in enumerate(self.train_loader):
sentences = sentences.to(self.device)
gt_topics = gt_topics.to(self.device)
pred_topics = self.model(sentences)
loss = self.cls_loss(pred_topics, gt_topics)
acc = accuracy(pred_topics, gt_topics)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# add loss summary when update generator to save memory
self.writer.set_step(
(epoch - 1) * len(self.train_loader) + batch_idx, mode='train')
self.train_metrics.update('loss', loss.item())
self.train_metrics.update('acc_1', acc[0].item())
self.train_metrics.update('acc_5', acc[1].item())
# log on console
if batch_idx % self.config.summary_step == 0:
self.logger.info(
'Train Epoch: {} {} Loss:{:.4f}, Acc_1: {:.2f}, Acc_5: {:.2f}]'
.format(epoch, self._progress(batch_idx), loss.item(),
acc[0].item(), acc[1].item()))
self.lr_scheduler.step()
log = self.train_metrics.result()
val_log = self.valid_epoch(epoch)
log.update(**{'val_' + k: v for k, v in val_log.items()})
return log
def main(args):
if args.seed >= 0:
random_seed(args.seed)
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
# train_y = get_one_hot(train_y, 2)
net = Net([Dense(100), ReLU(), Dense(30), ReLU(), Dense(1)])
model = Model(net=net,
loss=SigmoidCrossEntropy(),
optimizer=Adam(lr=args.lr))
iterator = BatchIterator(batch_size=args.batch_size)
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
pred = model.forward(batch.inputs)
loss, grads = model.backward(pred, batch.targets)
model.apply_grad(grads)
loss_list.append(loss)
print("Epoch %d time cost: %.4f" % (epoch, time.time() - t_start))
# evaluate
model.set_phase("TEST")
test_y_idx = np.asarray(test_y).reshape(-1)
test_pred = model.forward(test_x)
test_pred[test_pred > 0] = 1
test_pred[test_pred <= 0] = 0
test_pred_idx = test_pred.reshape(-1)
res = accuracy(test_pred_idx, test_y_idx)
print(res)
model.set_phase("TRAIN")
def test(self, epoch):
self.model.eval()
top1 = AverageMeter()
all_result = []
for batch_idx, data in enumerate(self.test_dataloader):
images, labels, images_path = data['images'], data['labels'], data[
'images_path']
if self.use_cuda:
images, labels = images.cuda(), labels.cuda()
outputs = self.model(images)
prec1 = accuracy(outputs.data, labels.data, topk=(1, ))
top1.update(prec1[0].detach().item(), images.size(0))
self.writer.add_scalar('test/acc', top1.val, self.iters)
if self.args.is_save:
probs, preds = outputs.softmax(dim=1).max(dim=1)
probs, preds = probs.view(-1), preds.view(-1)
for idx in range(images.size(0)):
result = '{}\t{}\t{}\t{}\n'.format(images_path[idx],
labels[idx].item(),
preds[idx].item(),
probs[idx].item())
all_result.append(result)
if self.args.is_save:
with open('result.txt', 'w') as f:
f.writelines(all_result)
self.acc = top1.avg
print('Test epoch:{}, acc:{}'.format(epoch, top1.avg))
def train(self, epoch):
self.model.train()
losses = AverageMeter()
top1 = AverageMeter()
pbar = tqdm(self.train_dataloader)
for batch_idx, data in enumerate(pbar):
self.iters += 1
images, labels = data['images'], data['labels']
if self.use_cuda:
images, labels = images.cuda(), labels.cuda()
outputs = self.model(images)
loss = self.criterion(outputs, labels)
self.optimizer.zero_grad()
loss.backward(loss.data)
self.optimizer.step()
if self.args.optimizer_type == 'sgd':
adjust_learning_rate(self.optimizer, self.args.base_lr, epoch,
self.args.lr_decay_epoch)
prec1 = accuracy(outputs.data, labels.data, topk=(1, ))
loss = loss.view(-1)
losses.update(loss.data[0], images.size(0))
top1.update(prec1[0].detach().item(), images.size(0))
self.writer.add_scalar('train/loss', loss.data[0], self.iters)
self.writer.add_scalar('train/acc', top1.val, self.iters)
if batch_idx % self.args.log_interval == 0:
print('epoch:{}, iter:{}/{}, loss:{}, acc:{}'.format(
epoch, batch_idx, self.iters, losses.avg,
round(top1.avg, 6)))
losses.reset(), top1.reset()
def train():
epoch_loss, epoch_acc = np.zeros(2)
