def main(opts):
# Set parameters
p = OrderedDict() # Parameters to include in report
p['trainBatch'] = opts.batch # Training batch size
testBatch = 1 # Testing batch size
useTest = True # See evolution of the test set when training
nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs
snapshot = 1 # Store a model every snapshot epochs
p['nAveGrad'] = 1 # Average the gradient of several iterations
p['lr'] = opts.lr # Learning rate
p['wd'] = 5e-4 # Weight decay
p['momentum'] = 0.9 # Momentum
p['epoch_size'] = opts.step # How many epochs to change learning rate
p['num_workers'] = opts.numworker
model_path = opts.pretrainedModel
backbone = 'xception' # Use xception or resnet as feature extractor
nEpochs = opts.epochs
max_id = 0
save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__)))
exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1]
runs = glob.glob(os.path.join(save_dir_root, 'run', 'run_*'))
for r in runs:
run_id = int(r.split('_')[-1])
if run_id >= max_id:
max_id = run_id + 1
# run_id = int(runs[-1].split('_')[-1]) + 1 if runs else 0
save_dir = os.path.join(save_dir_root, 'run', 'run_' + str(max_id))
# Device
if (opts.device == "gpu"):
use_cuda = torch.cuda.is_available()
if (use_cuda == True):
device = torch.device("cuda")
#torch.cuda.set_device(args.gpu_ids[0])
print("実行デバイス :", device)
print("GPU名 :", torch.cuda.get_device_name(device))
print("torch.cuda.current_device() =", torch.cuda.current_device())
else:
print("can't using gpu.")
device = torch.device("cpu")
print("実行デバイス :", device)
else:
device = torch.device("cpu")
print("実行デバイス :", device)
# Network definition
if backbone == 'xception':
net_ = deeplab_xception_universal.deeplab_xception_end2end_3d(
n_classes=20,
os=16,
hidden_layers=opts.hidden_layers,
source_classes=7,
middle_classes=18,
)
elif backbone == 'resnet':
# net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True)
raise NotImplementedError
else:
raise NotImplementedError
modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime(
'%b%d_%H-%M-%S')
criterion = ut.cross_entropy2d
if gpu_id >= 0:
# torch.cuda.set_device(device=gpu_id)
net_.to(device)
# net load weights
if not model_path == '':
x = torch.load(model_path)
net_.load_state_dict_new(x)
print('load pretrainedModel.')
else:
print('no pretrainedModel.')
if not opts.loadmodel == '':
x = torch.load(opts.loadmodel)
net_.load_source_model(x)
print('load model:', opts.loadmodel)
else:
print('no trained model load !!!!!!!!')
print(net_)
log_dir = os.path.join(
save_dir, 'models',
datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname())
writer = SummaryWriter(log_dir=log_dir)
writer.add_text('load model', opts.loadmodel, 1)
writer.add_text('setting', sys.argv[0], 1)
# Use the following optimizer
optimizer = optim.SGD(net_.parameters(),
lr=p['lr'],
momentum=p['momentum'],
weight_decay=p['wd'])
composed_transforms_tr = transforms.Compose([
tr.RandomSized_new(opts.image_size),
tr.Normalize_xception_tf(),
tr.ToTensor_()
])
composed_transforms_ts = transforms.Compose(
[tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts_flip = transforms.Compose(
[tr.HorizontalFlip(),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
#all_train = cihp_pascal_atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True)
#voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts)
#voc_val_flip = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
all_train = cihp_pascal_atr.VOCSegmentation(
cihp_dir="./data/datasets/CIHP_4w",
split='train',
transform=composed_transforms_tr,
flip=True)
#voc_val = pascal.VOCSegmentation(base_dir="./data/datasets/pascal", split='val', transform=composed_transforms_ts)
#voc_val_flip = pascal.VOCSegmentation(base_dir="./data/datasets/pascal", split='val', transform=composed_transforms_ts_flip)
num_cihp, num_pascal, num_atr = all_train.get_class_num()
ss = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch)
# balance datasets based pascal
ss_balanced = sam.Sampler_uni(num_cihp,
num_pascal,
num_atr,
opts.batch,
balance_id=1)
trainloader = DataLoader(all_train,
batch_size=p['trainBatch'],
shuffle=False,
num_workers=p['num_workers'],
sampler=ss,
drop_last=True)
trainloader_balanced = DataLoader(all_train,
batch_size=p['trainBatch'],
shuffle=False,
num_workers=p['num_workers'],
sampler=ss_balanced,
drop_last=True)
#testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=p['num_workers'])
#testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p['num_workers'])
num_img_tr = len(trainloader)
num_img_balanced = len(trainloader_balanced)
#num_img_ts = len(testloader)
num_img_ts = 0
running_loss_tr = 0.0
running_loss_tr_atr = 0.0
running_loss_ts = 0.0
aveGrad = 0
global_step = 0
print("Training Network")
net = torch.nn.DataParallel(net_)
id_list = torch.LongTensor(range(opts.batch))
pascal_iter = int(num_img_tr // opts.batch)
# Get graphs
train_graph, test_graph = get_graphs(opts, device)
adj1, adj2, adj3, adj4, adj5, adj6 = train_graph
adj1_test, adj2_test, adj3_test, adj4_test, adj5_test, adj6_test = test_graph
# Main Training and Testing Loop
for epoch in range(resume_epoch, int(1.5 * nEpochs)):
start_time = timeit.default_timer()
if epoch % p['epoch_size'] == p['epoch_size'] - 1 and epoch < nEpochs:
lr_ = ut.lr_poly(p['lr'], epoch, nEpochs, 0.9)
optimizer = optim.SGD(net_.parameters(),
lr=lr_,
momentum=p['momentum'],
weight_decay=p['wd'])
print('(poly lr policy) learning rate: ', lr_)
writer.add_scalar('data/lr_', lr_, epoch)
elif epoch % p['epoch_size'] == p['epoch_size'] - 1 and epoch > nEpochs:
lr_ = ut.lr_poly(p['lr'], epoch - nEpochs, int(0.5 * nEpochs), 0.9)
optimizer = optim.SGD(net_.parameters(),
lr=lr_,
momentum=p['momentum'],
weight_decay=p['wd'])
print('(poly lr policy) learning rate: ', lr_)
writer.add_scalar('data/lr_', lr_, epoch)
net_.train()
if epoch < nEpochs:
for ii, sample_batched in enumerate(trainloader):
inputs, labels = sample_batched['image'], sample_batched[
'label']
dataset_lbl = sample_batched['pascal'][0].item()
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=True), Variable(labels)
global_step += 1
if gpu_id >= 0:
inputs, labels = inputs.to(device), labels.to(device)
if dataset_lbl == 0:
# 0 is cihp -- target
#print( "inputs.shape : ", inputs.shape ) # torch.Size([batch, 3, 512, 512])
#print( "adj1.shape : ", adj1.shape ) # torch.Size([1, 1, 20, 20])
#print( "adj2.shape : ", adj2.shape ) # torch.Size([1, 1, 7, 7])
#print( "adj3.shape : ", adj3.shape ) # torch.Size([1, 1, 20, 20])
_, outputs, _ = net.forward(
None,
