def validate(val_loader, model, criterion, device, is_test):
losses = AverageMeter("Loss", ":.4e")
top1 = AverageMeter("Acc@1", ":6.2f")
top5 = AverageMeter("Acc@5", ":6.2f")
prefix = "Test: " if is_test else "Validation: "
progress = ProgressMeter(len(val_loader), [losses, top1, top5], prefix=prefix)
# switch to evaluate mode
model.eval()
with torch.no_grad():
for i, (images, target) in enumerate(val_loader):
if torch.cuda.is_available():
images = images.to(device)
target = target.to(device)
# 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].item(), images.size(0))
top5.update(acc5[0].item(), images.size(0))
if i % 100 == 0:
progress.display(i)
return losses.avg, top1.avg, top5.avg
def train(train_loader, model, criterion, optimizer, epoch, args, cluster_result=None):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
acc_inst = AverageMeter('Acc@Inst', ':6.2f')
acc_proto = AverageMeter('Acc@Proto', ':6.2f')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, losses, acc_inst, acc_proto],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i, (images, index) in tqdm(enumerate(train_loader)):
# measure data loading time
data_time.update(time.time() - end)
if args.gpu is not None:
images[0] = images[0].cuda(args.gpu, non_blocking=True)
images[1] = images[1].cuda(args.gpu, non_blocking=True)
# compute output
output, target, output_proto, target_proto = model(im_q=images[0], im_k=images[1],
cluster_result=cluster_result, index=index)
# InfoNCE loss
loss = criterion(output, target)
# ProtoNCE loss
if output_proto is not None:
loss_proto = 0
for proto_out, proto_target in zip(output_proto, target_proto):
loss_proto += criterion(proto_out, proto_target)
accp = accuracy(proto_out, proto_target)[0]
acc_proto.update(accp[0], images[0].size(0))
# average loss across all sets of prototypes
loss_proto /= len(args.num_cluster)
loss += loss_proto
losses.update(loss.item(), images[0].size(0))
acc = accuracy(output, target)[0]
acc_inst.update(acc[0], images[0].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 % args.print_freq == 0:
progress.display(i)
def train(args, epoch, loader, model, optimizer, writer):
model.train()
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
lr = AverageMeter('Lr', ':.3f')
progress = ProgressMeter(
len(loader),
[lr, batch_time, losses],
prefix='Epoch: [{}]'.format(epoch))
end = time.time()
for _iter, (images, targets) in enumerate(loader):
images[0], images[1] = images[0].cuda(args.gpu, non_blocking=True), images[1].cuda(args.gpu, non_blocking=True)
# swap the image
yi, xj_moment = model(images[0], images[1])
yj, xi_moment = model(images[1], images[0])
if args.loss == 'pixpro':
base_A_matrix, moment_A_matrix = targets[0].cuda(args.gpu), targets[1].cuda(args.gpu)
pixpro_loss = PixproLoss(args)
overall_loss = pixpro_loss(yi, xj_moment, base_A_matrix) + pixpro_loss(yj, xi_moment, moment_A_matrix)
elif args.loss == 'pixcontrast':
base_A_matrix, moment_A_matrix = targets[0][0].cuda(args.gpu), targets[0][1].cuda(args.gpu)
base_inter_mask, moment_inter_mask = targets[1][0].cuda(args.gpu), targets[1][1].cuda(args.gpu)
pixcontrast_loss = PixContrastLoss(args)
overall_loss = (pixcontrast_loss(yi, xj_moment, base_A_matrix, base_inter_mask)
+ pixcontrast_loss(yj, xi_moment, moment_A_matrix, moment_inter_mask)) / 2
else:
ValueError('HAVE TO SELECT PROPER LOSS TYPE')
# if there is no intersection, skip the update
if torch.max(base_A_matrix) < 1 and torch.max(moment_A_matrix) < 1:
continue
losses.update(overall_loss.item(), images[0].size(0))
for param_group in optimizer.param_groups:
cur_lr = param_group['lr']
lr.update(cur_lr)
optimizer.zero_grad()
overall_loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
if (_iter % args.print_freq == 0) and (args.gpu==0):
progress.display(_iter)
writer.add_scalar('Loss', overall_loss.item(), (epoch*len(loader))+_iter)
writer.add_scalar('lr', cur_lr, (epoch*len(loader))+_iter)
def train_kd(train_loader, teacher, model, criterion, optimizer, epoch, args):
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, idx) in enumerate(train_loader):
# grid_img = torchvision.utils.make_grid(images)
# imshow(grid_img)
# time.sleep(100)
# 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)
# compute output
output = model(images)
with torch.no_grad():
o_teacher = teacher(images)
#o_teacher = gaussian_noise(o_teacher, mean=0, stddev=0.5, alpha=0.4)
# 0.1, 0.4 76.640
# 0.3, 0.4 76.630
# 0.5, 0.4 76.632
# o_teacher = torch.from_numpy(o_teacher_label_train[idx]).cuda()
loss = criterion(output, o_teacher, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.detach().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 % args.print_freq == 0:
progress.display(i)
def train(train_loader, model, criterion, optimizer, epoch, args,
lr_scheduler):
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)
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, 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()
lr_scheduler.step()
if i % args.print_freq == 0:
progress.display(i)
if i % 1000 == 0:
print('cur lr: ', lr_scheduler.get_lr()[0])
def train_prune(train_loader, model, criterion, optimizer, epoch, zero_weight,
zero_grad, args):
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=len(train_loader), eta_min=0, last_epoch=-1)
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, idx) in enumerate(train_loader):
model.apply(zero_weight)
# 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)
# 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.detach().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()
model.apply(zero_grad)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def train_epoch(self):
self.model.train()
self.epoch += 1
# record training statistics
avg_meters = {
'loss': AverageMeter('Loss', ':.4e'),
'acc': AverageMeter('Acc', ':6.2f'),
'time': AverageMeter('Time', ':6.3f')
}
progress_meter = ProgressMeter(
len(self.train_loader),
avg_meters.values(),
prefix="Epoch: [{}]".format(self.epoch)
)
# begin training from minibatches
for ix, data in enumerate(self.train_loader):
start_time = time.time()
input_ids, attention_mask, labels = map(
lambda x: x.to(args.device), data
)
logits = self.model(
input_ids=input_ids,
attention_mask=attention_mask
)
loss = self.criterion(logits, labels)
acc = (logits.argmax(axis=1) == labels).float().mean().item()
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(),
args.max_grad_norm
)
self.optimizer.step()
self.scheduler.step()
avg_meters['loss'].update(loss.item(), input_ids.size(0))
avg_meters['acc'].update(acc * 100, input_ids.size(0))
avg_meters['time'].update(time.time() - start_time)
# log progress
if (ix + 1) % args.log_interval == 0:
progress_meter.display(ix + 1)
progress_meter.display(len(self.train_loader))
def validate(val_loader, model, criterion, args):
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):
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, 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()
if i % args.print_freq == 0:
progress.display(i)
# TODO: this should also be done with the ProgressMeter
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg
def train(train_loader, model, criterion, optimizer, epoch, device, print_freq):
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, 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.to(device)
target = target.to(device)
# 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 % print_freq == 0:
progress.display(i)
def train(train_loader, model, criterion, optimizer, epoch, cfg, logger):
curr_lr = optimizer.param_groups[0]["lr"]
progress = ProgressMeter(
len(train_loader),
[logger.time, logger.loss, logger.acc1, logger.acc5],
prefix="Epoch: [{}/{}]\t"
"LR: {}\t".format(epoch, cfg.epochs, curr_lr),
)
# switch to train mode
model.train()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
if cfg.gpu is not None:
images = images.cuda(cfg.gpu, non_blocking=True)
target = target.cuda(cfg.gpu, 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))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
logger.time.update(time.time() - end)
logger.loss.update(loss.item(), images.size(0))
logger.acc1.update(acc1[0].item(), images.size(0))
logger.acc5.update(acc5[0].item(), images.size(0))
logger.save(batch=i, epoch=epoch)
end = time.time()
if i % cfg.print_freq == 0:
progress.display(i)
def validate(val_loader, model, criterion, args):
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):
# blur images
if args.blur_val:
images = GaussianBlurAll(images, args.sigma)
if torch.cuda.is_available():
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, 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()
if i % args.print_freq == 0:
progress.display(i)
print(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}".format(top1=top1,
top5=top5))
return losses.avg, top1.avg, top5.avg
def validate(val_loader, model, criterion, cfg, epoch, logger):
progress = ProgressMeter(
len(val_loader),
[logger.time, logger.loss, logger.acc1, logger.acc5],
prefix="Test: ",
)
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
if cfg.gpu is not None:
images = images.cuda(cfg.gpu, non_blocking=True)
target = target.cuda(cfg.gpu, 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))
logger.time.update(time.time() - end)
logger.loss.update(loss.item(), images.size(0))
