def main(opts):
# ===== Setup distributed =====
distributed.init_process_group(backend='nccl', init_method='env://')
if opts.device is not None:
device_id = opts.device
else:
device_id = opts.local_rank
device = torch.device(device_id)
rank, world_size = distributed.get_rank(), distributed.get_world_size()
if opts.device is not None:
torch.cuda.set_device(opts.device)
else:
torch.cuda.set_device(device_id)
# ===== Initialize logging =====
logdir_full = f"{opts.logdir}/{opts.dataset}/{opts.name}/"
if rank == 0:
logger = Logger(logdir_full,
rank=rank,
debug=opts.debug,
summary=opts.visualize)
else:
logger = Logger(logdir_full,
rank=rank,
debug=opts.debug,
summary=False)
logger.print(f"Device: {device}")
checkpoint_path = f"checkpoints/{opts.dataset}/{opts.name}.pth"
os.makedirs(f"checkpoints/{opts.dataset}", exist_ok=True)
# ===== Setup random seed to reproducibility =====
torch.manual_seed(opts.random_seed)
torch.cuda.manual_seed(opts.random_seed)
np.random.seed(opts.random_seed)
random.seed(opts.random_seed)
# ===== Set up dataset =====
train_dst, val_dst = get_dataset(opts, train=True)
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,
pin_memory=True)
val_loader = data.DataLoader(val_dst,
batch_size=opts.batch_size,
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)}, "
f"Val set: {len(val_dst)}, n_classes {opts.num_classes}")
logger.info(f"Total batch size is {opts.batch_size * world_size}")
# This is necessary for computing the scheduler decay
opts.max_iter = opts.max_iter = opts.epochs * len(train_loader)
# ===== Set up model and ckpt =====
model = Trainer(device, logger, opts)
model.distribute()
cur_epoch = 0
if opts.continue_ckpt:
opts.ckpt = checkpoint_path
if opts.ckpt is not None:
assert os.path.isfile(
opts.ckpt), "Error, ckpt not found. Check the correct directory"
checkpoint = torch.load(opts.ckpt, map_location="cpu")
cur_epoch = checkpoint["epoch"] + 1
model.load_state_dict(checkpoint["model_state"])
logger.info("[!] Model restored from %s" % opts.ckpt)
del checkpoint
else:
logger.info("[!] Train from scratch")
# ===== Train procedure =====
# print opts before starting training to log all parameters
logger.add_table("Opts", vars(opts))
# uncomment if you want qualitative on val
# 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_metrics = StreamSegMetrics(opts.num_classes)
val_metrics = StreamSegMetrics(opts.num_classes)
results = {}
# check if random is equal here.
logger.print(torch.randint(0, 100, (1, 1)))
while cur_epoch < opts.epochs and not opts.test:
# ===== Train =====
start = time.time()
epoch_loss = model.train(cur_epoch=cur_epoch,
train_loader=train_loader,
metrics=train_metrics,
print_int=opts.print_interval)
train_score = train_metrics.get_results()
end = time.time()
len_ep = int(end - start)
logger.info(
f"End of Epoch {cur_epoch}/{opts.epochs}, Average Loss={epoch_loss[0] + epoch_loss[1]:.4f}, "
f"Class Loss={epoch_loss[0]:.4f}, Reg Loss={epoch_loss[1]}\n"
f"Train_Acc={train_score['Overall Acc']:.4f}, Train_Iou={train_score['Mean IoU']:.4f} "
f"\n -- time: {len_ep // 60}:{len_ep % 60} -- ")
logger.info(
f"I will finish in {len_ep * (opts.epochs - cur_epoch) // 60} minutes"
)
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...")
val_loss, _ = model.validate(loader=val_loader,
metrics=val_metrics,
ret_samples_ids=None)
val_score = val_metrics.get_results()
logger.print("Done validation")
logger.info(
f"End of Validation {cur_epoch}/{opts.epochs}, Validation Loss={val_loss}"
)
log_val(logger, val_metrics, val_score, val_loss, 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']
# ===== Save Model =====
if rank == 0:
if not opts.debug:
save_ckpt(checkpoint_path, model, cur_epoch)
logger.info("[!] Checkpoint saved.")
cur_epoch += 1
torch.distributed.barrier()
# ==== TESTING =====
logger.info("*** Test the model on all seen classes...")
# make data loader
test_dst = get_dataset(opts, train=False)
test_loader = data.DataLoader(test_dst,
batch_size=opts.batch_size_test,
sampler=DistributedSampler(
test_dst,
num_replicas=world_size,
rank=rank),
num_workers=opts.num_workers)
if rank == 0 and opts.sample_num > 0:
sample_ids = np.random.choice(len(test_loader),
opts.sample_num,
replace=False) # sample idxs for visual.