input_target=inputs,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
#print( "outputs.shape : ", outputs.shape ) # torch.Size([2, 20, 512, 512])
elif dataset_lbl == 1:
# pascal is source
outputs, _, _ = net.forward(
inputs,
input_target=None,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
else:
# atr
_, _, outputs = net.forward(
None,
input_target=None,
input_middle=inputs,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
# print(sample_batched['pascal'])
# print(outputs.size(),)
# print(labels)
loss = criterion(outputs, labels, batch_average=True)
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_tr == (num_img_tr - 1):
running_loss_tr = running_loss_tr / num_img_tr
writer.add_scalar('data/total_loss_epoch', running_loss_tr,
epoch)
print('[Epoch: %d, numImages: %5d]' % (epoch, epoch))
print('Loss: %f' % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) +
"\n")
# Backward the averaged gradient
loss /= p['nAveGrad']
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p['nAveGrad'] == 0:
writer.add_scalar('data/total_loss_iter', loss.item(),
global_step)
if dataset_lbl == 0:
writer.add_scalar('data/total_loss_iter_cihp',
loss.item(), global_step)
if dataset_lbl == 1:
writer.add_scalar('data/total_loss_iter_pascal',
loss.item(), global_step)
if dataset_lbl == 2:
writer.add_scalar('data/total_loss_iter_atr',
loss.item(), global_step)
optimizer.step()
optimizer.zero_grad()
# optimizer_gcn.step()
# optimizer_gcn.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each epoch
if ii % (num_img_tr // 10) == 0:
# if ii % (num_img_tr // 4000) == 0:
grid_image = make_grid(inputs[:3].clone().cpu().data,
3,
normalize=True)
writer.add_image('Image', grid_image, global_step)
grid_image = make_grid(ut.decode_seg_map_sequence(
torch.max(outputs[:3], 1)[1].detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255))
writer.add_image('Predicted label', grid_image,
global_step)
grid_image = make_grid(ut.decode_seg_map_sequence(
torch.squeeze(labels[:3], 1).detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255))
writer.add_image('Groundtruth label', grid_image,
global_step)
print('step {} | loss is {}'.format(ii,
loss.cpu().item()),
flush=True)
else:
# Balanced the number of datasets
for ii, sample_batched in enumerate(trainloader_balanced):
inputs, labels = sample_batched['image'], sample_batched[
'label']
dataset_lbl = sample_batched['pascal'][0].item()
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=True), Variable(labels)
global_step += 1
if gpu_id >= 0:
inputs, labels = inputs.to(device), labels.to(device)
if dataset_lbl == 0:
# 0 is cihp -- target
_, outputs, _ = net.forward(
None,
input_target=inputs,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
elif dataset_lbl == 1:
# pascal is source
outputs, _, _ = net.forward(
inputs,
input_target=None,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
else:
# atr
_, _, outputs = net.forward(
None,
input_target=None,
input_middle=inputs,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
# print(sample_batched['pascal'])
# print(outputs.size(),)
# print(labels)
loss = criterion(outputs, labels, batch_average=True)
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_balanced == (num_img_balanced - 1):
running_loss_tr = running_loss_tr / num_img_balanced
writer.add_scalar('data/total_loss_epoch', running_loss_tr,
epoch)
print('[Epoch: %d, numImages: %5d]' % (epoch, epoch))
print('Loss: %f' % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) +
"\n")
# Backward the averaged gradient
loss /= p['nAveGrad']
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p['nAveGrad'] == 0:
writer.add_scalar('data/total_loss_iter', loss.item(),
global_step)
if dataset_lbl == 0:
writer.add_scalar('data/total_loss_iter_cihp',
loss.item(), global_step)
if dataset_lbl == 1:
writer.add_scalar('data/total_loss_iter_pascal',
loss.item(), global_step)
if dataset_lbl == 2:
writer.add_scalar('data/total_loss_iter_atr',
loss.item(), global_step)
optimizer.step()
optimizer.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each epoch
if ii % (num_img_balanced // 10) == 0:
grid_image = make_grid(inputs[:3].clone().cpu().data,
3,
normalize=True)
writer.add_image('Image', grid_image, global_step)
grid_image = make_grid(ut.decode_seg_map_sequence(
torch.max(outputs[:3], 1)[1].detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255))
writer.add_image('Predicted label', grid_image,
global_step)
grid_image = make_grid(ut.decode_seg_map_sequence(
torch.squeeze(labels[:3], 1).detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255))
writer.add_image('Groundtruth label', grid_image,
global_step)
print('loss is ', loss.cpu().item(), flush=True)
# Save the model
if (epoch % snapshot) == snapshot - 1:
torch.save(
net_.state_dict(),
os.path.join(save_dir, 'models',
modelName + '_epoch-' + str(epoch) + '.pth'))
print("Save model at {}\n".format(
os.path.join(save_dir, 'models',
modelName + '_epoch-' + str(epoch) + '.pth')))
# One testing epoch
"""
def main(opts):
p = OrderedDict() # Parameters to include in report
p['trainBatch'] = opts.batch # Training batch size
testBatch = 1 # Testing batch size
useTest = True # See evolution of the test set when training
nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs
snapshot = 1 # Store a model every snapshot epochs
p['nAveGrad'] = 1 # Average the gradient of several iterations
p['lr'] = opts.lr # Learning rate
p['lrFtr'] = 1e-5
p['lraspp'] = 1e-5
p['lrpro'] = 1e-5
p['lrdecoder'] = 1e-5
p['lrother'] = 1e-5
p['wd'] = 5e-4 # Weight decay
p['momentum'] = 0.9 # Momentum
p['epoch_size'] = opts.step # How many epochs to change learning rate
p['num_workers'] = opts.numworker
model_path = opts.pretrainedModel
backbone = 'xception' # Use xception or resnet as feature extractor,
nEpochs = opts.epochs
max_id = 0
save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__)))
exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1]
runs = glob.glob(os.path.join(save_dir_root, 'run_cihp', 'run_*'))
for r in runs:
run_id = int(r.split('_')[-1])
if run_id >= max_id:
max_id = run_id + 1
save_dir = os.path.join(save_dir_root, 'run_cihp', 'run_' + str(max_id))
# Device
if (opts.device == "gpu"):
use_cuda = torch.cuda.is_available()
if (use_cuda == True):
device = torch.device("cuda")
#torch.cuda.set_device(args.gpu_ids[0])
print("実行デバイス :", device)
print("GPU名 :", torch.cuda.get_device_name(device))
print("torch.cuda.current_device() =", torch.cuda.current_device())
else:
print("can't using gpu.")