logger.acc1.update(acc1[0].item(), images.size(0))
logger.acc5.update(acc5[0].item(), images.size(0))
logger.time.update(time.time() - end)
end = time.time()
if i % cfg.print_freq == 0:
progress.display(i)
logger.save(batch=i, epoch=epoch)
# measure elapsed time
# TODO: this should also be done with the ProgressMeter
print(" * Acc@1 {acc1:.3f} Acc@5 {acc5:.3f}".format(
acc1=logger.acc1.avg, acc5=logger.acc5.avg))
return logger.acc1.avg
def validate_kd(val_loader, teacher, model, criterion, args):
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: ')
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target, idx) 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)
output = model(images)
o_teacher = teacher(images)
#o_teacher = torch.from_numpy(o_teacher_label_val[idx]).cuda()
loss = criterion(output, o_teacher, target)
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))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1,
top5=top5))
return top1.avg
def train(
train_loader, models, optimizers, criterion, epoch, device, method_name, **kwargs,
):
loss_meters = []
top1_meters = []
top5_meters = []
inds_updates = [[] for _ in range(len(models))]
show_logs = []
for i in range(len(models)):
loss_meter = AverageMeter(f"Loss{i}", ":.4e")
top1_meter = AverageMeter(f"Acc{i}@1", ":6.2f")
top5_meter = AverageMeter(f"Acc{i}@5", ":6.2f")
loss_meters.append(loss_meter)
top1_meters.append(top1_meter)
top5_meters.append(top5_meter)
show_logs += [loss_meter, top1_meter, top5_meter]
progress = ProgressMeter(
len(train_loader), show_logs, prefix="Epoch: [{}]".format(epoch),
)
# switch to train mode
for i in range(len(models)):
models[i].train()
for i, (images, target, indexes) in enumerate(train_loader):
if torch.cuda.is_available():
images = images.to(device)
target = target.to(device)
outputs = []
for m in range(len(models)):
output = models[m](images)
outputs.append(output)
# calculate loss and selected index
if method_name in ["ours", "ftl", "greedy", "precision"]:
ind = indexes.cpu().numpy()
losses, ind_updates = loss_general(outputs, target, criterion)
elif method_name == "f-correction":
losses, ind_updates = loss_forward(outputs, target, kwargs["P"])
elif method_name == "decouple":
losses, ind_updates = loss_decouple(outputs, target, criterion)
elif method_name == "co-teaching":
losses, ind_updates = loss_coteaching(
outputs, target, kwargs["rate_schedule"][epoch]
)
elif method_name == "co-teaching+":
ind = indexes.cpu().numpy().transpose()
if epoch < kwargs["init_epoch"]:
losses, ind_updates = loss_coteaching(
outputs, target, kwargs["rate_schedule"][epoch]
)
else:
losses, ind_updates = loss_coteaching_plus(
outputs, target, kwargs["rate_schedule"][epoch], ind, epoch * i,
)
elif method_name == "jocor":
losses, ind_updates = loss_jocor(
outputs, target, kwargs["rate_schedule"][epoch], kwargs["co_lambda"]
)
else:
losses, ind_updates = loss_general(outputs, target, criterion)
if None in losses or any(~torch.isfinite(torch.tensor(losses))):
continue
# compute gradient and do BP
for loss, optimizer in zip(losses, optimizers):
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
for m in range(len(models)):
acc1, acc5 = accuracy(outputs[m], target, topk=(1, 5))
top1_meters[m].update(acc1[0].item(), images.size(0))
top5_meters[m].update(acc5[0].item(), images.size(0))
if len(ind_updates[m]) > 0:
loss_meters[m].update(losses[m].item(), len(ind_updates[m]))
inds_updates[m] += indexes[ind_updates[m]].numpy().tolist()
else:
loss_meters[m].update(losses[m].item(), images.size(0))
if i % 100 == 0:
progress.display(i)
loss_avgs = [loss_meter.avg for loss_meter in loss_meters]
top1_avgs = [top1_meter.avg for top1_meter in top1_meters]
top5_avgs = [top5_meter.avg for top5_meter in top1_meters]
return loss_avgs, top1_avgs, top5_avgs, inds_updates
def ssl(
model,
device,
dataloader,
criterion,
optimizer,
lr_scheduler=None,
epoch=0,
args=None,
):
print(
" ->->->->->->->->->-> One epoch with self-supervised training <-<-<-<-<-<-<-<-<-<-"
)
batch_time = AverageMeter("Time", ":6.3f")
data_time = AverageMeter("Data", ":6.3f")
losses = AverageMeter("Loss", ":.4f")
progress = ProgressMeter(
len(dataloader),
[batch_time, data_time, losses],
prefix="Epoch: [{}]".format(epoch),
)
model.train()
end = time.time()
for i, data in enumerate(dataloader):
images, target = data[0], data[1].to(device)
images = torch.cat([images[0], images[1]], dim=0).to(device)
bsz = target.shape[0]
# basic properties of training
if i == 0:
print(
images.shape,
target.shape,
f"Batch_size from args: {args.batch_size}",
"lr: {:.5f}".format(optimizer.param_groups[0]["lr"]),
)
print("Pixel range for training images : [{}, {}]".format(
