def main(opts):
distributed.init_process_group(backend='nccl', init_method='env://')
device_id, device = opts.local_rank, torch.device(opts.local_rank)
rank, world_size = distributed.get_rank(), distributed.get_world_size()
torch.cuda.set_device(device_id)
# Initialize logging
task_name = f"{opts.task}-{opts.dataset}"
logdir_full = f"{opts.logdir}/{task_name}/{opts.name}/"
if rank == 0:
logger = Logger(logdir_full, rank=rank, debug=opts.debug, summary=opts.visualize, step=opts.step)
else:
logger = Logger(logdir_full, rank=rank, debug=opts.debug, summary=False)
logger.print(f"Device: {device}")
# Set up random seed
torch.manual_seed(opts.random_seed)
torch.cuda.manual_seed(opts.random_seed)
np.random.seed(opts.random_seed)
random.seed(opts.random_seed)
# xxx Set up dataloader
train_dst, val_dst, test_dst, n_classes = get_dataset(opts)
# reset the seed, this revert changes in random seed
random.seed(opts.random_seed)
train_loader = data.DataLoader(train_dst, batch_size=opts.batch_size,
sampler=DistributedSampler(train_dst, num_replicas=world_size, rank=rank),
num_workers=opts.num_workers, drop_last=True)
val_loader = data.DataLoader(val_dst, batch_size=opts.batch_size if opts.crop_val else 1,
sampler=DistributedSampler(val_dst, num_replicas=world_size, rank=rank),
num_workers=opts.num_workers)
logger.info(f"Dataset: {opts.dataset}, Train set: {len(train_dst)}, Val set: {len(val_dst)},"
f" Test set: {len(test_dst)}, n_classes {n_classes}")
logger.info(f"Total batch size is {opts.batch_size * world_size}")
# xxx Set up model
logger.info(f"Backbone: {opts.backbone}")
step_checkpoint = None
model = make_model(opts, classes=tasks.get_per_task_classes(opts.dataset, opts.task, opts.step))
logger.info(f"[!] Model made with{'out' if opts.no_pretrained else ''} pre-trained")
if opts.step == 0: # if step 0, we don't need to instance the model_old
model_old = None
else: # instance model_old
model_old = make_model(opts, classes=tasks.get_per_task_classes(opts.dataset, opts.task, opts.step - 1))
if opts.fix_bn:
model.fix_bn()
logger.debug(model)
# xxx Set up optimizer
params = []
if not opts.freeze:
params.append({"params": filter(lambda p: p.requires_grad, model.body.parameters()),
'weight_decay': opts.weight_decay})
params.append({"params": filter(lambda p: p.requires_grad, model.head.parameters()),
'weight_decay': opts.weight_decay})
params.append({"params": filter(lambda p: p.requires_grad, model.cls.parameters()),
'weight_decay': opts.weight_decay})
optimizer = torch.optim.SGD(params, lr=opts.lr, momentum=0.9, nesterov=True)
if opts.lr_policy == 'poly':
scheduler = utils.PolyLR(optimizer, max_iters=opts.epochs * len(train_loader), power=opts.lr_power)
elif opts.lr_policy == 'step':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=opts.lr_decay_step, gamma=opts.lr_decay_factor)
else:
raise NotImplementedError
logger.debug("Optimizer:\n%s" % optimizer)
if model_old is not None:
[model, model_old], optimizer = amp.initialize([model.to(device), model_old.to(device)], optimizer,
opt_level=opts.opt_level)
model_old = DistributedDataParallel(model_old)
else:
model, optimizer = amp.initialize(model.to(device), optimizer, opt_level=opts.opt_level)
# Put the model on GPU
model = DistributedDataParallel(model, delay_allreduce=True)
# xxx Load old model from old weights if step > 0!
if opts.step > 0:
# get model path
if opts.step_ckpt is not None:
path = opts.step_ckpt
else:
path = f"checkpoints/step/{task_name}_{opts.name}_{opts.step - 1}.pth"
# generate model from path
if os.path.exists(path):
step_checkpoint = torch.load(path, map_location="cpu")
model.load_state_dict(step_checkpoint['model_state'], strict=False) # False because of incr. classifiers
if opts.init_balanced:
# implement the balanced initialization (new cls has weight of background and bias = bias_bkg - log(N+1)
model.module.init_new_classifier(device)
# Load state dict from the model state dict, that contains the old model parameters
model_old.load_state_dict(step_checkpoint['model_state'], strict=True) # Load also here old parameters
logger.info(f"[!] Previous model loaded from {path}")
# clean memory
del step_checkpoint['model_state']
elif opts.debug:
logger.info(f"[!] WARNING: Unable to find of step {opts.step - 1}! Do you really want to do from scratch?")