device = torch.device("cpu")
print("実行デバイス :", device)
else:
device = torch.device("cpu")
print("実行デバイス :", device)
# Network definition
if backbone == 'xception':
net_ = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem(
n_classes=opts.classes,
os=16,
hidden_layers=opts.hidden_layers,
source_classes=7,
)
elif backbone == 'resnet':
# net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True)
raise NotImplementedError
else:
raise NotImplementedError
modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime(
'%b%d_%H-%M-%S')
criterion = util.cross_entropy2d
if gpu_id >= 0:
# torch.cuda.set_device(device=gpu_id)
#net_.cuda()
net_.to(device)
# net load weights
if not model_path == '':
x = torch.load(model_path)
net_.load_state_dict_new(x)
print('load pretrainedModel:', model_path)
else:
print('no pretrainedModel.')
if not opts.loadmodel == '':
x = torch.load(opts.loadmodel)
net_.load_source_model(x)
print('load model:', opts.loadmodel)
else:
print('no model load !!!!!!!!')
log_dir = os.path.join(
save_dir, 'models',
datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname())
writer = SummaryWriter(log_dir=log_dir)
writer.add_text('load model', opts.loadmodel, 1)
writer.add_text('setting', sys.argv[0], 1)
if opts.freezeBN:
net_.freeze_bn()
print(net_)
# Use the following optimizer
optimizer = optim.SGD(net_.parameters(),
lr=p['lr'],
momentum=p['momentum'],
weight_decay=p['wd'])
composed_transforms_tr = transforms.Compose([
tr.RandomSized_new(opts.image_size),
tr.Normalize_xception_tf(),
tr.ToTensor_()
])
composed_transforms_ts = transforms.Compose(
[tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts_flip = transforms.Compose(
[tr.HorizontalFlip(),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
#voc_train = cihp.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True)
#voc_val = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts)
#voc_val_flip = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
voc_train = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w",
split='train',
transform=composed_transforms_tr,
flip=True)
voc_val = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w",
split='val',
transform=composed_transforms_ts)
voc_val_flip = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w",
split='val',
transform=composed_transforms_ts_flip)
trainloader = DataLoader(voc_train,
batch_size=p['trainBatch'],
shuffle=True,
num_workers=p['num_workers'],
drop_last=True)
testloader = DataLoader(voc_val,
batch_size=testBatch,
shuffle=False,
num_workers=p['num_workers'])
testloader_flip = DataLoader(voc_val_flip,
batch_size=testBatch,
shuffle=False,
num_workers=p['num_workers'])
num_img_tr = len(trainloader)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_ts = 0.0
aveGrad = 0
global_step = 0
print("Training Network")
print("num_img_tr : ", num_img_tr)
net = torch.nn.DataParallel(net_)
train_graph, test_graph = get_graphs(opts, device)
adj1, adj2, adj3 = train_graph
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
start_time = timeit.default_timer()
if epoch % p['epoch_size'] == p['epoch_size'] - 1:
lr_ = util.lr_poly(p['lr'], epoch, nEpochs, 0.9)
optimizer = optim.SGD(net_.parameters(),
lr=lr_,
momentum=p['momentum'],
weight_decay=p['wd'])
writer.add_scalar('data/lr_', lr_, epoch)
print('(poly lr policy) learning rate: ', lr_)
net.train()
for ii, sample_batched in enumerate(trainloader):
inputs, labels = sample_batched['image'], sample_batched['label']
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=True), Variable(labels)
global_step += inputs.data.shape[0]
if gpu_id >= 0:
#inputs, labels = inputs.cuda(), labels.cuda()
inputs, labels = inputs.to(device), labels.to(device)
#print( "inputs.shape : ", inputs.shape ) # torch.Size([batch, 3, 512, 512])
#print( "adj1.shape : ", adj1.shape ) # torch.Size([8, 1, 20, 20])
#print( "adj2.shape : ", adj2.shape ) # torch.Size([8, 1, 20, 7])
#print( "adj3.shape : ", adj3.shape ) # torch.Size([8, 1, 7, 7])
outputs = net.forward(inputs, adj1, adj3, adj2)
#print( "outputs.shape : ", outputs.shape ) # torch.Size([2, 20, 512, 512])
loss = criterion(outputs, labels, batch_average=True)
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_tr == (num_img_tr - 1):
running_loss_tr = running_loss_tr / num_img_tr
writer.add_scalar('data/total_loss_epoch', running_loss_tr,
epoch)
print('[Epoch: %d, numImages: %5d]' %
(epoch, ii * p['trainBatch'] + inputs.data.shape[0]))
print('Loss: %f' % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
# Backward the averaged gradient
loss /= p['nAveGrad']
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p['nAveGrad'] == 0:
writer.add_scalar('data/total_loss_iter', loss.item(),
ii + num_img_tr * epoch)
optimizer.step()
optimizer.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each epoch
if ii % (num_img_tr // 10) == 0:
# if ii % (num_img_tr // 4000) == 0:
grid_image = make_grid(inputs[:3].clone().cpu().data,
3,
normalize=True)
writer.add_image('Image', grid_image, global_step)
grid_image = make_grid(util.decode_seg_map_sequence(
torch.max(outputs[:3], 1)[1].detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255))
writer.add_image('Predicted label', grid_image, global_step)
grid_image = make_grid(util.decode_seg_map_sequence(
torch.squeeze(labels[:3], 1).detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255))
writer.add_image('Groundtruth label', grid_image, global_step)
print('loss is ', loss.cpu().item(), flush=True)
# Save the model
if (epoch % snapshot) == snapshot - 1:
torch.save(
net_.state_dict(),
os.path.join(save_dir, 'models',
modelName + '_epoch-' + str(epoch) + '.pth'))
print("Save model at {}\n".format(
os.path.join(save_dir, 'models',
modelName + '_epoch-' + str(epoch) + '.pth')))
torch.cuda.empty_cache()