# Sets the module in training mode, only on modules such as Dropout or BatchNorm.
model.train()
stamp = time.time()
for partial_epoch, (volume, label) in enumerate(train_data_loader, 1):
volume_var = Variable(volume).float().cuda()
label_var = Variable(label).cuda()
out = model(volume_var)
loss = nll_loss(out, label_var)
optimizer.zero_grad()
loss.backward()
optimizer.step()
acc = accuracy(out, label_var)
epoch_loss += loss.data[0]
epoch_acc += acc.data[0]
consume = time.time() - stamp
avg_loss, avg_acc = np.array([epoch_loss, epoch_acc]) / partial_epoch
print('===> Training epoch: {:.2f}/{}\t'.format(i / train_compress, TRAIN.expect_epoch),
'Loss: {:.5f} | Accuracy: {:.5f}'.format(avg_loss, avg_acc),
' | Elapsed: {:.3f}s / batch({})'.format(consume / len(train_data_loader), TRAIN.batch_size))
return avg_loss, avg_acc, volume
def train_epoch(self, epoch):
self.model.train()
# add free adversarial learning to improve generalization ability of the model and robust to noise
for batch_idx, (images, labels) in enumerate(self.train_loader):
images = images.to(self.device)
labels = labels.to(self.device)
noise = Variable(self.noise[0:images.size(0)],
requires_grad=True).to(self.device)
noisy_images = images + noise
noisy_images.clamp_(0, 1.0)
self.norm.do(noisy_images)
outputs = self.model(noisy_images)
loss = self.cls_loss(outputs, labels)
acc = accuracy(outputs, labels)
self.optimizer.zero_grad()
loss.backward()
# Update the noise for the next iteration
pert = self.config.fgsm_step * torch.sign(noise.grad)
self.noise[:images.size(0)] += pert.data
self.noise.clamp_(-self.config.clip_eps, self.config.clip_eps)
self.optimizer.step()
self.lr_scheduler.step()
# add loss summary when update generator to save memory
self.writer.set_step(
(epoch - 1) * len(self.train_loader) + batch_idx, mode='train')
self.train_metrics.update('loss', loss.item())
self.train_metrics.update('acc_avg', np.mean(acc))
for acc, attr in zip(acc, self.config.attrs):
self.train_metrics.update('acc_' + attr, acc)
# log on console
if batch_idx % self.config.summary_step == 0:
self.logger.info(
'Train Epoch: {} {} Loss:{:.4f}, Acc: {:.2f}]'.format(
epoch, self._progress(batch_idx), loss.item(),
np.mean(acc)))
log = self.train_metrics.result()
val_log = self.valid_epoch(epoch)
log.update(**{'val_' + k: v for k, v in val_log.items()})
return log
def train(train_loader, model, criterion, optimizer, epoch, cfgs):
logger = logging.getLogger('{}.train'.format(cfgs['log_name']))
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(train_loader),
[batch_time, data_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
images = images.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % cfgs['print_freq'] == 0:
logger.info(progress.display(i))
def validate(val_loader, model, criterion, cfgs):
logger = logging.getLogger('{}.validate'.format(cfgs['log_name']))
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
batch_time.update(time.time() - end)
end = time.time()
if i % cfgs['print_freq'] == 0:
logger.info(progress.display(i))
# TODO: this should also be done with the ProgressMeter
logger.info(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(
top1=top1, top5=top5))
return top1.avg
def training(self, epoch):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
self.model.train()
for i, (input, target) in tqdm(enumerate(self.train_loader), total=len(self.train_loader)):
output = self.model(input)
loss = self.criterion(output, target)