else:
raise FileNotFoundError(path)
# put the old model into distributed memory and freeze it
for par in model_old.parameters():
par.requires_grad = False
model_old.eval()
# xxx Set up Trainer
trainer_state = None
# if not first step, then instance trainer from step_checkpoint
if opts.step > 0 and step_checkpoint is not None:
if 'trainer_state' in step_checkpoint:
trainer_state = step_checkpoint['trainer_state']
# instance trainer (model must have already the previous step weights)
trainer = Trainer(model, model_old, device=device, opts=opts, trainer_state=trainer_state,
classes=tasks.get_per_task_classes(opts.dataset, opts.task, opts.step))
# xxx Handle checkpoint for current model (model old will always be as previous step or None)
best_score = 0.0
cur_epoch = 0
if opts.ckpt is not None and os.path.isfile(opts.ckpt):
checkpoint = torch.load(opts.ckpt, map_location="cpu")
model.load_state_dict(checkpoint["model_state"], strict=True)
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
cur_epoch = checkpoint["epoch"] + 1
best_score = checkpoint['best_score']
logger.info("[!] Model restored from %s" % opts.ckpt)
# if we want to resume training, resume trainer from checkpoint
if 'trainer_state' in checkpoint:
trainer.load_state_dict(checkpoint['trainer_state'])
del checkpoint
else:
if opts.step == 0:
logger.info("[!] Train from scratch")
# xxx Train procedure
# print opts before starting training to log all parameters
logger.add_table("Opts", vars(opts))
if rank == 0 and opts.sample_num > 0:
sample_ids = np.random.choice(len(val_loader), opts.sample_num, replace=False) # sample idxs for visualization
logger.info(f"The samples id are {sample_ids}")
else:
sample_ids = None
label2color = utils.Label2Color(cmap=utils.color_map(opts.dataset)) # convert labels to images
denorm = utils.Denormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # de-normalization for original images
TRAIN = not opts.test
val_metrics = StreamSegMetrics(n_classes)
results = {}
# check if random is equal here.
logger.print(torch.randint(0,100, (1,1)))
# train/val here
while cur_epoch < opts.epochs and TRAIN:
# ===== Train =====
model.train()
epoch_loss = trainer.train(cur_epoch=cur_epoch, optim=optimizer,
train_loader=train_loader, scheduler=scheduler, logger=logger)
logger.info(f"End of Epoch {cur_epoch}/{opts.epochs}, Average Loss={epoch_loss[0]+epoch_loss[1]},"
f" Class Loss={epoch_loss[0]}, Reg Loss={epoch_loss[1]}")
# ===== Log metrics on Tensorboard =====
logger.add_scalar("E-Loss", epoch_loss[0]+epoch_loss[1], cur_epoch)
logger.add_scalar("E-Loss-reg", epoch_loss[1], cur_epoch)
logger.add_scalar("E-Loss-cls", epoch_loss[0], cur_epoch)
# ===== Validation =====
if (cur_epoch + 1) % opts.val_interval == 0:
logger.info("validate on val set...")
model.eval()
val_loss, val_score, ret_samples = trainer.validate(loader=val_loader, metrics=val_metrics,
ret_samples_ids=sample_ids, logger=logger)
logger.print("Done validation")
logger.info(f"End of Validation {cur_epoch}/{opts.epochs}, Validation Loss={val_loss[0]+val_loss[1]},"
f" Class Loss={val_loss[0]}, Reg Loss={val_loss[1]}")
logger.info(val_metrics.to_str(val_score))
# ===== Save Best Model =====
if rank == 0: # save best model at the last iteration
score = val_score['Mean IoU']
# best model to build incremental steps
save_ckpt(f"checkpoints/step/{task_name}_{opts.name}_{opts.step}.pth",
model, trainer, optimizer, scheduler, cur_epoch, score)
logger.info("[!] Checkpoint saved.")
