def val(model, val_loader, criterion, n_labels):
model.eval()
val_loss = metrics.LossAverage()
val_dice = metrics.DiceAverage(n_labels)
with torch.no_grad():
for idx, (data, target) in tqdm(enumerate(val_loader),
total=len(val_loader)):
data, target = data.float(), target.long()
target = common.to_one_hot_3d(target, n_labels)
data, target = data.to(device), target.to(device)
output = model(data)
loss = criterion(output, target)
val_loss.update(loss.item(), data.size(0))
val_dice.update(output, target)
if n_labels == 2:
return OrderedDict({
'Val Loss': val_loss.avg,
'Val dice0': val_dice.avg[0],
'Val dice1': val_dice.avg[1]
})
else:
return OrderedDict({
'Val Loss': val_loss.avg,
'Val dice0': val_dice.avg[0],
'Val dice1': val_dice.avg[1],
'Val dice2': val_dice.avg[2]
})
def validate(val_loader, model, criterion):
batch_time = tools.AverageMeter('Time', ':6.3f')
losses = tools.AverageMeter('Loss', ':.4e')
top1 = tools.AverageMeter('Acc@1', ':2.2f')
top5 = tools.AverageMeter('Acc@5', ':2.2f')
progress = tools.ProgressMeter(len(val_loader), batch_time, losses)
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
input = input.cuda()
target = target.cuda()
output = model(input)
loss = criterion(output, target)
acc1, acc5 = tools.accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))
batch_time.update(time.time() - end)
end = time.time()
return top1.avg.data.cpu().numpy(), losses.avg
def validate(val_loader, model, criterion, epoch, device, args):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
tbar = tqdm(val_loader, desc='\r')
end = time.time()
model.eval()
with torch.no_grad():
preds = []
gts = []
paths = []
for batch_idx, (inputs, targets, image_path) in enumerate(tbar):
# measure data loading time
data_time.update(time.time() - end)
inputs = inputs.float()
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
[
prec1,
] = accuracy(outputs, targets, topk=(1, ))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
score = F.softmax(outputs, dim=1)
pred = score.data.max(1)[1]
preds.extend(pred.tolist())
gts.extend(targets.tolist())
paths.extend(image_path)
tbar.set_description('\r %s Loss: %.3f | Top1: %.3f' %
('Valid Stats', losses.avg, top1.avg))
f1s = list(f1_score(gts, preds, average=None))
f1_avg = sum(f1s) / len(f1s)
epoch_summary(args.output_csv_dir, epoch, paths, preds, gts)
return losses.avg, top1.avg, f1_avg, f1s
def train(model, optimizer, criterion, epoch, print_freq, data_loader,
data_pth):
# model.train()
losses = AverageMeter()
is_add_margin = False
labels_count_in_one_batch = 8
labels_count_for_all = 40
iteration_count = 40
start = time.time()
for _j in range(iteration_count):
for i in range(int(labels_count_for_all / labels_count_in_one_batch)):
start_label = i * labels_count_in_one_batch + 1
model.eval()
feat1, feat2, feat3 = _get_input_samples(model, [
x for x in range(start_label, start_label +
labels_count_in_one_batch)
], data_pth)
model.train()
loss = criterion(torch.stack(feat1), torch.stack(feat2),
torch.stack(feat3))
optimizer.zero_grad()
# backward
loss.backward()
optimizer.step()
losses.update(loss.item())
if (_j * int(labels_count_for_all / labels_count_in_one_batch) +
i + 1) % print_freq == 0:
print(
'Epoch: [{}][{}/{}]\t'
'Loss {:.6f} ({:.6f})\t'.format(
epoch,
_j *
int(labels_count_for_all / labels_count_in_one_batch) +
i + 1,
iteration_count *
int(labels_count_for_all / labels_count_in_one_batch),
losses.val, losses.mean))
if losses.val < 1e-5:
is_add_margin = True
param_group = optimizer.param_groups
print('Epoch: [{}]\tEpoch Time {:.1f} s\tLoss {:.6f}\t'
'Lr {:.2e}'.format(epoch, (time.time() - start), losses.mean,
param_group[0]['lr']))
return is_add_margin
def train(train_loader,
model,
criterion,
optimizer,
epoch,