# print(output) # Tensor(shape=[256, 1000]
prec1, prec5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.numpy()[0], input.shape[0])
top1.update(prec1.numpy()[0], input.shape[0])
top5.update(prec5.numpy()[0], input.shape[0])
self.optimizer.clear_grad()
loss.backward()
self.optimizer.step()
if i % self.cfg.Log_print_freq == 0:
self.logger.info('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(self.train_loader), loss=losses, top1=top1, top5=top5))
prec1, prec5 = self.validate()
if self.cfg.visualDL:
with LogWriter(logdir=self.logDir) as writer:
# 使用scalar组件记录一个标量数据
writer.add_scalar(tag="loss", step=epoch, value=losses.avg)
writer.add_scalar(tag="prec1", step=epoch, value=prec1)
writer.add_scalar(tag="prec5", step=epoch, value=prec5)
self.logger.info("Epoch {}: prec1: {} prec5: {}".format(epoch, prec1, prec5))
return prec1, prec5, losses
def validate(val_loader, net):
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
net.eval()
prefetcher = DataPrefetcher(val_loader)
inputs, labels = prefetcher.next()
with torch.no_grad():
while inputs is not None:
inputs = inputs.float().cuda()
labels = labels.cuda()
stu_outputs, _ = net(inputs)
pred1, pred5 = accuracy(stu_outputs[-1], labels, topk=(1, 5))
top1.update(pred1.item(), inputs.size(0))
top5.update(pred5.item(), inputs.size(0))
inputs, labels = prefetcher.next()
return top1.avg, top5.avg
def validate(testloader, model, gpu, size):
model.eval()
acc = 0
acc_cnt = 0
with torch.no_grad():
for idx, data in enumerate(testloader):
if size is None or idx < size:
data, target, task = data
if gpu:
with torch.no_grad():
data = data.cuda()
target = target.cuda()
outputs = model.forward(data, task)
acc += accuracy(outputs, target)
acc_cnt += 1
else:
break
return acc / acc_cnt
def validate(self):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
self.model.eval()
for i, (input, target) in enumerate(self.val_loader):
output = self.model(input)
loss = self.criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.numpy()[0], input.shape[0])
top1.update(prec1.numpy()[0], input.shape[0])
top5.update(prec5.numpy()[0], input.shape[0])
self.logger.info(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg, top5.avg
def test():
epoch_loss, epoch_acc = np.zeros(2)
# Sets the module in training mode, only on modules such as Dropout or BatchNorm.
model.eval()
for partial_epoch, (volume, label) in enumerate(test_data_loader, 1):
volume_var = Variable(volume, volatile=True).float().cuda()
label_var = Variable(label, volatile=True).cuda()
out = model(volume_var)
loss = nll_loss(out, label_var)
acc = accuracy(out, label_var)
epoch_loss += loss.data[0]
epoch_acc += acc.data[0]
avg_loss, avg_acc = np.array([epoch_loss, epoch_acc]) / partial_epoch
if i % TRAIN.val_interval == 0:
print('{}\n===> Validation | '.format('-' * 130),
'Loss: {:.5f} | Accuracy: {:.5f}'.format(avg_loss, avg_acc),
'Current time: {}\n{}'.format(time.strftime('%Y-%m-%d %H:%M:%S'), '-' * 130))
return avg_loss, avg_acc, volume
def valid_epoch(self, epoch):
self.model.eval()
val_loss = []
val_acc = []
with torch.no_grad():
for batch_idx, (images, labels) in enumerate(self.valid_loader):
images, labels = images.to(self.device), labels.to(self.device)
outputs = self.model(images)
loss = self.cls_loss(outputs, labels)
acc = accuracy(outputs, labels)
val_loss.append(loss.item())
val_acc.append(acc)
self.writer.set_step(epoch, mode='val')
self.val_metrics.update('loss', np.mean(val_loss))
attr_acc = np.mean(val_acc, axis=0)
self.val_metrics.update('acc_avg', np.mean(attr_acc))