torch.min(images).data.cpu().numpy(),
torch.max(images).data.cpu().numpy(),
))
features = model(images)
f1, f2 = torch.split(features, [bsz, bsz], dim=0)
features = torch.cat([f1.unsqueeze(1), f2.unsqueeze(1)], dim=1)
if args.training_mode == "SupCon":
loss = criterion(features, target)
elif args.training_mode == "SimCLR":
loss = criterion(features)
else:
raise ValueError("training mode not supported")
losses.update(loss.item(), bsz)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if lr_scheduler:
lr_scheduler.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def validate(val_loader, model, classifier, criterion, config, logger, block=None):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.3f')
top1 = AverageMeter('Acc@1', ':6.3f')
top5 = AverageMeter('Acc@5', ':6.3f')
progress = ProgressMeter(
len(val_loader),
[batch_time, losses, top1, top5],
prefix='Eval: ')
# switch to evaluate mode
model.eval()
if config.dataset == 'places':
block.eval()
classifier.eval()
class_num = torch.zeros(config.num_classes).cuda()
correct = torch.zeros(config.num_classes).cuda()
confidence = np.array([])
pred_class = np.array([])
true_class = np.array([])
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
if config.gpu is not None:
images = images.cuda(config.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(config.gpu, non_blocking=True)
# compute output
feat = model(images)
if config.dataset == 'places':
feat = block(feat)
output = classifier(feat)
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))
_, predicted = output.max(1)
target_one_hot = F.one_hot(target, config.num_classes)
predict_one_hot = F.one_hot(predicted, config.num_classes)
class_num = class_num + target_one_hot.sum(dim=0).to(torch.float)
correct = correct + (target_one_hot + predict_one_hot == 2).sum(dim=0).to(torch.float)
prob = torch.softmax(output, dim=1)
confidence_part, pred_class_part = torch.max(prob, dim=1)
confidence = np.append(confidence, confidence_part.cpu().numpy())
pred_class = np.append(pred_class, pred_class_part.cpu().numpy())
true_class = np.append(true_class, target.cpu().numpy())
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config.print_freq == 0:
progress.display(i, logger)
acc_classes = correct / class_num
head_acc = acc_classes[config.head_class_idx[0]:config.head_class_idx[1]].mean() * 100
med_acc = acc_classes[config.med_class_idx[0]:config.med_class_idx[1]].mean() * 100
tail_acc = acc_classes[config.tail_class_idx[0]:config.tail_class_idx[1]].mean() * 100
logger.info('* Acc@1 {top1.avg:.3f}% Acc@5 {top5.avg:.3f}% HAcc {head_acc:.3f}% MAcc {med_acc:.3f}% TAcc {tail_acc:.3f}%.'.format(top1=top1, top5=top5, head_acc=head_acc, med_acc=med_acc, tail_acc=tail_acc))
cal = calibration(true_class, pred_class, confidence, num_bins=15)
logger.info('* ECE {ece:.3f}%.'.format(ece=cal['expected_calibration_error'] * 100))
return top1.avg, cal['expected_calibration_error'] * 100
def train(train_loader, model, classifier, criterion, optimizer, epoch, config, logger, block=None):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.3f')
top1 = AverageMeter('Acc@1', ':6.3f')
top5 = AverageMeter('Acc@5', ':6.3f')
progress = ProgressMeter(
len(train_loader),
[batch_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
if config.dataset == 'places':
model.eval()
block.train()
else:
model.train()
classifier.train()
training_data_num = len(train_loader.dataset)
end_steps = int(training_data_num / train_loader.batch_size)
end = time.time()
for i, (images, target) in enumerate(train_loader):
if i > end_steps:
break
# measure data loading time
data_time.update(time.time() - end)
if torch.cuda.is_available():
images = images.cuda(config.gpu, non_blocking=True)
target = target.cuda(config.gpu, non_blocking=True)
if config.mixup is True:
images, targets_a, targets_b, lam = mixup_data(images, target, alpha=config.alpha)
if config.dataset == 'places':
with torch.no_grad():
feat_a = model(images)
feat = block(feat_a.detach())
output = classifier(feat)
else:
feat = model(images)
output = classifier(feat)
loss = mixup_criterion(criterion, output, targets_a, targets_b, lam)
else:
if config.dataset == 'places':
with torch.no_grad():
feat_a = model(images)
feat = block(feat_a.detach())
output = classifier(feat)
else:
feat = model(images)
output = classifier(feat)
loss = criterion(output, target)
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 % config.print_freq == 0:
progress.display(i, logger)
def adv(model, device, dataloader, criterion, optimizer, lr_scheduler=None, epoch=0, args=None):
if args.local_rank == 0:
print(" ->->->->->->->->->-> One epoch with Adversarial (Trades) training <-<-<-<-<-<-<-<-<-<-")