# ===== Log metrics on Tensorboard =====
# visualize validation score and samples
logger.add_scalar("V-Loss", val_loss[0]+val_loss[1], cur_epoch)
logger.add_scalar("V-Loss-reg", val_loss[1], cur_epoch)
logger.add_scalar("V-Loss-cls", val_loss[0], cur_epoch)
logger.add_scalar("Val_Overall_Acc", val_score['Overall Acc'], cur_epoch)
logger.add_scalar("Val_MeanIoU", val_score['Mean IoU'], cur_epoch)
logger.add_table("Val_Class_IoU", val_score['Class IoU'], cur_epoch)
logger.add_table("Val_Acc_IoU", val_score['Class Acc'], cur_epoch)
# logger.add_figure("Val_Confusion_Matrix", val_score['Confusion Matrix'], cur_epoch)
# keep the metric to print them at the end of training
results["V-IoU"] = val_score['Class IoU']
results["V-Acc"] = val_score['Class Acc']
for k, (img, target, lbl) in enumerate(ret_samples):
img = (denorm(img) * 255).astype(np.uint8)
target = label2color(target).transpose(2, 0, 1).astype(np.uint8)
lbl = label2color(lbl).transpose(2, 0, 1).astype(np.uint8)
concat_img = np.concatenate((img, target, lbl), axis=2) # concat along width
logger.add_image(f'Sample_{k}', concat_img, cur_epoch)
cur_epoch += 1
# ===== Save Best Model at the end of training =====
if rank == 0 and TRAIN: # save best model at the last iteration
# best model to build incremental steps
save_ckpt(f"checkpoints/step/{task_name}_{opts.name}_{opts.step}.pth",
model, trainer, optimizer, scheduler, cur_epoch, best_score)
logger.info("[!] Checkpoint saved.")
torch.distributed.barrier()
# xxx From here starts the test code
logger.info("*** Test the model on all seen classes...")
# make data loader
test_loader = data.DataLoader(test_dst, batch_size=opts.batch_size if opts.crop_val else 1,
sampler=DistributedSampler(test_dst, num_replicas=world_size, rank=rank),
num_workers=opts.num_workers)
# load best model
if TRAIN:
model = make_model(opts, classes=tasks.get_per_task_classes(opts.dataset, opts.task, opts.step))
# Put the model on GPU
model = DistributedDataParallel(model.cuda(device))
ckpt = f"checkpoints/step/{task_name}_{opts.name}_{opts.step}.pth"
checkpoint = torch.load(ckpt, map_location="cpu")
model.load_state_dict(checkpoint["model_state"])
logger.info(f"*** Model restored from {ckpt}")
del checkpoint
trainer = Trainer(model, None, device=device, opts=opts)
model.eval()
val_loss, val_score, _ = trainer.validate(loader=test_loader, metrics=val_metrics, logger=logger)
logger.print("Done test")
logger.info(f"*** End of Test, Total Loss={val_loss[0]+val_loss[1]},"
f" Class Loss={val_loss[0]}, Reg Loss={val_loss[1]}")
logger.info(val_metrics.to_str(val_score))
logger.add_table("Test_Class_IoU", val_score['Class IoU'])
logger.add_table("Test_Class_Acc", val_score['Class Acc'])
logger.add_figure("Test_Confusion_Matrix", val_score['Confusion Matrix'])
results["T-IoU"] = val_score['Class IoU']
results["T-Acc"] = val_score['Class Acc']
logger.add_results(results)
logger.add_scalar("T_Overall_Acc", val_score['Overall Acc'], opts.step)
logger.add_scalar("T_MeanIoU", val_score['Mean IoU'], opts.step)
logger.add_scalar("T_MeanAcc", val_score['Mean Acc'], opts.step)
logger.close()
def train(args):
# Configuration
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
input_height, input_width = args.input_size
logger = Logger(log_root='logs/', name=args.logger_name)
for k, v in args.__dict__.items():