# One testing epoch
if useTest and epoch % nTestInterval == (nTestInterval - 1):
val_cihp(net_,
testloader=testloader,
testloader_flip=testloader_flip,
test_graph=test_graph,
epoch=epoch,
writer=writer,
criterion=criterion,
classes=opts.classes,
device=device)
torch.cuda.empty_cache()
def main(opts):
p = OrderedDict() # Parameters to include in report
p["trainBatch"] = opts.batch # Training batch size
testBatch = 1 # Testing batch size
useTest = True # See evolution of the test set when training
nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs
snapshot = 1 # Store a model every snapshot epochs
p["nAveGrad"] = 1 # Average the gradient of several iterations
p["lr"] = opts.lr # Learning rate
p["lrFtr"] = 1e-5
p["lraspp"] = 1e-5
p["lrpro"] = 1e-5
p["lrdecoder"] = 1e-5
p["lrother"] = 1e-5
p["wd"] = 5e-4 # Weight decay
p["momentum"] = 0.9 # Momentum
p["epoch_size"] = opts.step # How many epochs to change learning rate
p["num_workers"] = opts.numworker
model_path = opts.pretrainedModel
backbone = "xception" # Use xception or resnet as feature extractor,
nEpochs = opts.epochs
max_id = 0
save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__)))
exp_name = os.path.dirname(os.path.abspath(__file__)).split("/")[-1]
runs = glob.glob(os.path.join(save_dir_root, "run_cihp", "run_*"))
for r in runs:
run_id = int(r.split("_")[-1])
if run_id >= max_id:
max_id = run_id + 1
save_dir = os.path.join(save_dir_root, "run_cihp", "run_" + str(max_id))
# Network definition
if backbone == "xception":
net_ = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem(
n_classes=opts.classes,
os=16,
hidden_layers=opts.hidden_layers,
source_classes=7,
)
elif backbone == "resnet":
# net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True)
raise NotImplementedError
else:
raise NotImplementedError
modelName = ("deeplabv3plus-" + backbone + "-voc" +
datetime.now().strftime("%b%d_%H-%M-%S"))
criterion = util.cross_entropy2d
if gpu_id >= 0:
# torch.cuda.set_device(device=gpu_id)
net_.cuda()
# net load weights
if not model_path == "":
x = torch.load(model_path)
net_.load_state_dict_new(x)
print("load pretrainedModel:", model_path)
else:
print("no pretrainedModel.")
if not opts.loadmodel == "":
x = torch.load(opts.loadmodel)
net_.load_source_model(x)
print("load model:", opts.loadmodel)
else:
print("no model load !!!!!!!!")
log_dir = os.path.join(
save_dir,
"models",
datetime.now().strftime("%b%d_%H-%M-%S") + "_" + socket.gethostname(),
)
writer = SummaryWriter(log_dir=log_dir)
writer.add_text("load model", opts.loadmodel, 1)
writer.add_text("setting", sys.argv[0], 1)
if opts.freezeBN:
net_.freeze_bn()
# Use the following optimizer
optimizer = optim.SGD(net_.parameters(),
lr=p["lr"],
momentum=p["momentum"],
weight_decay=p["wd"])
composed_transforms_tr = transforms.Compose(
[tr.RandomSized_new(512),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts = transforms.Compose(
[tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts_flip = transforms.Compose(
[tr.HorizontalFlip(),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
voc_train = cihp.VOCSegmentation(split="train",
transform=composed_transforms_tr,
flip=True)
voc_val = cihp.VOCSegmentation(split="val",
transform=composed_transforms_ts)
voc_val_flip = cihp.VOCSegmentation(split="val",
transform=composed_transforms_ts_flip)
trainloader = DataLoader(
voc_train,
batch_size=p["trainBatch"],
shuffle=True,
num_workers=p["num_workers"],
drop_last=True,
)
testloader = DataLoader(voc_val,
batch_size=testBatch,
shuffle=False,
num_workers=p["num_workers"])
testloader_flip = DataLoader(voc_val_flip,
batch_size=testBatch,
shuffle=False,
num_workers=p["num_workers"])
num_img_tr = len(trainloader)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_ts = 0.0
aveGrad = 0
global_step = 0
print("Training Network")
net = torch.nn.DataParallel(net_)
train_graph, test_graph = get_graphs(opts)
adj1, adj2, adj3 = train_graph
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
start_time = timeit.default_timer()
if epoch % p["epoch_size"] == p["epoch_size"] - 1:
lr_ = util.lr_poly(p["lr"], epoch, nEpochs, 0.9)
optimizer = optim.SGD(net_.parameters(),
lr=lr_,
momentum=p["momentum"],
weight_decay=p["wd"])
writer.add_scalar("data/lr_", lr_, epoch)
print("(poly lr policy) learning rate: ", lr_)
net.train()
for ii, sample_batched in enumerate(trainloader):
inputs, labels = sample_batched["image"], sample_batched["label"]
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=True), Variable(labels)
global_step += inputs.data.shape[0]
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
outputs = net.forward(inputs, adj1, adj3, adj2)
loss = criterion(outputs, labels, batch_average=True)
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_tr == (num_img_tr - 1):
running_loss_tr = running_loss_tr / num_img_tr
writer.add_scalar("data/total_loss_epoch", running_loss_tr,
epoch)
print("[Epoch: %d, numImages: %5d]" %
(epoch, ii * p["trainBatch"] + inputs.data.shape[0]))
print("Loss: %f" % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
# Backward the averaged gradient
loss /= p["nAveGrad"]
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p["nAveGrad"] == 0:
writer.add_scalar("data/total_loss_iter", loss.item(),
ii + num_img_tr * epoch)
optimizer.step()
optimizer.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each epoch
if ii % (num_img_tr // 10) == 0:
grid_image = make_grid(inputs[:3].clone().cpu().data,
3,
normalize=True)
writer.add_image("Image", grid_image, global_step)
grid_image = make_grid(
util.decode_seg_map_sequence(
torch.max(outputs[:3], 1)[1].detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255),
)
writer.add_image("Predicted label", grid_image, global_step)
grid_image = make_grid(
util.decode_seg_map_sequence(