batch_time = AverageMeter("Time", ":6.3f")
data_time = AverageMeter("Data", ":6.3f")
losses = AverageMeter("Loss", ":.4f")
top1 = AverageMeter("Acc_1", ":6.2f")
top5 = AverageMeter("Acc_5", ":6.2f")
top1_adv = AverageMeter("Acc_1_adv", ":6.2f")
top5_adv = AverageMeter("Acc_5_adv", ":6.2f")
progress = ProgressMeter(
len(dataloader),
[batch_time, data_time, losses, top1, top5, top1_adv, top5_adv],
prefix="Epoch: [{}]".format(epoch),
)
model.train()
end = time.time()
for i, data in enumerate(dataloader):
images, target = data[0].to(device), data[1].to(device)
# basic properties of training
if i == 0 and args.local_rank == 0:
print(
images.shape,
target.shape,
f"Batch_size from args: {args.batch_size}",
"lr: {:.5f}".format(optimizer.param_groups[0]["lr"]),
)
print(
"Pixel range for training images : [{}, {}]".format(
torch.min(images).data.cpu().numpy(),
torch.max(images).data.cpu().numpy(),
)
)
# calculate robust loss
loss, logits, logits_adv = trades_loss(
model=model,
x_natural=images,
y=target,
device=device,
optimizer=optimizer,
step_size=args.step_size,
epsilon=args.epsilon,
perturb_steps=args.num_steps,
beta=args.beta,
clip_min=args.clip_min,
clip_max=args.clip_max,
distance=args.distance,
)
# measure accuracy and record loss
acc1, acc5 = accuracy(logits, 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))
acc1_adv, acc5_adv = accuracy(logits_adv, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1_adv.update(acc1_adv[0], images.size(0))
top5_adv.update(acc5_adv[0], images.size(0))
optimizer.zero_grad()
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
if lr_scheduler:
lr_scheduler.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0 and args.local_rank == 0:
progress.display(i)
result = {"top1": top1.avg, "top5": top5.avg, "top1_adv": top1_adv.avg, "top5_adv": top5_adv.avg}
return result
def baseline(model, device, dataloader, criterion, optimizer, lr_scheduler=None, epoch=0, args=None):
if args.local_rank == 0:
print(" ->->->->->->->->->-> One epoch with Baseline natural training <-<-<-<-<-<-<-<-<-<-")
batch_time = AverageMeter("Time", ":6.3f")
data_time = AverageMeter("Data", ":6.3f")
losses = AverageMeter("Loss", ":.4f")
top1 = AverageMeter("Acc_1", ":6.2f")
top5 = AverageMeter("Acc_5", ":6.2f")
progress = ProgressMeter(
len(dataloader),
[batch_time, data_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch),
)
model.train()
end = time.time()
for i, data in enumerate(dataloader):
images, target = data[0].to(device), data[1].to(device)
# basic properties of training
if i == 0 and args.local_rank == 0:
print(
images.shape,
target.shape,
f"Batch_size from args: {args.batch_size}",
"lr: {:.5f}".format(optimizer.param_groups[0]["lr"]),
)
print(
"Pixel range for training images : [{}, {}]".format(
torch.min(images).data.cpu().numpy(),
torch.max(images).data.cpu().numpy(),
)
)
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))
optimizer.zero_grad()
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
if lr_scheduler:
lr_scheduler.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0 and args.local_rank == 0:
progress.display(i)
result = {"top1": top1.avg, "top5": top5.avg}
return result
def train(loader: DataLoader, model: nn.Module, criterion: Callable,
optimizer: Optimizer, scheduler: object, scaler: GradScaler,
device: torch.device, loop_steps: int, step: int):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
# path_losses = AverageMeter('Path Loss', ':.4e')
# mlm_losses = AverageMeter('MLM Loss', ':.4e')
accuracies = AverageMeter('Accuracy', ':6.2f')
# progress = ProgressMeter(
# loop_steps,
# [batch_time, data_time, losses, path_losses, mlm_losses, accuracies],
# prefix="Epoch: [{}]".format(step))
progress = ProgressMeter(loop_steps,
[batch_time, data_time, losses, accuracies],
prefix="Epoch: [{}]".format(step))
model.train()
end = time.time()
for i, (path, attention_mask, targets, _) in enumerate(loader):
data_time.update(time.time() - end)
non_blocking = device.type != 'cpu'
path = path.to(device, non_blocking=non_blocking)
attention_mask = path.to(device, non_blocking=non_blocking)
targets = targets.to(device, non_blocking=non_blocking)
#
# mask_indices = []
# for input_ids_batch, attention_mask_batch in zip(path, attention_masks):
# sequence_lengths_batch = attention_mask_batch.shape[1] - attention_mask_batch.flip(1).argmax(1)
# attention_mask_batch = attention_mask_batch.detach().clone()
# # remove masking for sequence length
# for batch_idx in range(input_ids_batch.shape[0]):
# attention_mask_batch[batch_idx, sequence_lengths_batch[batch_idx]:] = 1
# mask_indices_batch = (attention_mask_batch == 0).nonzero(as_tuple=True)
# mask_indices.append(mask_indices_batch)
#
with autocast():