for acc, attr in zip(attr_acc, self.config.attrs):
self.val_metrics.update('acc_' + attr, acc)
return self.val_metrics.result()
def test_resnet():
model = Resnet50_new()
top1 = AverageMeter()
top5 = AverageMeter()
accu_20 = []
for i in range(80):
accu_20.append(AverageMeter())
for step, sample in enumerate(gd.val_loader):
weight = sample[0].shape[0]
s = Variable(sample[0].cuda())
pre = model(s)
prec1, prec5 = accuracy(pre.data, sample[1].cuda(), topk=(1, 5))
top1.update(prec1[0], n=weight)
top5.update(prec5[0], n=weight)
update_class_acc(accu_20, pre.data, sample[1].cuda())
print("Step: {step}, top1: {top1.avg:.3f}({top1.val:.3f}), "
"top5: {top5.avg:.3f}({top5.val:.3f})".format(step=step, top1=top1, top5=top5))
for k, j in enumerate(accu_20):
print("{k}: {top1.avg:.3f}({top1.val:.3f}), ".format(k=k, top1=j))
def train(train_loader,
model,
optimizer,
scheduler,
epoch,
args,
streams=None,
scaler=None):
"""training function"""
batch_time = metric.AverageMeter('Time', ':6.3f')
data_time = metric.AverageMeter('Data', ':6.3f')
avg_ce_loss = metric.AverageMeter('ce_loss', ':.4e')
avg_cot_loss = metric.AverageMeter('cot_loss', ':.4e')
# record the top1 accuray of each small network
top1_all = []
for i in range(args.loop_factor):
# ce_losses_l.append(metric.AverageMeter('{}_CE_Loss'.format(i), ':.4e'))
top1_all.append(metric.AverageMeter('{}_Acc@1'.format(i), ':6.2f'))
avg_top1 = metric.AverageMeter('Avg_Acc@1', ':6.2f')
#if args.dataset == 'imagenet':
# avg_top5 = metric.AverageMeter('Avg_Acc@1', ':6.2f')
# show all
total_iters = len(train_loader)
progress = metric.ProgressMeter(total_iters,
batch_time,
data_time,
avg_ce_loss,
avg_cot_loss,
*top1_all,
avg_top1,
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
# prefetch data
prefetcher = prefetch.data_prefetcher(train_loader)
images, target = prefetcher.next()
i = 0
"""Another way to load the data
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
"""
optimizer.zero_grad()
while images is not None:
# measure data loading time
data_time.update(time.time() - end)
# adjust the lr first
scheduler(optimizer, i, epoch)
i += 1
# compute outputs and losses
if args.is_amp:
# Runs the forward pass with autocasting.
with amp.autocast():
ensemble_output, outputs, ce_loss, cot_loss = model(
images,
target=target,
mode='train',
epoch=epoch,
streams=streams)
else:
ensemble_output, outputs, ce_loss, cot_loss = model(
images,
target=target,
mode='train',
epoch=epoch,
streams=streams)
# measure accuracy and record loss
batch_size_now = images.size(0)
# notice the index i and j, avoid contradictory
for j in range(args.loop_factor):
acc1 = metric.accuracy(outputs[j, ...], target, topk=(1, ))
top1_all[j].update(acc1[0].item(), batch_size_now)
# simply average outputs of small networks
avg_acc1 = metric.accuracy(ensemble_output, target, topk=(1, ))
avg_top1.update(avg_acc1[0].item(), batch_size_now)
# avg_top5.update(avg_acc1[0].item(), batch_size_now)
avg_ce_loss.update(ce_loss.mean().item(), batch_size_now)
avg_cot_loss.update(cot_loss.mean().item(), batch_size_now)
# compute gradient and do SGD step
total_loss = (ce_loss + cot_loss) / args.iters_to_accumulate
if args.is_amp:
# Scales loss. Calls backward() on scaled loss to create scaled gradients.
# Backward passes under autocast are not recommended.
# Backward ops run in the same dtype autocast chose for corresponding forward ops.
scaler.scale(total_loss).backward()
if i % args.iters_to_accumulate == 0 or i == total_iters:
# scaler.step() first unscales the gradients of the optimizer's assigned params.
# If these gradients do not contain infs or NaNs, optimizer.step() is then called,
# otherwise, optimizer.step() is skipped.
scaler.step(optimizer)
# Updates the scale for next iteration.
scaler.update()
optimizer.zero_grad()
else:
total_loss.backward()
if i % args.iters_to_accumulate == 0 or i == total_iters:
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.multiprocessing_distributed or (args.rank %
args.ngpus_per_node == 0):
if i % (args.print_freq * args.iters_to_accumulate) == 0:
progress.print(i)
images, target = prefetcher.next()
# save model
new_path = os.path.join(loc.densenet_dir, "version")
if not os.path.exists(new_path):
os.mkdir(new_path)
torch.save(densenet, os.path.join(new_path, "model_best.pth.tar"))
# evaluate on val
densenet = torch.load(loc.densenet_model_dir)
top1 = AverageMeter()
top5 = AverageMeter()
accu_20 = []
for i in range(80):
accu_20.append(AverageMeter())
for step, sample in enumerate(gd.val_loader):
weight = sample[0].shape[0]
s = Variable(sample[0].cuda())
pre = densenet(s)
prec1, prec5 = accuracy(pre.data, sample[1].cuda(), topk=(1, 5))
top1.update(prec1[0], n=weight)
top5.update(prec5[0], n=weight)
update_class_acc(accu_20, pre.data, sample[1].cuda())
print("Step: {step}, top1: {top1.avg:.3f}({top1.val:.3f}), "
"top5: {top5.avg:.3f}({top5.val:.3f})".format(step=step,
top1=top1,
top5=top5))
# for k, j in enumerate(accu_20):
# print("{k}: {top1.avg:.3f}({top1.val:.3f}), ".format(k=k, top1=j))
def train(train_loader, net, discriminator, criterion, optimizer_logit,
scheduler_logit, optimizer_s_fmap, scheduler_s_fmap,
optimizer_t_fmap, scheduler_t_fmap, epoch, logger):
top1 = [AverageMeter(), AverageMeter()]
top5 = [AverageMeter(), AverageMeter()]
loss_total = [AverageMeter(), AverageMeter()]
# switch to train mode
net.train()
iters = len(train_loader.dataset) // Config.batch_size
prefetcher = DataPrefetcher(train_loader) # 加速数据读取
inputs, labels = prefetcher.next()
iter = 1
while inputs is not None:
inputs, labels = inputs.float().cuda(), labels.cuda()
# Adversarial ground truths
valid = Variable(torch.cuda.FloatTensor(inputs.shape[0], 1, 1,
1).fill_(1.0),
requires_grad=False)
fake = Variable(torch.cuda.FloatTensor(inputs.shape[0], 1, 1,
1).fill_(0.0),
requires_grad=False)
# -----------------
# Train Generator
# -----------------
# zero the parameter gradients
# student
optimizer_logit[0].zero_grad() # s_g_logit
optimizer_s_fmap[0].zero_grad() # s_g_fmap
# teacher
optimizer_logit[1].zero_grad() # t_g_logit
optimizer_t_fmap[0].zero_grad() # t_g_fmap
# forward + backword + optimize
stu_outputs, tea_outputs = net(inputs) # [x1, x2, x3, x4, x]
# student
loss_stu_logit = criterion[0](stu_outputs[-1], labels) + criterion[1](
stu_outputs[-1], tea_outputs[-1].detach())
loss_stu_g = criterion[2](valid,
discriminator.discri_s(stu_outputs[-2]))
loss_stu = loss_stu_logit + loss_stu_g
# teacher
loss_tea_logit = criterion[0](tea_outputs[-1], labels) + criterion[1](
tea_outputs[-1], stu_outputs[-1].detach())
loss_tea_g = criterion[2](valid,
discriminator.discri_t(tea_outputs[-2]))
loss_tea = loss_tea_logit + loss_tea_g
# student
loss_stu.backward()
optimizer_logit[0].step() # s_g_logit
optimizer_s_fmap[0].step() # s_g_fmap
# teacher
loss_tea.backward()
optimizer_logit[1].step() # t_g_logit
optimizer_t_fmap[0].step() # t_g_fmap
# ---------------------
# Train Discriminator
# ---------------------
optimizer_s_fmap[1].zero_grad() # s_d
optimizer_t_fmap[1].zero_grad() # t_d
# discriminator loss
# student
loss_stu_d = criterion[2](valid, discriminator.discri_s(tea_outputs[-2].detach())) + \
criterion[2](fake, discriminator.discri_s(stu_outputs[-2].detach()))