scheduler,
mixup=False):
batch_time = tools.AverageMeter('Time', ':6.3f')
losses = tools.AverageMeter('Loss', ':.4e')
top1 = tools.AverageMeter('Acc@1', ':2.2f')
top5 = tools.AverageMeter('Acc@5', ':2.2f')
progress = tools.ProgressMeter(len(train_loader), batch_time, losses, top1,
top5)
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
scheduler.step(epoch + i / len(train_loader))
optimizer.zero_grad()
if mixup == False:
output = model(input)
loss = criterion(output, target)
else:
output, loss = tools.cutmix(input, target, model, criterion)
acc1, acc5 = tools.accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
if i % 100 == 0:
progress.pr2int(i)
return top1.avg.data.cpu().numpy(), losses.avg
def val(model, val_loader, loss_func, n_labels):
model.eval()
val_loss = metrics.LossAverage()
val_dice = metrics.DiceAverage(n_labels)
with torch.no_grad():
for idx, (data, target) in tqdm(enumerate(val_loader),
total=len(val_loader)):
data, target = data.float(), target.long()
target = common.to_one_hot_3d(target, n_labels)
data, target = data.to(device), target.to(device)
output = model(data)
loss = loss_func(output, target)
val_loss.update(loss.item(), data.size(0))
val_dice.update(output, target)
val_log = OrderedDict({
'Val_Loss': val_loss.avg,
'Val_dice_liver': val_dice.avg[1]
})
if n_labels == 3: val_log.update({'Val_dice_tumor': val_dice.avg[2]})
return val_log
def train(model, train_loader, optimizer, criterion, n_labels):
print("=======Epoch:{}=======lr:{}".format(
epoch,
optimizer.state_dict()['param_groups'][0]['lr']))
model.train()
train_loss = metrics.LossAverage()
train_dice = metrics.DiceAverage(n_labels)
for idx, (data, target) in tqdm(enumerate(train_loader),
total=len(train_loader)):
data, target = data.float(), target.long()
target = common.to_one_hot_3d(target, n_labels)
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
# if idx==0:
# print(output.shape)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss.update(loss.item(), data.size(0))
train_dice.update(output, target)
if n_labels == 2:
return OrderedDict({
'Train Loss': train_loss.avg,
'Train dice0': train_dice.avg[0],
'Train dice1': train_dice.avg[1]
})
else:
return OrderedDict({
'Train Loss': train_loss.avg,
'Train dice0': train_dice.avg[0],
'Train dice1': train_dice.avg[1],
'Train dice2': train_dice.avg[2]
})
def main():
# get unity environment
env, brain = get_unity_envs()
# get arguments
args = get_arguments()
print(args)
# set gpu environment
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
cudnn.enabled = True
cudnn.benchmark = True
cuda = torch.cuda.is_available()
# set random seed
rn = set_seeds(args.random_seed, cuda)
# make directory
os.makedirs(args.snapshot_dir, exist_ok=True)
# get validation dataset
val_set = get_validation_dataset(args)
print("len of test set: ", len(val_set))
val_loader = data.DataLoader(val_set,
batch_size=args.real_batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
# generate training list
with open(args.syn_list_path, "w") as fp:
for i in range(args.syn_img_num):
if i % 10 != 0:
fp.write(str(i + 1) + '\n')
# get main model
main_model = MLP(args.num_inputs, args.num_outputs, args.hidden_size)
if args.resume != "":
main_model.load_state_dict(torch.load(args.resume))
# get task model
if args.task_model_name == "FCN8s":
task_model = FCN8s_sourceonly(n_class=args.num_classes)
vgg16 = VGG16(pretrained=True)
task_model.copy_params_from_vgg16(vgg16)
else:
raise ValueError("Specified model name: FCN8s")
# save initial task model
torch.save(task_model.state_dict(),
os.path.join(args.snapshot_dir, "task_model_init.pth"))
if cuda:
main_model = main_model.cuda()
task_model = task_model.cuda()
# get optimizer
main_optimizer = optim.Adam(main_model.parameters(), lr=args.main_lr)