torch.squeeze(labels[:3], 1).detach().cpu().numpy()),
3,
normalize=False,
range=(0, 255),
)
writer.add_image("Groundtruth label", grid_image, global_step)
print("loss is ", loss.cpu().item(), flush=True)
# Save the model
if (epoch % snapshot) == snapshot - 1:
torch.save(
net_.state_dict(),
os.path.join(save_dir, "models",
modelName + "_epoch-" + str(epoch) + ".pth"),
)
print("Save model at {}\n".format(
os.path.join(save_dir, "models",
modelName + "_epoch-" + str(epoch) + ".pth")))
torch.cuda.empty_cache()
# One testing epoch
if useTest and epoch % nTestInterval == (nTestInterval - 1):
val_cihp(
net_,
testloader=testloader,
testloader_flip=testloader_flip,
test_graph=test_graph,
epoch=epoch,
writer=writer,
criterion=criterion,
classes=opts.classes,
)
torch.cuda.empty_cache()
return _img, _target, type_lbl
def __str__(self):
return 'datasets(split=' + str(self.split) + ')'
if __name__ == '__main__':
from dataloaders import custom_transforms as tr
from dataloaders.utils import decode_segmap
from torch.utils.data import DataLoader
from torchvision import transforms
import matplotlib.pyplot as plt
composed_transforms_tr = transforms.Compose([
# tr.RandomHorizontalFlip(),
tr.RandomSized_new(512),
tr.RandomRotate(15),
tr.ToTensor_()
])
voc_train = VOCSegmentation(split='train',
transform=composed_transforms_tr)
dataloader = DataLoader(voc_train,
batch_size=5,
shuffle=True,
num_workers=1)
for ii, sample in enumerate(dataloader):
if ii > 10:
break
def main(opts):
# Some of the settings are not used
p = OrderedDict() # Parameters to include in report
p['trainBatch'] = opts.batch # Training batch size
testBatch = 1 # Testing batch size
useTest = True # See evolution of the test set when training
nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs
snapshot = 1 # Store a model every snapshot epochs
p['nAveGrad'] = 1 # Average the gradient of several iterations
p['lr'] = opts.lr # Learning rate
p['lrFtr'] = 1e-5
p['lraspp'] = 1e-5
p['lrpro'] = 1e-5
p['lrdecoder'] = 1e-5
p['lrother'] = 1e-5
p['wd'] = 5e-4 # Weight decay
p['momentum'] = 0.9 # Momentum
p['epoch_size'] = opts.step # How many epochs to change learning rate
p['num_workers'] = opts.numworker
backbone = 'xception' # Use xception or resnet as feature extractor,
nEpochs = opts.epochs
resume_epoch = opts.resume_epoch
max_id = 0
save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__)))
exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1]
runs = glob.glob(os.path.join(save_dir_root, 'run_cihp', 'run_*'))
for r in runs:
run_id = int(r.split('_')[-1])
if run_id >= max_id:
max_id = run_id + 1
save_dir = os.path.join(save_dir_root, 'run_cihp', 'run_' + str(max_id))
print(save_dir)
# Network definition
net_ = grapy_net.GrapyMutualLearning(os=16, hidden_layers=opts.hidden_graph_layers)
modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime('%b%d_%H-%M-%S')
criterion = util.cross_entropy2d
log_dir = os.path.join(save_dir, 'models', datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname())
writer = SummaryWriter(log_dir=log_dir)
writer.add_text('load model', opts.loadmodel, 1)
writer.add_text('setting', sys.argv[0], 1)
# Use the following optimizer
optimizer = optim.SGD(net_.parameters(), lr=p['lr'], momentum=p['momentum'], weight_decay=p['wd'])
composed_transforms_tr = transforms.Compose([
tr.RandomSized_new(512),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts = transforms.Compose([
tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts_flip = transforms.Compose([
tr.HorizontalFlip(),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
if opts.train_mode == 'cihp_pascal_atr':
all_train = cihp_pascal_atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True)
num_cihp, num_pascal, num_atr = all_train.get_class_num()
voc_val = atr.VOCSegmentation(split='val', transform=composed_transforms_ts)
voc_val_flip = atr.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
ss = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch)
trainloader = DataLoader(all_train, batch_size=p['trainBatch'], shuffle=False, num_workers=18, sampler=ss, drop_last=True)
elif opts.train_mode == 'cihp_pascal_atr_1_1_1':
all_train = cihp_pascal_atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True)
num_cihp, num_pascal, num_atr = all_train.get_class_num()
voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts)
voc_val_flip = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
ss_uni = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch, balance_id=1)
trainloader = DataLoader(all_train, batch_size=p['trainBatch'], shuffle=False, num_workers=1, sampler=ss_uni, drop_last=True)
elif opts.train_mode == 'cihp':
voc_train = cihp.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True)
voc_val = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts)
voc_val_flip = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=18, drop_last=True)
elif opts.train_mode == 'pascal':
# here we train without flip but test with flip
voc_train = pascal_flip.VOCSegmentation(split='train', transform=composed_transforms_tr)
voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts)
voc_val_flip = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=18, drop_last=True)
elif opts.train_mode == 'atr':
# here we train without flip but test with flip
voc_train = atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True)
voc_val = atr.VOCSegmentation(split='val', transform=composed_transforms_ts)
voc_val_flip = atr.VOCSegmentation(split='val', transform=composed_transforms_ts_flip)
trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=18, drop_last=True)
else:
raise NotImplementedError
if not opts.loadmodel == '':
x = torch.load(opts.loadmodel)
net_.load_state_dict_new(x, strict=False)
print('load model:', opts.loadmodel)
else:
print('no model load !!!!!!!!')