# teacher
loss_tea_d = criterion[2](valid, discriminator.discri_t(stu_outputs[-2].detach())) + \
criterion[2](fake, discriminator.discri_t(tea_outputs[-2].detach()))
# student
loss_stu_d.backward()
optimizer_s_fmap[1].step()
# teacher
loss_tea_d.backward()
optimizer_t_fmap[1].step()
# student
prec_s = accuracy(stu_outputs[-1], labels, topk=(1, 5))
top1[0].update(prec_s[0].item(), inputs.size(0))
top5[0].update(prec_s[1].item(), inputs.size(0))
loss_total[0].update(loss_stu.item(), inputs.size(0))
# teacher
prec_t = accuracy(tea_outputs[-1], labels, topk=(1, 5))
top1[1].update(prec_t[0].item(), inputs.size(0))
top5[1].update(prec_t[1].item(), inputs.size(0))
loss_total[1].update(loss_tea.item(), inputs.size(0))
inputs, labels = prefetcher.next() # 取下一批数据
if iter % 20 == 0:
loss_log = f"train: epoch {epoch:0>3d}, iter [{iter:0>4d}, {iters:0>4d}]\n"
loss_log += f"Student detail:\n "
loss_log += f"top1 acc: {prec_s[0]:.2f}%, top5 acc: {prec_s[1]:.2f}%, "
loss_log += f"loss_total: {loss_stu.item():3f}, "
loss_log += f"loss_logit: {loss_stu_logit.item():3f} "
loss_log += f"loss_g: {loss_stu_g.item():3f} "
loss_log += f"loss_d: {loss_stu_d.item():3f} "
loss_log += f"\nTeacher detail:\n "
loss_log += f"top1 acc: {prec_t[0]:.2f}%, top5 acc: {prec_t[1]:.2f}%, "
loss_log += f"loss_total: {loss_tea.item():3f}, "
loss_log += f"loss_logit: {loss_tea_logit.item():3f} "
loss_log += f"loss_g: {loss_tea_g.item():3f} "
loss_log += f"loss_d: {loss_tea_d.item():3f} "
logger.info(loss_log)
iter += 1
scheduler_logit[0].step()
scheduler_s_fmap[0].step()
scheduler_logit[1].step()
scheduler_t_fmap[0].step()
scheduler_s_fmap[1].step()
scheduler_t_fmap[1].step()
return top1[0].avg, top1[1].avg, top5[0].avg, top5[1].avg, loss_total[
0].avg, loss_total[1].avg
def accumulate_acc(output, target, meter):
# Single-headed model
meter.update(accuracy(output, target), len(target))
return meter
def train_baseline(train_loader, net, criterion, optimizer, scheduler, epoch,
logger):
top1 = [AverageMeter(), AverageMeter()]
top5 = [AverageMeter(), AverageMeter()]
loss_total = [AverageMeter(), AverageMeter()]
# switch to train mode
net.train()
iters = len(train_loader.dataset) // Config.batch_size
prefetcher = DataPrefetcher(train_loader) # 加速数据读取
inputs, labels = prefetcher.next()
iter = 1
while inputs is not None:
inputs, labels = inputs.float().cuda(), labels.cuda()
# zero the parameter gradients
optimizer[0].zero_grad()
optimizer[1].zero_grad()
# forward + backword + optimize
stu_outputs, tea_outputs = net(inputs) # [x1, x2, x3, x4, x]
loss_stu = criterion(stu_outputs[-1], labels)
loss_tea = criterion(tea_outputs[-1], labels)
loss_stu.backward()
loss_tea.backward()
optimizer[0].step()
optimizer[1].step()
# student
prec_s = accuracy(stu_outputs[-1], labels, topk=(1, 5))
top1[0].update(prec_s[0].item(), inputs.size(0))
top5[0].update(prec_s[1].item(), inputs.size(0))
loss_total[0].update(loss_stu.item(), inputs.size(0))
# teacher
prec_t = accuracy(tea_outputs[-1], labels, topk=(1, 5))
top1[1].update(prec_t[0].item(), inputs.size(0))
top5[1].update(prec_t[1].item(), inputs.size(0))
loss_total[1].update(loss_tea.item(), inputs.size(0))
inputs, labels = prefetcher.next() # 取下一批数据
if iter % 20 == 0:
loss_log = f"train: epoch {epoch:0>3d}, iter [{iter:0>4d}, {iters:0>4d}]\n"
loss_log += f"Student detail:\n "
loss_log += f"top1 acc: {prec_s[0]:.2f}%, top5 acc: {prec_s[1]:.2f}%, "
loss_log += f"loss_s: {loss_stu.item():3f}, "
loss_log += f"\nTeacher detail:\n "
loss_log += f"top1 acc: {prec_t[0]:.2f}%, top5 acc: {prec_t[1]:.2f}%, "
loss_log += f"loss_t: {loss_tea.item():3f}, "
logger.info(loss_log)
iter += 1
scheduler[0].step()
scheduler[1].step()
return top1[0].avg, top1[1].avg, top5[0].avg, top5[1].avg, loss_total[
0].avg, loss_total[1].avg
def validation(self, test_loader, from_train=1):