task_optimizer = optim.SGD(task_model.parameters(),
lr=args.task_lr,
momentum=0.9,
weight_decay=1e-4)
frame_idx = 0
whole_start_time = time.time()
while frame_idx < args.max_frames:
log_probs = []
rewards = []
start_time = time.time()
for i_step in range(1, args.step_each_frame + 1):
# get initial attribute list
state = np.random.rand(1, args.num_inputs)
state = torch.from_numpy(state).float()
if cuda:
state = state.cuda()
# get modified attribute list
dist = main_model(state)
action = dist.sample()
action_actual = action.float() / 10.0 # [0, 0.9]
# generate images by attribute list
print("action: " + str(action_actual.cpu().numpy()))
get_images_by_attributes(args, i_step, env, brain,
action_actual[0].cpu().numpy())
train_set = get_training_dataset(args, i_step)
train_loader = data.DataLoader(train_set,
batch_size=args.syn_batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
# train the task model using synthetic dataset
task_model.load_state_dict(
torch.load(
os.path.join(args.snapshot_dir, "task_model_init.pth")))
reward = train_task_model(train_loader, val_loader, task_model,
task_optimizer, args, cuda)
log_prob = dist.log_prob(action)[0]
log_probs.append(log_prob)
rewards.append(torch.FloatTensor([reward]))
frame_idx += 1
if frame_idx == 1:
moving_start = torch.FloatTensor([reward])
baseline = compute_returns(rewards, moving_start)
moving_start = baseline[-1]
log_probs = torch.cat(log_probs)
baseline = torch.cat(baseline).detach()
rewards = torch.cat(rewards).detach()
advantage = rewards - baseline
if cuda:
advantage = advantage.cuda()
loss = -(log_probs * advantage.detach()).mean()
with open(os.path.join(args.snapshot_dir, "logs.txt"), 'a') as fp:
fp.write(
"frame idx: {0:4d}, state: {1:s}, action: {2:s}, reward: {3:s}, baseline: {4:s}, loss: {5:.2f} \n"
.format(frame_idx, str(state.cpu()[0].numpy()),
str(action.cpu()[0].numpy()), str(rewards.numpy()),
str(baseline.numpy()), loss.item()))
print("optimize the main model parameters")
main_optimizer.zero_grad()
loss.backward()
main_optimizer.step()
elapsed_time = time.time() - start_time
print("[frame: {0:3d}], [loss: {1:.2f}], [time: {2:.1f}]".format(
frame_idx, loss.item(), elapsed_time))
torch.save(
main_model.state_dict(),
os.path.join(args.snapshot_dir, "main_model_%d.pth" % frame_idx))
elapsed_time = time.time() - whole_start_time
print("whole time: {0:.1f}".format(elapsed_time))
env.close()
def train(train_loader, val_loader, model, optimizer, criterion, lr_scheduler,
device, opt):
'''
model training
:param: train_loader: dataloader, val_loader: dataloader, model: cpkt,
optimizer: optimizer, criterion: weighted_binary_crossentropy, lr_scheduler: LRScheduler,
device: device, opt: dict
:return
'''
total_step = len(train_loader)
best_acc = -1
losses = AverageMeter()
batch_time = AverageMeter()
end = time.time()
print_freq = 20
iter_per_epoch = len(train_loader)
iter_sum = iter_per_epoch * opt.epochs
fast_train = hasattr(opt, 'fast_train')
writer = SummaryWriter(opt.model_save_path)
for epoch in range(opt.epochs):
model.train()
prefetcher = data_prefetcher(train_loader)
datas, ages, sexs, labels = prefetcher.next()
i = 0
while datas is not None:
i += 1
lr_scheduler.update(i, epoch)
# for i, (datas, ages, sexs, labels) in enumerate(train_loader):
datas = datas.to(device)
ages = ages.to(device)
sexs = sexs.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(datas, ages, sexs)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Update tensorboard
batch_time.update(time.time() - end)
losses.update(loss.item(), datas.size(0))
# Print
if (i + 1) % print_freq == 0:
iter_used = epoch * iter_per_epoch + i
used_time = batch_time.sum
total_time = used_time / iter_used * iter_sum