if not opts.resume_model == '':
x = torch.load(opts.resume_model)
net_.load_state_dict(x)
print('resume model:', opts.resume_model)
else:
print('we are not resuming from any model')
# We only validate on pascal dataset to save time
testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=3)
testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=3)
num_img_tr = len(trainloader)
num_img_ts = len(testloader)
# Set the category relations
c1, c2, p1, p2, a1, a2 = [[0], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]],\
[[0], [1, 2, 4, 13], [5, 6, 7, 10, 11, 12], [3, 14, 15], [8, 9, 16, 17, 18, 19]], \
[[0], [1, 2, 3, 4, 5, 6]], [[0], [1], [2], [3, 4], [5, 6]], [[0], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]],\
[[0], [1, 2, 3, 11], [4, 5, 7, 8, 16, 17], [14, 15], [6, 9, 10, 12, 13]]
net_.set_category_list(c1, c2, p1, p2, a1, a2)
if gpu_id >= 0:
# torch.cuda.set_device(device=gpu_id)
net_.cuda()
running_loss_tr = 0.0
running_loss_ts = 0.0
running_loss_tr_main = 0.0
running_loss_tr_aux = 0.0
aveGrad = 0
global_step = 0
miou = 0
cur_miou = 0
print("Training Network")
net = torch.nn.DataParallel(net_)
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
start_time = timeit.default_timer()
if opts.poly:
if epoch % p['epoch_size'] == p['epoch_size'] - 1:
lr_ = util.lr_poly(p['lr'], epoch, nEpochs, 0.9)
optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd'])
writer.add_scalar('data/lr_', lr_, epoch)
print('(poly lr policy) learning rate: ', lr_)
net.train()
for ii, sample_batched in enumerate(trainloader):
inputs, labels = sample_batched['image'], sample_batched['label']
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs, requires_grad=True), Variable(labels)
global_step += inputs.data.shape[0]
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
if opts.train_mode == 'cihp_pascal_atr' or opts.train_mode == 'cihp_pascal_atr_1_1_1':
num_dataset_lbl = sample_batched['pascal'][0].item()
elif opts.train_mode == 'cihp':
num_dataset_lbl = 0
elif opts.train_mode == 'pascal':
num_dataset_lbl = 1
else:
num_dataset_lbl = 2
outputs, outputs_aux = net.forward((inputs, num_dataset_lbl))
# print(inputs.shape, labels.shape, outputs.shape, outputs_aux.shape)
loss_main = criterion(outputs, labels, batch_average=True)
loss_aux = criterion(outputs_aux, labels, batch_average=True)
loss = opts.beta_main * loss_main + opts.beta_aux * loss_aux
running_loss_tr_main += loss_main.item()
running_loss_tr_aux += loss_aux.item()
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_tr == (num_img_tr - 1):
running_loss_tr = running_loss_tr / num_img_tr
running_loss_tr_aux = running_loss_tr_aux / num_img_tr
running_loss_tr_main = running_loss_tr_main / num_img_tr
writer.add_scalar('data/total_loss_epoch', running_loss_tr, epoch)
writer.add_scalars('data/scalar_group', {'loss': running_loss_tr_main,
'loss_aux': running_loss_tr_aux}, epoch)
print('[Epoch: %d, numImages: %5d]' % (epoch, ii * p['trainBatch'] + inputs.data.shape[0]))
print('Loss: %f' % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
# Backward the averaged gradient
loss /= p['nAveGrad']
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p['nAveGrad'] == 0:
writer.add_scalar('data/total_loss_iter', loss.item(), ii + num_img_tr * epoch)
if num_dataset_lbl == 0:
writer.add_scalar('data/total_loss_iter_cihp', loss.item(), global_step)
if num_dataset_lbl == 1:
writer.add_scalar('data/total_loss_iter_pascal', loss.item(), global_step)
if num_dataset_lbl == 2:
writer.add_scalar('data/total_loss_iter_atr', loss.item(), global_step)
optimizer.step()
optimizer.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each
# print(ii, (num_img_tr * 10), (ii % (num_img_tr * 10) == 0))
if ii % (num_img_tr * 10) == 0:
grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True)
writer.add_image('Image', grid_image, global_step)
grid_image = make_grid(
util.decode_seg_map_sequence(torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3,
normalize=False,
range=(0, 255))
writer.add_image('Predicted label', grid_image, global_step)
grid_image = make_grid(
util.decode_seg_map_sequence(torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3,
normalize=False, range=(0, 255))
writer.add_image('Groundtruth label', grid_image, global_step)
print('loss is ', loss.cpu().item(), flush=True)
# Save the model
# One testing epoch
if useTest and epoch % nTestInterval == (nTestInterval - 1):
cur_miou = validation(net_, testloader=testloader, testloader_flip=testloader_flip, classes=opts.classes,
epoch=epoch, writer=writer, criterion=criterion, dataset=opts.train_mode)
torch.cuda.empty_cache()
if (epoch % snapshot) == snapshot - 1:
torch.save(net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_epoch' + '_current' + '.pth'))
print("Save model at {}\n".format(
os.path.join(save_dir, 'models', modelName + str(epoch) + '_epoch-' + str(epoch) + '.pth as our current model')))
if cur_miou > miou:
miou = cur_miou
torch.save(net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_best' + '.pth'))
print("Save model at {}\n".format(
os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth as our best model')))
torch.cuda.empty_cache()
def main(opts):
# Set parameters
p = OrderedDict() # Parameters to include in report
p["trainBatch"] = opts.batch # Training batch size
testBatch = 1 # Testing batch size