# logits, mask_encodings = model(input_ids=path, attention_mask=attention_mask)
output = model(input_ids=path,
attention_mask=attention_mask,
labels=targets)
logits = output.logits
loss = output.loss.mean()
# # mlm_loss = torch.zeros(1).to(device, non_blocking=non_blocking)
# for mask_encoding_batch, input_ids_batch, mask_indices_batch in zip(mask_encodings, path, mask_indices):
# if len(mask_indices_batch[0]) == 0:
# continue
# mask_targets = input_ids_batch[mask_indices_batch]
# mask_logits = mlm_head(mask_encoding_batch)
# # gradually take mean
# mlm_loss += criterion(mask_logits, mask_targets) * (1. / len(path))
# loss = path_loss + MLM_COEF * mlm_loss
accuracy = (logits.argmax(1) == targets).float().mean().item()
losses.update(loss.item(), targets.shape[0])
# path_losses.update(path_loss.item(), targets.shape[0])
# mlm_losses.update(mlm_loss.item(), targets.shape[0])
accuracies.update(accuracy, targets.shape[0])
optimizer.zero_grad()
scaler.scale(loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
CLIPPING_GRADIENT_NORM)
scaler.step(optimizer)
scaler.update()
scheduler.step()
batch_time.update(time.time() - end)
end = time.time()
if i % (loop_steps // 50) == 0:
progress.display(i)
if i == loop_steps - 1:
break
return losses.avg
def train(train_loader, model, criterion, optimizer, epoch, args):
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 + 1),
)
# switch to train mode
model.train()
# blur settings
if args.mode == "normal":
args.sigma = 0 # no blur
elif args.mode == "multi-steps-cbt":
args.sigma = adjust_multi_steps_cbt(
args.init_sigma, epoch, args.cbt_rate,
every=5) # sigma decay every 5 epoch
elif args.mode == "multi-steps":
args.sigma = adjust_multi_steps(epoch)
elif args.mode == "single-step":
if epoch >= args.epochs // 2:
args.sigma = 0
elif args.mode == "fixed-single-step":
if epoch >= args.epochs // 2:
args.sigma = 0 # no blur
# fix parameters of 1st Conv layer
model.features[0].weight.requires_grad = False
model.features[0].bias.requires_grad = False
elif args.mode == "reversed-single-step":
if epoch < args.epochs // 2:
args.sigma = 0
else:
args.sigma = args.reverse_sigma
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# blur images (no blur when args.sigma = 0)
if args.mode == "mix":
half1, half2 = images.chunk(2)
# blur first half images
half1 = GaussianBlurAll(half1, args.sigma)
images = torch.cat((half1, half2))
elif args.mode == "random-mix":
half1, half2 = images.chunk(2)
# blur first half images
half1 = RandomGaussianBlurAll(half1, args.min_sigma,
args.max_sigma)
images = torch.cat((half1, half2))
else:
images = GaussianBlurAll(images, args.sigma)
if torch.cuda.is_available():
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
# compute outputs
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 % args.print_freq == 0:
progress.display(i)
return losses.avg, top1.avg, top5.avg
## eval
eval_acc = model.evaluation(testloader)
## log
for i, loss in enumerate(loss_list):
log_list[i + 1].update(loss.avg)
writer.add_scalar(loss.name, loss.avg, epoch)
lr_name = 'lr'
for i, opt in enumerate(model.optimizer.optimizers):
writer.add_scalar(lr_name, opt.param_groups[0]['lr'], epoch)
lr_name = f'lr_{i+2}'
log_list[-1].update(eval_acc)
writer.add_scalar(log_list[-1].name, eval_acc, epoch)
logger.log(progress.display(epoch), consol=False)
## save
state = {
'args': args,
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': model.optimizer.state_dict()
}
if max_acc < eval_acc:
max_acc = eval_acc
filename = os.path.join(args.save_folder,
'checkpoint_best.pth.tar')
logger.log('#' * 20 + 'Save Best Model' + '#' * 20)
torch.save(state, filename)
def validate(val_loader, model, epsilon, args):
batch_time = AverageMeter('Time', ':6.3f')
top1 = AverageMeter('Acc@1', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_time, top1],
prefix='Test: ')
# switch to evaluate mode
model.eval()
mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
preprocessing = (mean, std)
fmodel = PyTorchModel(model,
bounds=(0, 1),
num_classes=1000,
preprocessing=preprocessing)
clean_labels = np.zeros(len(val_loader))
target_labels = np.zeros(len(val_loader))
clean_pred_labels = np.zeros(len(val_loader))
adv_pred_labels = np.zeros(len(val_loader))
end = time.time()
# Batch processing is not supported in in foolbox 1.8, so we feed images one by one. Note that we are using a batch
# size of 2, which means we consider every other image (due to computational costs)
for i, (images, target) in enumerate(val_loader):
image = images.cpu().numpy()[0]
clean_label = target.cpu().numpy()[0]