used_time = str(datetime.timedelta(seconds=used_time))
total_time = str(datetime.timedelta(seconds=total_time))
print(
'Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, LR:{:.5f}, Time[{:.7s}/{:.7s}]'
.format(epoch + 1, opt.epochs, i + 1, total_step,
loss.item(), optimizer.param_groups[0]['lr'],
used_time, total_time),
flush=True)
writer.add_scalar('Learning_rate',
optimizer.param_groups[0]['lr'], iter_used)
writer.add_scalar('Train/Avg_Loss', losses.avg, iter_used)
end = time.time()
datas, ages, sexs, labels = prefetcher.next()
if not fast_train:
# acc in train set
acc_train = val(train_loader, model, device)
print('Train Accuracy: {} %'.format(acc_train), flush=True)
writer.add_scalar('Train/F1_Score', acc_train, iter_used)
# acc in validation set
acc_val = val(val_loader, model, device)
if acc_val > best_acc:
# Save the model checkpoint
best_acc = acc_val
if epoch > int(opt.epochs * 0.8):
save_name = args.model + '_e{}.ckpt'.format(epoch)
save_path = opt.model_save_path + save_name
torch.save(model.state_dict(), save_path)
print('Validation Accuracy: {} %'.format(acc_val), flush=True)
writer.add_scalar('Validation/F1_Score', acc_val, iter_used)
else:
if epoch > int(opt.epochs * 0.8):
acc_val = val(val_loader, model, device)
if acc_val > best_acc:
best_acc = acc_val
save_name = args.model + '_e{}.ckpt'.format(epoch)
save_path = opt.model_save_path + save_name
torch.save(model.state_dict(), save_path)
return
def train(model, optimizer, criterion, epoch, print_freq, data_loader, data_pth):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
start = time.time()
evaluator = Evaluator(model)
distmat = evaluator.calDistmat(data_loader)
is_add_margin = False
# _t1 = time.time()
# _get_avg_feature_for_labels(model, [x for x in range(1, 41)], data_pth)
# print('time for getting center: %.1f s' % (time.time() - _t1))
# model.train()
prefix = None
for i, inputs in enumerate(data_loader):
data_time.update(time.time() - start)
# model optimizer
# parse data
imgs, pids = inputs
labels = [x.item() for x in pids]
if i % 1 == 0:
# clean_cache(prefix)
_t1 = time.time()
prefix = _get_avg_feature_for_labels(model, [x for x in range(1, 41)], data_pth)
# print('time for getting center: %.1f s' % (time.time() - _t1), i)
img1, img2, img3 = triplet_example(imgs, pids, distmat)
# input1 = img1.cuda()
input1 = get_center_anchor(labels, prefix)
feat1 = torch.stack(input1)
# print(input1)
if random.randint(1, 10) > 5:
input2 = get_furthest_from_center(labels, prefix)
# input2 = get_nearest_to_center(labels, prefix)
feat2 = torch.stack(input2)
else:
feat2 = img2.cuda()
feat2 = model(feat2)
input3 = img3.cuda()
# forward
# feat1 = model(input1)
# feat2 = model(input2)
feat3 = model(input3)
# print(feat1)
# print(feat3)
loss = criterion(feat1, feat2, feat3)
optimizer.zero_grad()
# backward
loss.backward()
optimizer.step()
batch_time.update(time.time() - start)
losses.update(loss.item())
start = time.time()
if (i + 1) % print_freq == 0:
print('Epoch: [{}][{}/{}]\t'
'Batch Time {:.3f} ({:.3f})\t'
'Data Time {:.3f} ({:.3f})\t'
'Loss {:.6f} ({:.6f})\t'
.format(epoch, i + 1, len(data_loader),
batch_time.val, batch_time.mean,
data_time.val, data_time.mean,
losses.val, losses.mean))
if losses.val < 1e-5:
is_add_margin = True
param_group = optimizer.param_groups
print('Epoch: [{}]\tEpoch Time {:.3f} s\tLoss {:.6f}\t'
'Lr {:.2e}'
.format(epoch, batch_time.sum, losses.mean, param_group[0]['lr']))
print()
return is_add_margin
def train(train_loader, model, optimizer, criterion, device, args):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
end = time.time()
top1 = AverageMeter()
tbar = tqdm(train_loader, desc='\r')
model.train()
preds = []
gts = []
paths = []