logger.info(f"The samples id are {sample_ids}")
else:
sample_ids = None
val_loss, ret_samples = model.validate(loader=test_loader,
metrics=val_metrics,
ret_samples_ids=sample_ids)
val_score = val_metrics.get_results()
conf_matrixes = val_metrics.get_conf_matrixes()
logger.print("Done test on all")
logger.info(f"*** End of Test on all, Total Loss={val_loss}")
logger.info(val_metrics.to_str(val_score))
log_samples(logger, ret_samples, denorm, label2color, 0)
logger.add_figure("Test_Confusion_Matrix_Recall",
conf_matrixes['Confusion Matrix'])
logger.add_figure("Test_Confusion_Matrix_Precision",
conf_matrixes["Confusion Matrix Pred"])
results["T-IoU"] = val_score['Class IoU']
results["T-Acc"] = val_score['Class Acc']
results["T-Prec"] = val_score['Class Prec']
logger.add_results(results)
logger.add_scalar("T_Overall_Acc", val_score['Overall Acc'])
logger.add_scalar("T_MeanIoU", val_score['Mean IoU'])
logger.add_scalar("T_MeanAcc", val_score['Mean Acc'])
ret = val_score['Mean IoU']
logger.close()
return ret
# metrics
preds = outputs.detach().max(dim=1)[1].cpu().numpy()
targets = labels.cpu().numpy()
metrics.update(targets, preds)
end = time.time()
if step%10==0:
print('Epoch: ',str(epoch),' Iter: ',step,'Loss: ',loss.item(),)
print('iter time: ',end-start)
# update training_loss, training_accuracy and training_iou
train_loss = train_loss/float(len(train_loader))
train_loss_list.append(train_loss)
results = metrics.get_results()
train_iou = results["Mean IoU"]
train_iou_list.append(train_iou)
writer.add_scalar("loss/train", train_loss, epoch)
writer.add_scalar("iou/train", train_iou, epoch)
if epoch%5==0:
metrics.reset()
model.eval()
val_loss = 0.0
for step, (images, labels) in enumerate(val_loader):
with torch.no_grad():
def main():
opts = get_argparser().parse_args()
opts = modify_command_options(opts)
os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_id
device = torch.device( 'cuda' if torch.cuda.is_available() else 'cpu' )
print("Device: %s"%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)
# Set up dataloader
_, val_dst = get_dataset(opts)
val_loader = data.DataLoader(val_dst, batch_size=opts.batch_size if opts.crop_val else 1 , shuffle=False, num_workers=opts.num_workers)
print("Dataset: %s, Val set: %d"%(opts.dataset, len(val_dst)))
# Set up model
print("Backbone: %s"%opts.backbone)
model = DeepLabv3(num_classes=opts.num_classes, backbone=opts.backbone, pretrained=True, momentum=opts.bn_mom, output_stride=opts.output_stride, use_separable_conv=opts.use_separable_conv)
if opts.use_gn==True:
print("[!] Replace BatchNorm with GroupNorm!")
model = utils.convert_bn2gn(model)
if torch.cuda.device_count()>1: # Parallel
print("%d GPU parallel"%(torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
model_ref = model.module # for ckpt
else:
model_ref = model
model = model.to(device)
# Set up metrics
metrics = StreamSegMetrics(opts.num_classes)
if opts.save_path is not None:
utils.mkdir(opts.save_path)
# Restore
if opts.ckpt is not None and os.path.isfile(opts.ckpt):
checkpoint = torch.load(opts.ckpt)
model_ref.load_state_dict(checkpoint["model_state"])
print("Model restored from %s"%opts.ckpt)
else:
print("[!] Retrain")
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]) # denormalization for ori images
model.eval()
metrics.reset()
idx = 0
if opts.save_path is not None:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
with torch.no_grad():
for i, (images, labels) in tqdm( enumerate( val_loader ) ):
images = images.to(device, dtype=torch.float32)
labels = labels.to(device, dtype=torch.long)
outputs = model(images)
preds = outputs.detach().max(dim=1)[1].cpu().numpy()
targets = labels.cpu().numpy()
metrics.update(targets, preds)
if opts.save_path is not None:
for i in range(len(images)):
image = images[i].detach().cpu().numpy()
target = targets[i]
pred = preds[i]
image = (denorm(image) * 255).transpose(1,2,0).astype(np.uint8)
target = label2color(target).astype(np.uint8)
pred = label2color(pred).astype(np.uint8)
Image.fromarray(image).save(os.path.join(opts.save_path, '%d_image.png'%idx) )
Image.fromarray(target).save(os.path.join(opts.save_path, '%d_target.png'%idx) )
Image.fromarray(pred).save(os.path.join(opts.save_path, '%d_pred.png'%idx) )
fig = plt.figure()
plt.imshow(image)
plt.axis('off')
plt.imshow(pred, alpha=0.7)
ax = plt.gca()
ax.xaxis.set_major_locator(matplotlib.ticker.NullLocator())
ax.yaxis.set_major_locator(matplotlib.ticker.NullLocator())
plt.savefig(os.path.join(opts.save_path, '%d_overlay.png'%idx), bbox_inches='tight', pad_inches=0)
plt.close()
idx+=1
score = metrics.get_results()
print(metrics.to_str(score))
if opts.save_path is not None:
with open(os.path.join(opts.save_path, 'score.txt'), mode='w') as f:
f.write(metrics.to_str(score))