logger.add_text('configuration', "{}: {}".format(k, v))
# Dataset
train_loader, val_loader = get_data_loaders(args)
batchs_in_val = math.ceil(len(val_loader.dataset) / args.validate_batch)
print("Train set size:", len(train_loader.dataset))
print("Val set size:", len(val_loader.dataset))
# Network
if args.use_noise is True:
noise_layers = {
'crop': '((0.4,0.55),(0.4,0.55))',
'cropout': '((0.25,0.35),(0.25,0.35))',
'dropout': '(0.25,0.35)',
'jpeg': '()',
'resize': '(0.4,0.6)',
} # This is a combined noise used in the paper
else:
noise_layers = dict()
encoder = Encoder(input_height, input_width, args.info_size)
noiser = Noiser(noise_layers, torch.device('cuda'))
decoder = Decoder(args.info_size)
discriminator = Discriminator()
encoder.cuda()
noiser.cuda()
decoder.cuda()
discriminator.cuda()
# Optimizers
optimizer_enc = torch.optim.Adam(encoder.parameters())
optimizer_dec = torch.optim.Adam(decoder.parameters())
optimizer_dis = torch.optim.Adam(discriminator.parameters())
# Training
dir_save = 'ckpt/{}'.format(logger.log_name)
os.makedirs(dir_save, exist_ok=True)
os.makedirs(dir_save + '/images/', exist_ok=True)
os.makedirs(dir_save + '/models/', exist_ok=True)
training_losses = defaultdict(AverageLoss)
info_fix = torch.randint(0, 2, size=(100, args.info_size)).to(device, dtype=torch.float32)
image_fix = None
for image, _ in val_loader:
image_fix = image.cuda() # 100 images for validate, the first batch
break
global_step = 1
for epoch in range(1, args.epochs + 1):
# Train one epoch
for image, _ in train_loader:
image = image.cuda()
batch_size = image.shape[0]
info = torch.randint(0, 2, size=(batch_size, args.info_size)).to(device, dtype=torch.float32)
encoder.train()
noiser.train()
decoder.train()
discriminator.train()
# ---------------- Train the discriminator -----------------------------
optimizer_dis.zero_grad()
# train on cover
y_real = torch.ones(batch_size, 1).cuda()
y_fake = torch.zeros(batch_size, 1).cuda()
d_on_cover = discriminator(image)
encoded_image = encoder(image, info)
d_on_encoded = discriminator(encoded_image.detach())
if args.relative_loss:
d_loss_on_cover = F.binary_cross_entropy_with_logits(d_on_cover - torch.mean(d_on_encoded),
y_real)
d_loss_on_encoded = F.binary_cross_entropy_with_logits(d_on_encoded - torch.mean(d_on_cover),
y_fake)
d_loss = d_loss_on_cover + d_loss_on_encoded
else:
d_loss_on_cover = F.binary_cross_entropy_with_logits(d_on_cover, y_real)
d_loss_on_encoded = F.binary_cross_entropy_with_logits(d_on_encoded, y_fake)
d_loss = d_loss_on_cover + d_loss_on_encoded
d_loss.backward()
optimizer_dis.step()
# --------------Train the generator (encoder-decoder) ---------------------
optimizer_enc.zero_grad()
optimizer_dec.zero_grad()
d_on_cover = discriminator(image)
encoded_image = encoder(image, info)
noised_and_cover = noiser([encoded_image, image])
noised_image = noised_and_cover[0]
decoded_info = decoder(noised_image)
d_on_encoded = discriminator(encoded_image)
if args.relative_loss:
g_loss_adv = \
(F.binary_cross_entropy_with_logits(d_on_encoded - torch.mean(d_on_cover), y_real) +
F.binary_cross_entropy_with_logits(d_on_cover - torch.mean(d_on_encoded), y_fake)) * 0.5
g_loss_enc = F.mse_loss(encoded_image, image)
g_loss_dec = F.mse_loss(decoded_info, info)
else:
g_loss_adv = F.binary_cross_entropy_with_logits(d_on_encoded, y_real)