# this might possibly change for other incremental scenario
# This function doesn't distinguish tasks.
batch_timer = Timer()
acc = AverageMeter()
losses = AverageMeter()
acc_5 = AverageMeter()
acc_class = [
AverageMeter()
for i in range(len(self.train_loader.dataset.class_list))
] #[AverageMeter()] * len(self.train_loader.dataset.class_list)
acc_class_5 = [
AverageMeter()
for i in range(len(self.train_loader.dataset.class_list))
]
batch_timer.tic()
orig_mode = self.training
self.eval()
for i, (input, target) in enumerate(test_loader):
if self.gpu:
with torch.no_grad():
input = input.cuda()
target = target.cuda()
output = self.forward(input)
loss = self.criterion(output, target)
losses.update(loss, input.size(0))
# Summarize the performance of all tasks, or 1 task, depends on dataloader.
# Calculated by total number of data.
t_acc, acc_class = accuracy(
output, target, topk=(1, ), avg_meters=acc_class
) #self.accumulate_acc(output, target, acc)
t_acc_5, acc_class_5 = accuracy(output,
target,
topk=(5, ),
avg_meters=acc_class_5)
# import pdb; pdb.set_trace()
acc.update(t_acc, len(target))
acc_5.update(t_acc_5, len(target))
class_list = self.train_loader.dataset.class_list.inverse
acc_cl_1 = {}
acc_cl_5 = {}
#from accuracies obtained create inst size based accuracies
inst_clss_lst = self.train_loader.dataset.class_inst_list
# import pdb; pdb.set_trace()
for ins_clss_, insts in inst_clss_lst.items():
cls_sum = sum([acc_class[inst].sum for inst in insts])
cls_cnt = sum([acc_class[inst].count for inst in insts])
if cls_cnt == 0:
import pdb
pdb.set_trace()
inst_avg = cls_sum / cls_cnt
self.writer.add_scalar(self.str_ + '/Acc_1_{}'.format(ins_clss_),
inst_avg, self.n_iter)
cls_sum_5 = sum([acc_class_5[inst].sum for inst in insts])
cls_cnt_5 = sum([acc_class_5[inst].count for inst in insts])
inst_avg_5 = cls_sum_5 / cls_cnt_5
self.writer.add_scalar(self.str_ + '/Acc_5_{}'.format(ins_clss_),
inst_avg_5, self.n_iter)
for idx, cl_ in class_list.items():
acc_cl_1[cl_] = [
acc_class[idx].avg, acc_class[idx].sum, acc_class[idx].count
]
acc_cl_5[cl_] = [
acc_class_5[idx].avg, acc_class_5[idx].sum,
acc_class_5[idx].count
]
# self.log(' * Val Acc {acc.avg:.3f} for class {cls}, {acc.sum} / {acc.count} '
# .format(acc=acc_class[idx], cls=cl_))
self.train(orig_mode)
self.log(' * Val Acc {acc.avg:.3f}, Total time {time:.2f}'.format(
acc=acc, time=batch_timer.toc()))
if from_train:
return acc, losses
else:
return acc, acc_5, acc_cl_1, acc_cl_5, losses
def accumulate_acc(self, output, target, meter):
meter.update(accuracy(output, target), len(target))
return meter
def validate(val_loader, model, args, streams=None):
"""validate function"""
batch_time = metric.AverageMeter('Time', ':6.3f')
avg_ce_loss = metric.AverageMeter('ce_loss', ':.4e')
# record the top1 accuray of each small network
top1_all = []
for i in range(args.loop_factor):
top1_all.append(metric.AverageMeter('{}_Acc@1'.format(i), ':6.2f'))
avg_top1 = metric.AverageMeter('Avg_Acc@1', ':6.2f')