useTest = True # See evolution of the test set when training
nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs
snapshot = 1 # Store a model every snapshot epochs
p["nAveGrad"] = 1 # Average the gradient of several iterations
p["lr"] = opts.lr # Learning rate
p["wd"] = 5e-4 # Weight decay
p["momentum"] = 0.9 # Momentum
p["epoch_size"] = opts.step # How many epochs to change learning rate
p["num_workers"] = opts.numworker
model_path = opts.pretrainedModel
backbone = "xception" # Use xception or resnet as feature extractor
nEpochs = opts.epochs
max_id = 0
save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__)))
exp_name = os.path.dirname(os.path.abspath(__file__)).split("/")[-1]
runs = glob.glob(os.path.join(save_dir_root, "run", "run_*"))
for r in runs:
run_id = int(r.split("_")[-1])
if run_id >= max_id:
max_id = run_id + 1
# run_id = int(runs[-1].split('_')[-1]) + 1 if runs else 0
save_dir = os.path.join(save_dir_root, "run", "run_" + str(max_id))
# Network definition
if backbone == "xception":
net_ = deeplab_xception_universal.deeplab_xception_end2end_3d(
n_classes=20,
os=16,
hidden_layers=opts.hidden_layers,
source_classes=7,
middle_classes=18,
)
elif backbone == "resnet":
# net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True)
raise NotImplementedError
else:
raise NotImplementedError
modelName = (
"deeplabv3plus-" + backbone + "-voc" + datetime.now().strftime("%b%d_%H-%M-%S")
)
criterion = ut.cross_entropy2d
if gpu_id >= 0:
# torch.cuda.set_device(device=gpu_id)
net_.cuda()
# net load weights
if not model_path == "":
x = torch.load(model_path)
net_.load_state_dict_new(x)
print("load pretrainedModel.")
else:
print("no pretrainedModel.")
if not opts.loadmodel == "":
x = torch.load(opts.loadmodel)
net_.load_source_model(x)
print("load model:", opts.loadmodel)
else:
print("no trained model load !!!!!!!!")
log_dir = os.path.join(
save_dir,
"models",
datetime.now().strftime("%b%d_%H-%M-%S") + "_" + socket.gethostname(),
)
writer = SummaryWriter(log_dir=log_dir)
writer.add_text("load model", opts.loadmodel, 1)
writer.add_text("setting", sys.argv[0], 1)
# Use the following optimizer
optimizer = optim.SGD(
net_.parameters(), lr=p["lr"], momentum=p["momentum"], weight_decay=p["wd"]
)
composed_transforms_tr = transforms.Compose(
[tr.RandomSized_new(512), tr.Normalize_xception_tf(), tr.ToTensor_()]
)
composed_transforms_ts = transforms.Compose(
[tr.Normalize_xception_tf(), tr.ToTensor_()]
)
composed_transforms_ts_flip = transforms.Compose(
[tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]
)
all_train = cihp_pascal_atr.VOCSegmentation(
split="train", transform=composed_transforms_tr, flip=True
)
voc_val = pascal.VOCSegmentation(split="val", transform=composed_transforms_ts)
voc_val_flip = pascal.VOCSegmentation(
split="val", transform=composed_transforms_ts_flip
)
num_cihp, num_pascal, num_atr = all_train.get_class_num()
ss = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch)
# balance datasets based pascal
ss_balanced = sam.Sampler_uni(
num_cihp, num_pascal, num_atr, opts.batch, balance_id=1
)
trainloader = DataLoader(
all_train,
batch_size=p["trainBatch"],
shuffle=False,
num_workers=p["num_workers"],
sampler=ss,
drop_last=True,
)
trainloader_balanced = DataLoader(
all_train,
batch_size=p["trainBatch"],
shuffle=False,
num_workers=p["num_workers"],
sampler=ss_balanced,
drop_last=True,
)
testloader = DataLoader(
voc_val, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"]
)
testloader_flip = DataLoader(
voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"]
)
num_img_tr = len(trainloader)
num_img_balanced = len(trainloader_balanced)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_tr_atr = 0.0
running_loss_ts = 0.0
aveGrad = 0
global_step = 0
print("Training Network")
net = torch.nn.DataParallel(net_)
id_list = torch.LongTensor(range(opts.batch))
pascal_iter = int(num_img_tr // opts.batch)
# Get graphs
train_graph, test_graph = get_graphs(opts)
adj1, adj2, adj3, adj4, adj5, adj6 = train_graph
adj1_test, adj2_test, adj3_test, adj4_test, adj5_test, adj6_test = test_graph
# Main Training and Testing Loop
for epoch in range(resume_epoch, int(1.5 * nEpochs)):
start_time = timeit.default_timer()
if epoch % p["epoch_size"] == p["epoch_size"] - 1 and epoch < nEpochs:
lr_ = ut.lr_poly(p["lr"], epoch, nEpochs, 0.9)
optimizer = optim.SGD(
net_.parameters(), lr=lr_, momentum=p["momentum"], weight_decay=p["wd"]
)
print("(poly lr policy) learning rate: ", lr_)
writer.add_scalar("data/lr_", lr_, epoch)
elif epoch % p["epoch_size"] == p["epoch_size"] - 1 and epoch > nEpochs:
lr_ = ut.lr_poly(p["lr"], epoch - nEpochs, int(0.5 * nEpochs), 0.9)
optimizer = optim.SGD(
net_.parameters(), lr=lr_, momentum=p["momentum"], weight_decay=p["wd"]
)
print("(poly lr policy) learning rate: ", lr_)
writer.add_scalar("data/lr_", lr_, epoch)
net_.train()
if epoch < nEpochs:
for ii, sample_batched in enumerate(trainloader):
inputs, labels = sample_batched["image"], sample_batched["label"]
dataset_lbl = sample_batched["pascal"][0].item()
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs, requires_grad=True), Variable(labels)
global_step += 1
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
if dataset_lbl == 0:
# 0 is cihp -- target
_, outputs, _ = net.forward(
None,
input_target=inputs,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