target_label = np.random.choice(
np.setdiff1d(np.arange(1000), clean_label))
attack = RandomStartProjectedGradientDescentAttack(
model=fmodel,
criterion=TargetClass(target_label),
distance=Linfinity)
adversarial = attack(image,
clean_label,
binary_search=False,
epsilon=epsilon,
stepsize=2. / 255,
iterations=args.pgd_steps,
random_start=True)
if np.any(adversarial == None):
# Non-adversarial
adversarial = image
target_label = clean_label
adv_pred_labels[i] = np.argmax(fmodel.predictions(adversarial))
clean_labels[i] = clean_label
target_labels[i] = target_label
clean_pred_labels[i] = np.argmax(fmodel.predictions(image))
print('Iter, Clean, Clean_pred, Adv, Adv_pred: ', i, clean_label,
clean_pred_labels[i], target_label, adv_pred_labels[i])
# measure accuracy and update average
acc1 = 100. * np.mean(clean_label == adv_pred_labels[i])
top1.update(acc1, 1)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print('* Acc@1 {top1.avg:.3f} '.format(top1=top1))
return top1.avg
acc1, acc5 = accuracy(sim_t_2_i,
torch.arange(image.size(0)).cuda(),
topk=(1, 5))
losses.update(loss.item(), image.size(0))
top1_acc.update(acc1[0], image.size(0))
top5_acc.update(acc5[0], image.size(0))
loss.backward()
optimizer.step()
scheduler.step()
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % cfg.TRAIN.PRINT_FREQ == 0:
progress.display(global_step % (len(trainloader) * 30))
if epoch % 8 == 1:
checkpoint_file = args.name + "/checkpoint_%d.pth" % epoch
torch.save(
{
"epoch": epoch,
"global_step": global_step,
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict()
}, checkpoint_file)
if top1_acc.avg > best_top1:
best_top1 = top1_acc.avg
checkpoint_file = args.name + "/checkpoint_best.pth"
torch.save(
{
def validate(loader: DataLoader, model: nn.Module, criterion: Callable,
num_classes: int, num_super_classes: int, maf: torch.FloatTensor,
args: ArgumentParser) -> torch.FloatTensor:
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('MLM Loss', ':.4e')
accuracies = AverageMeter('Acc', ':.4e')
accuracy_deltas = AverageMeter('Acc Delta', ':.4e')
progress = ProgressMeter(len(loader),
[batch_time, losses, accuracies, accuracy_deltas],
prefix="Test: ")
model.eval()
device = get_device(args)
with torch.no_grad():
end = time.time()
for i, (genotypes, labels, super_labels) in enumerate(loader):
### Mask for Masked Language Modeling
mask_num = int((i % 9 + 1) / 10 * genotypes.shape[1])
mask_scores = torch.rand(genotypes.shape[1])
mask_indices = mask_scores.argsort(descending=True)[:mask_num]
masked_genotypes = genotypes[:, mask_indices].reshape(-1)
targets = (masked_genotypes == 1).float().clone().detach()
genotypes[:, mask_indices] = 0
maf_vector = maf[labels[0]]
genotypes = genotypes.to(device)
masked_genotypes = masked_genotypes.to(device)
targets = targets.to(device)
labels = labels.to(device)
super_labels = super_labels.to(device)
maf_vector = maf_vector.to(device)
logits = model(genotypes, labels, super_labels)
logits = logits[:, mask_indices].reshape(-1)
# add weight to nonzero maf snps
weights = torch.ones_like(logits)
weight_coefficients = (maf_vector[mask_indices] > 0).repeat(
genotypes.shape[0]).float() * (args.minor_coefficient - 1) + 1
weights *= weight_coefficients
loss = criterion(logits, targets, weight=weights, reduction='mean')
accuracy = (masked_genotypes * logits.sign()).mean() / 2 + .5
baseline_accuracy = (
masked_genotypes *
(maf_vector[mask_indices].repeat(genotypes.shape[0]) -
.5000001).sign()).mean() / 2 + .5
accuracy_delta = accuracy - baseline_accuracy
losses.update(loss.item(), genotypes.shape[0])
accuracies.update(accuracy.item(), genotypes.shape[0])
accuracy_deltas.update(accuracy_delta.item(), genotypes.shape[0])
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
progress.display(i)
return losses.avg
def train_epoch(self):
self.models.train()
self.epoch += 1
# record training statistics
avg_meters = {
'loss_rec': AverageMeter('Loss Rec', ':.4e'),
'loss_adv': AverageMeter('Loss Adv', ':.4e'),
'loss_disc': AverageMeter('Loss Disc', ':.4e'),
'time': AverageMeter('Time', ':6.3f')
}
progress_meter = ProgressMeter(len(self.train_loaders[0]),
avg_meters.values(),
prefix="Epoch: [{}]".format(self.epoch))
# begin training from minibatches
for ix, (data_0, data_1) in enumerate(zip(*self.train_loaders)):
start_time = time.time()
# load text and labels
src_0, src_len_0, labels_0 = data_0
src_0, labels_0 = src_0.to(args.device), labels_0.to(args.device)
src_1, src_len_1, labels_1 = data_1
src_1, labels_1 = src_1.to(args.device), labels_1.to(args.device)
# encode
encoder = self.models['encoder']
z_0 = encoder(labels_0, src_0, src_len_0) # (batch_size, dim_z)
z_1 = encoder(labels_1, src_1, src_len_1)