g_loss_enc = F.mse_loss(encoded_image, image)
g_loss_dec = F.mse_loss(decoded_info, info)
g_loss = args.adversarial_loss_constant * g_loss_adv + \
args.encoder_loss_constant * g_loss_enc + \
args.decoder_loss_constant * g_loss_dec
g_loss.backward()
optimizer_enc.step()
optimizer_dec.step()
decoded_rounded = decoded_info.detach().cpu().numpy().round().clip(0, 1)
bitwise_avg_err = \
np.sum(np.abs(decoded_rounded - info.detach().cpu().numpy())) / \
(batch_size * info.shape[1])
losses = {
'g_loss': g_loss.item(),
'g_loss_enc': g_loss_enc.item(),
'g_loss_dec': g_loss_dec.item(),
'bitwise_avg_error': bitwise_avg_err,
'g_loss_adv': g_loss_adv.item(),
'd_loss_on_cover': d_loss_on_cover.item(),
'd_loss_on_encoded': d_loss_on_encoded.item(),
'd_loss': d_loss.item()
}
if logger:
for name, loss in losses.items():
logger.add_scalar(name + '_iter', loss, global_step)
training_losses[name].update(loss)
global_step += 1
if logger:
logger.add_scalar('d_loss_epoch', training_losses['d_loss'].avg, epoch)
logger.add_scalar('g_loss_epoch', training_losses['g_loss'].avg, epoch)
# Validate each epoch
info_random = torch.randint(0, 2, size=(100, args.info_size)).to(device, dtype=torch.float32)
image_random = None
choice = random.randint(0, batchs_in_val - 2)
# print(choice)
for i, (image, _) in enumerate(val_loader):
if i < choice:
continue
if image.shape[0] < 100:
continue
image_random = image.cuda() # Grub the first batch
break
encoder.eval()
noiser.eval()
decoder.eval()
discriminator.eval()
encoded_image_random = encoder(image_random, info_random)
noised_and_cover_random = noiser([encoded_image_random, image_random])
noised_image_random = noised_and_cover_random[0]
decoded_info_random = decoder(noised_image_random)
encoded_image_fix = encoder(image_fix, info_fix)
noised_and_cover_fix = noiser([encoded_image_fix, image_fix])
noised_image_fix = noised_and_cover_fix[0]
decoded_info_fix = decoder(noised_image_fix)
decoded_rounded_fix = decoded_info_fix.detach().cpu().numpy().round().clip(0, 1)
bitwise_avg_err_fix = \
np.sum(np.abs(decoded_rounded_fix - info_fix.detach().cpu().numpy())) / \
(100 * info_fix.shape[1])
decoded_rounded_random = decoded_info_random.detach().cpu().numpy().round().clip(0, 1)
bitwise_avg_err_random = \
np.sum(np.abs(decoded_rounded_random - info_random.detach().cpu().numpy())) / \
(100 * info_random.shape[1])
stack_image_random = exec_val(image_random, encoded_image_random,
os.path.join(dir_save, 'images', 'random_epoch{:0>3d}.png'.format(epoch)))
stack_image_fix = exec_val(image_fix, encoded_image_fix,
os.path.join(dir_save, 'images', 'fix_epoch{:0>3d}.png'.format(epoch)))
if logger:
logger.add_scalar('fix_err_ratio', bitwise_avg_err_fix, epoch)
logger.add_scalar('random_err_ratio', bitwise_avg_err_random, epoch)
logger.add_image('image_rand', stack_image_random, epoch)
logger.add_image('image_fix', stack_image_fix, epoch)
torch.save(encoder.state_dict(), '{}/models/encoder-epoch{:0>3d}.pth'.format(dir_save, epoch))
torch.save(decoder.state_dict(), '{}/models/decoder-epoch{:0>3d}.pth'.format(dir_save, epoch))
if args.use_noise:
torch.save(noiser.state_dict(), '{}/models/noiser-epoch{:0>3d}.pth'.format(dir_save, epoch))
torch.save(discriminator.state_dict(), '{}/models/discriminator-epoch{:0>3d}.pth'.format(dir_save, epoch))