for batch_idx, (inputs, targets, image_path) in enumerate(tbar):
# measure data loading time
data_time.update(time.time() - end)
inputs, targets = inputs.to(device), targets.to(device)
r = np.random.rand(1)
if args.circlemix_prob > r:
rand_index = torch.randperm(inputs.size()[0])
target_a = targets
target_b = targets[rand_index]
r1 = np.random.randint(0, 360)
r2 = np.random.randint(0, 360)
start, end = min(r1, r2), max(r1, r2)
lam = (end - start) / 360
height = inputs.shape[2]
width = inputs.shape[3]
assert height == width, 'height does not equal to width'
side = height
mask = np.zeros((side, side), np.uint8)
vertices = polygon_vertices(side, start, end)
roi_mask = cv2.fillPoly(mask, np.array([vertices]), 255)
roi_mask_rgb = np.repeat(roi_mask[np.newaxis, :, :],
inputs.shape[1],
axis=0)
roi_mask_batch = np.repeat(roi_mask_rgb[np.newaxis, :, :, :],
inputs.shape[0],
axis=0)
roi_mask_batch = torch.from_numpy(roi_mask_batch)
roi_mask_batch = roi_mask_batch.to(device)
rand_index = rand_index.to(device)
inputs2 = inputs[rand_index].clone()
inputs[roi_mask_batch > 0] = inputs2[roi_mask_batch > 0]
outputs = model(inputs)
loss = criterion(outputs, target_a) * (1. - lam) + criterion(
outputs, target_b) * lam
if args.optimizer == 'SAM':
loss.backward()
optimizer.first_step(zero_grad=True)
# second forward-backward pass
(criterion(model(inputs), target_a) * (1. - lam) +
criterion(model(inputs), target_b) * lam).backward()
optimizer.second_step(zero_grad=True)
else:
loss.backward()
optimizer.step()
optimizer.zero_grad()
elif args.cutmix_prob > r:
lam = np.random.beta(args.beta, args.beta)
rand_index = torch.randperm(inputs.size()[0]).to(device)
target_a = targets
target_b = targets[rand_index]
bbx1, bby1, bbx2, bby2 = rand_bbox(inputs.size(), lam)
inputs[:, :, bbx1:bbx2, bby1:bby2] = inputs[rand_index, :,
bbx1:bbx2, bby1:bby2]
# adjust lambda to exactly match pixel ratio
lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) /
(inputs.size()[-1] * inputs.size()[-2]))
outputs = model(inputs)
loss = criterion(outputs, target_a) * lam + criterion(
outputs, target_b) * (1. - lam)
if args.optimizer == 'SAM':
loss.backward()
optimizer.first_step(zero_grad=True)
# second forward-backward pass
(criterion(model(inputs), target_a) * (1. - lam) +
criterion(model(inputs), target_b) * lam).backward()
optimizer.second_step(zero_grad=True)
else:
loss.backward()
optimizer.step()
optimizer.zero_grad()
elif args.cutout_prob > r:
lam = np.random.beta(args.beta, args.beta)
bbx1, bby1, bbx2, bby2 = rand_bbox(inputs.size(), lam)
inputs[:, :, bbx1:bbx2, bby1:bby2] = 0
# compute output
outputs = model(inputs)
loss = criterion(outputs, targets)
if args.optimizer == 'SAM':
loss.backward()
optimizer.first_step(zero_grad=True)
# second forward-backward pass
criterion(model(inputs), targets).backward()
optimizer.second_step(zero_grad=True)
else:
loss.backward()
optimizer.step()
optimizer.zero_grad()
else:
inputs = inputs.float()
outputs = model(inputs)
loss = criterion(outputs, targets)
if args.optimizer == 'SAM':
loss.backward()
optimizer.first_step(zero_grad=True)
# second forward-backward pass
criterion(
model(inputs),
targets).backward() # make sure to do a full forward pass
optimizer.second_step(zero_grad=True)
else:
loss.backward()
optimizer.step()
optimizer.zero_grad()
score = F.softmax(outputs, dim=1)
pred = score.data.max(1)[1]
preds.extend(pred.tolist())
gts.extend(targets.tolist())
paths.extend(image_path)
[
acc1,
] = accuracy(outputs, targets, topk=(1, ))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
tbar.set_description('\r Train Loss: %.3f | Top1: %.3f' %
(losses.avg, top1.avg))
f1s = list(f1_score(gts, preds, average=None))
f1_avg = sum(f1s) / len(f1s)
return losses.avg, top1.avg, f1_avg, f1s