# recon & transfer
generator = self.models['generator']
inputs_0 = (z_0, labels_0, src_0)
h_ori_seq_0, pred_ori_0 = generator(*inputs_0, src_len_0, False)
h_trans_seq_0_to_1, _ = generator(*inputs_0, src_len_1, True)
inputs_1 = (z_1, labels_1, src_1)
h_ori_seq_1, pred_ori_1 = generator(*inputs_1, src_len_1, False)
h_trans_seq_1_to_0, _ = generator(*inputs_1, src_len_0, True)
# discriminate real and transfer
disc_0, disc_1 = self.models['disc_0'], self.models['disc_1']
d_0_real = disc_0(h_ori_seq_0.detach()) # detached
d_0_fake = disc_0(h_trans_seq_1_to_0.detach())
d_1_real = disc_1(h_ori_seq_1.detach())
d_1_fake = disc_1(h_trans_seq_0_to_1.detach())
# discriminator loss
loss_disc = (loss_fn(args.gan_type)(d_0_real, self.ones) +
loss_fn(args.gan_type)(d_0_fake, self.zeros) +
loss_fn(args.gan_type)(d_1_real, self.ones) +
loss_fn(args.gan_type)(d_1_fake, self.zeros))
# gradient penalty
if args.gan_type == 'wgan-gp':
loss_disc += args.gp_weight * gradient_penalty(
h_ori_seq_0, # real data for 0
h_trans_seq_1_to_0, # fake data for 0
disc_0)
loss_disc += args.gp_weight * gradient_penalty(
h_ori_seq_1, # real data for 1
h_trans_seq_0_to_1, # fake data for 1
disc_1)
avg_meters['loss_disc'].update(loss_disc.item(), src_0.size(0))
self.disc_optimizer.zero_grad()
loss_disc.backward()
self.disc_optimizer.step()
# reconstruction loss
loss_rec = (
F.cross_entropy( # Recon 0 -> 0
pred_ori_0.view(-1, pred_ori_0.size(-1)),
src_0[1:].view(-1),
ignore_index=bert_tokenizer.pad_token_id,
reduction='sum') + F.cross_entropy( # Recon 1 -> 1
pred_ori_1.view(-1, pred_ori_1.size(-1)),
src_1[1:].view(-1),
ignore_index=bert_tokenizer.pad_token_id,
reduction='sum')) / (
2.0 * args.batch_size
) # match scale with the orginal paper
avg_meters['loss_rec'].update(loss_rec.item(), src_0.size(0))
# generator loss
d_0_fake = disc_0(h_trans_seq_1_to_0) # not detached
d_1_fake = disc_1(h_trans_seq_0_to_1)
loss_adv = (loss_fn(args.gan_type, disc=False)
(d_0_fake, self.ones) +
loss_fn(args.gan_type, disc=False)(d_1_fake, self.ones)
) / 2.0 # match scale with the original paper
avg_meters['loss_adv'].update(loss_adv.item(), src_0.size(0))
# XXX: threshold for training stability
if (not args.two_stage):
if (args.threshold is not None and loss_disc < args.threshold):
loss = loss_rec + args.rho * loss_adv
else:
loss = loss_rec
else: # two_stage training
if (args.second_stage_num > args.epochs - self.epoch):
# last second_stage; flow loss_adv gradients
loss = loss_rec + args.rho * loss_adv
else:
loss = loss_rec
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
avg_meters['time'].update(time.time() - start_time)
# log progress
if (ix + 1) % args.log_interval == 0:
progress_meter.display(ix + 1)
progress_meter.display(len(self.train_loaders[0]))
def evaluate(model, valloader, epoch, cfg, index=2):
global best_top1_eval
print("Test::::")
model.eval()
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1_acc = AverageMeter('Acc@1', ':6.2f')
top5_acc = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(
len(valloader), [batch_time, data_time, losses, top1_acc, top5_acc],
prefix="Test Epoch: [{}]".format(epoch))
end = time.time()
with torch.no_grad():
for batch_idx, batch in enumerate(valloader):
if cfg.DATA.USE_MOTION:
image, text, bk, id_car = batch
else:
image, text, id_car = batch
tokens = tokenizer.batch_encode_plus(text,
padding='longest',
return_tensors='pt')
data_time.update(time.time() - end)
if cfg.DATA.USE_MOTION:
pairs, logit_scale, cls_logits = model(
tokens['input_ids'].cuda(),
tokens['attention_mask'].cuda(), image.cuda(), bk.cuda())
else:
pairs, logit_scale, cls_logits = model(
tokens['input_ids'].cuda(),
tokens['attention_mask'].cuda(), image.cuda())
logit_scale = logit_scale.mean().exp()
loss = 0
# for visual_embeds,lang_embeds in pairs:
visual_embeds, lang_embeds = pairs[index]
sim_i_2_t = torch.matmul(torch.mul(logit_scale, visual_embeds),
torch.t(lang_embeds))
sim_t_2_i = sim_i_2_t.t()
loss_t_2_i = F.cross_entropy(sim_t_2_i,
torch.arange(image.size(0)).cuda())
loss_i_2_t = F.cross_entropy(sim_i_2_t,
torch.arange(image.size(0)).cuda())
loss += (loss_t_2_i + loss_i_2_t) / 2
acc1, acc5 = accuracy(sim_t_2_i,
torch.arange(image.size(0)).cuda(),
topk=(1, 5))
losses.update(loss.item(), image.size(0))
top1_acc.update(acc1[0], image.size(0))
top5_acc.update(acc5[0], image.size(0))
batch_time.update(time.time() - end)
end = time.time()
progress.display(batch_idx)
if top1_acc.avg > best_top1_eval:
best_top1_eval = top1_acc.avg
checkpoint_file = args.name + "/checkpoint_best_eval.pth"
torch.save(
{
"epoch": epoch,
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict()
}, checkpoint_file)