elif dataset_lbl == 1:
# pascal is source
outputs, _, _ = net.forward(
inputs,
input_target=None,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
else:
# atr
_, _, outputs = net.forward(
None,
input_target=None,
input_middle=inputs,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
# print(sample_batched['pascal'])
# print(outputs.size(),)
# print(labels)
loss = criterion(outputs, labels, batch_average=True)
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_tr == (num_img_tr - 1):
running_loss_tr = running_loss_tr / num_img_tr
writer.add_scalar("data/total_loss_epoch", running_loss_tr, epoch)
print("[Epoch: %d, numImages: %5d]" % (epoch, epoch))
print("Loss: %f" % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
# Backward the averaged gradient
loss /= p["nAveGrad"]
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p["nAveGrad"] == 0:
writer.add_scalar("data/total_loss_iter", loss.item(), global_step)
if dataset_lbl == 0:
writer.add_scalar(
"data/total_loss_iter_cihp", loss.item(), global_step
)
if dataset_lbl == 1:
writer.add_scalar(
"data/total_loss_iter_pascal", loss.item(), global_step
)
if dataset_lbl == 2:
writer.add_scalar(
"data/total_loss_iter_atr", loss.item(), global_step
)
optimizer.step()
optimizer.zero_grad()
# optimizer_gcn.step()
# optimizer_gcn.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each epoch
if ii % (num_img_tr // 10) == 0:
grid_image = make_grid(
inputs[:3].clone().cpu().data, 3, normalize=True
)
writer.add_image("Image", grid_image, global_step)
grid_image = make_grid(
ut.decode_seg_map_sequence(
torch.max(outputs[:3], 1)[1].detach().cpu().numpy()
),
3,
normalize=False,
range=(0, 255),
)
writer.add_image("Predicted label", grid_image, global_step)
grid_image = make_grid(
ut.decode_seg_map_sequence(
torch.squeeze(labels[:3], 1).detach().cpu().numpy()
),
3,
normalize=False,
range=(0, 255),
)
writer.add_image("Groundtruth label", grid_image, global_step)
print("loss is ", loss.cpu().item(), flush=True)
else:
# Balanced the number of datasets
for ii, sample_batched in enumerate(trainloader_balanced):
inputs, labels = sample_batched["image"], sample_batched["label"]
dataset_lbl = sample_batched["pascal"][0].item()
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs, requires_grad=True), Variable(labels)
global_step += 1
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
if dataset_lbl == 0:
# 0 is cihp -- target
_, outputs, _ = net.forward(
None,
input_target=inputs,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
elif dataset_lbl == 1:
# pascal is source
outputs, _, _ = net.forward(
inputs,
input_target=None,
input_middle=None,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
else:
# atr
_, _, outputs = net.forward(
None,
input_target=None,
input_middle=inputs,
adj1_target=adj1,
adj2_source=adj2,
adj3_transfer_s2t=adj3,
adj3_transfer_t2s=adj3.transpose(2, 3),
adj4_middle=adj4,
adj5_transfer_s2m=adj5.transpose(2, 3),
adj6_transfer_t2m=adj6.transpose(2, 3),
adj5_transfer_m2s=adj5,
adj6_transfer_m2t=adj6,
)
# print(sample_batched['pascal'])
# print(outputs.size(),)
# print(labels)
loss = criterion(outputs, labels, batch_average=True)
running_loss_tr += loss.item()
# Print stuff
if ii % num_img_balanced == (num_img_balanced - 1):
running_loss_tr = running_loss_tr / num_img_balanced
writer.add_scalar("data/total_loss_epoch", running_loss_tr, epoch)
print("[Epoch: %d, numImages: %5d]" % (epoch, epoch))
print("Loss: %f" % running_loss_tr)
running_loss_tr = 0
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
# Backward the averaged gradient
loss /= p["nAveGrad"]
loss.backward()
aveGrad += 1
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % p["nAveGrad"] == 0:
writer.add_scalar("data/total_loss_iter", loss.item(), global_step)
if dataset_lbl == 0:
writer.add_scalar(
"data/total_loss_iter_cihp", loss.item(), global_step
)
if dataset_lbl == 1:
writer.add_scalar(
"data/total_loss_iter_pascal", loss.item(), global_step
)
if dataset_lbl == 2:
writer.add_scalar(
"data/total_loss_iter_atr", loss.item(), global_step
)
optimizer.step()
optimizer.zero_grad()
aveGrad = 0
# Show 10 * 3 images results each epoch
if ii % (num_img_balanced // 10) == 0:
grid_image = make_grid(
inputs[:3].clone().cpu().data, 3, normalize=True
)
writer.add_image("Image", grid_image, global_step)
grid_image = make_grid(
ut.decode_seg_map_sequence(
torch.max(outputs[:3], 1)[1].detach().cpu().numpy()
),
3,
normalize=False,
range=(0, 255),
)
writer.add_image("Predicted label", grid_image, global_step)
grid_image = make_grid(
ut.decode_seg_map_sequence(
torch.squeeze(labels[:3], 1).detach().cpu().numpy()
),
3,
normalize=False,
range=(0, 255),
)
writer.add_image("Groundtruth label", grid_image, global_step)
print("loss is ", loss.cpu().item(), flush=True)
# Save the model
if (epoch % snapshot) == snapshot - 1:
torch.save(
net_.state_dict(),
os.path.join(
save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth"
),
)
print(
"Save model at {}\n".format(
os.path.join(
save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth"
)
)
)
# One testing epoch
if useTest and epoch % nTestInterval == (nTestInterval - 1):
val_pascal(
net_=net_,
testloader=testloader,
testloader_flip=testloader_flip,
test_graph=test_graph,
criterion=criterion,
epoch=epoch,
writer=writer,
)