avg_top5 = metric.AverageMeter('Avg_Acc@1', ':6.2f')
progress = metric.ProgressMeter(len(val_loader),
batch_time,
avg_ce_loss,
*top1_all,
avg_top1,
avg_top5,
prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
# compute outputs and losses
if args.is_amp:
with amp.autocast():
ensemble_output, outputs, ce_loss = model(images,
target=target,
mode='val')
else:
ensemble_output, outputs, ce_loss = model(images,
target=target,
mode='val')
# measure accuracy and record loss
batch_size_now = images.size(0)
for j in range(args.loop_factor):
acc1, acc5 = metric.accuracy(outputs[j, ...],
target,
topk=(1, 5))
top1_all[j].update(acc1[0].item(), batch_size_now)
# simply average outputs of small networks
avg_acc1, avg_acc5 = metric.accuracy(ensemble_output,
target,
topk=(1, 5))
avg_top1.update(avg_acc1[0].item(), batch_size_now)
avg_top5.update(avg_acc5[0].item(), batch_size_now)
avg_ce_loss.update(ce_loss.mean().item(), batch_size_now)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.print(i)
acc_all = []
acc_all.append(avg_top1.avg)
acc_all.append(avg_top5.avg)
acc_info = '* Acc@1 {:.3f} Acc@5 {:.3f}'.format(acc_all[0], acc_all[1])
for j in range(args.loop_factor):
acc_all.append(top1_all[j].avg)
acc_info += '\t {}_acc@1 {:.3f}'.format(j, top1_all[j].avg)
print(acc_info)
# torch.cuda.empty_cache()
return acc_all
def main(args):
if args.seed >= 0:
random_seed(args.seed)
train_set, valid_set, test_set = mnist(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
train_y = get_one_hot(train_y, 10)
if args.model_type == "cnn":
train_x = train_x.reshape((-1, 28, 28, 1))
test_x = test_x.reshape((-1, 28, 28, 1))
if args.model_type == "cnn":
# a LeNet-5 model with activation function changed to ReLU
net = Net([
Conv2D(kernel=[5, 5, 1, 6], stride=[1, 1], padding="SAME"),
ReLU(),
MaxPool2D(pool_size=[2, 2], stride=[2, 2]),
Conv2D(kernel=[5, 5, 6, 16], stride=[1, 1], padding="SAME"),
ReLU(),
MaxPool2D(pool_size=[2, 2], stride=[2, 2]),
Flatten(),
Dense(120),
ReLU(),
Dense(84),
ReLU(),
Dense(10)
])
elif args.model_type == "dense":
net = Net([
Dense(200),
ReLU(),
Dense(100),
ReLU(),
Dense(70),
ReLU(),
Dense(30),
ReLU(),
Dense(10)
])
else:
raise ValueError("Invalid argument: model_type")
model = Model(net=net,
loss=SoftmaxCrossEntropy(),
optimizer=Adam(lr=args.lr))
iterator = BatchIterator(batch_size=args.batch_size)
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
pred = model.forward(batch.inputs)
loss, grads = model.backward(pred, batch.targets)
model.apply_grad(grads)
loss_list.append(loss)
print("Epoch %d time cost: %.4f" % (epoch, time.time() - t_start))
# evaluate
model.set_phase("TEST")
test_pred = model.forward(test_x)
test_pred_idx = np.argmax(test_pred, axis=1)
test_y_idx = np.asarray(test_y)
res = accuracy(test_pred_idx, test_y_idx)
print(res)
model.set_phase("TRAIN")
def metric(self, logit, truth, threshold=0.5):
prob = F.sigmoid(logit)
acc = accuracy(prob, truth, threshold=threshold, is_average=True)
return acc
