def getModel(sourcemodel):
net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem(
n_classes=20,
hidden_layers=128,
source_classes=7,
)
x = torch.load(sourcemodel)
net.load_source_model(x)
net.cuda()
return net
def inference_batch(sourcemodel, imgPaths):
# launch gpu model
net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem(
n_classes=20,
hidden_layers=128,
source_classes=7,
)
x = torch.load(sourcemodel)
net.load_source_model(x)
net.cuda()
n = len(imgPaths)
for i, imgpath in enumerate(imgPaths):
print('Running Segmentation on {}: No. {}/{}'.format(
imgpath, i + 1, n))
dirname, basename = os.path.split(imgpath)
basename = '.'.join(basename.split('.')[:-1])
inferenceWrite(net, imgpath, dirname, basename)
if __name__ == "__main__":
"""argparse begin"""
parser = argparse.ArgumentParser()
# parser.add_argument('--loadmodel',default=None,type=str)
parser.add_argument("--loadmodel", default="", type=str)
parser.add_argument("--img_path", default="", type=str)
parser.add_argument("--output_path", default="", type=str)
parser.add_argument("--output_name", default="", type=str)
parser.add_argument("--use_gpu", default=1, type=int)
opts = parser.parse_args()
net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem(
n_classes=20,
hidden_layers=128,
source_classes=7,
)
if not opts.loadmodel == "":
x = torch.load(opts.loadmodel)
net.load_source_model(x)
print("load model:", opts.loadmodel)
else:
print("no model load !!!!!!!!")
raise RuntimeError("No model!!!!")
if opts.use_gpu > 0:
net.cuda()
use_gpu = True
else:
use_gpu = False
def main(opts):
adj2_ = torch.from_numpy(graph.cihp2pascal_nlp_adj).float()
adj2_test = (adj2_.unsqueeze(0).unsqueeze(0).expand(1, 1, 7,
20).cuda().transpose(
2, 3))
adj1_ = Variable(
torch.from_numpy(graph.preprocess_adj(graph.pascal_graph)).float())
adj3_test = adj1_.unsqueeze(0).unsqueeze(0).expand(1, 1, 7, 7).cuda()
cihp_adj = graph.preprocess_adj(graph.cihp_graph)
adj3_ = Variable(torch.from_numpy(cihp_adj).float())
adj1_test = adj3_.unsqueeze(0).unsqueeze(0).expand(1, 1, 20, 20).cuda()
p = OrderedDict() # Parameters to include in report
p["trainBatch"] = opts.batch # Training batch size
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"] = 10 # How many epochs to change learning rate
p["num_workers"] = opts.numworker
backbone = "xception" # Use xception or resnet as feature extractor,
with open(opts.txt_file, "r") as f:
img_list = f.readlines()
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
# 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
if gpu_id >= 0:
net.cuda()
# net load weights
if not opts.loadmodel == "":
x = torch.load(opts.loadmodel)
net.load_source_model(x)
print("load model:", opts.loadmodel)
else:
print("no model load !!!!!!!!")
## multi scale
scale_list = [1, 0.5, 0.75, 1.25, 1.5, 1.75]
testloader_list = []
testloader_flip_list = []
for pv in scale_list:
composed_transforms_ts = transforms.Compose(
[tr.Scale_(pv),
tr.Normalize_xception_tf(),
tr.ToTensor_()])
composed_transforms_ts_flip = transforms.Compose([
tr.Scale_(pv),
tr.HorizontalFlip(),
tr.Normalize_xception_tf(),
tr.ToTensor_(),
])
voc_val = cihp.VOCSegmentation(split="test",
transform=composed_transforms_ts)
voc_val_f = cihp.VOCSegmentation(split="test",
transform=composed_transforms_ts_flip)
testloader = DataLoader(voc_val,
batch_size=1,
shuffle=False,
num_workers=p["num_workers"])
testloader_flip = DataLoader(voc_val_f,
batch_size=1,
shuffle=False,
num_workers=p["num_workers"])
testloader_list.append(copy.deepcopy(testloader))
testloader_flip_list.append(copy.deepcopy(testloader_flip))
print("Eval Network")
if not os.path.exists(opts.output_path + "cihp_output_vis/"):
os.makedirs(opts.output_path + "cihp_output_vis/")
if not os.path.exists(opts.output_path + "cihp_output/"):
os.makedirs(opts.output_path + "cihp_output/")
start_time = timeit.default_timer()
# One testing epoch
total_iou = 0.0
net.eval()
for ii, large_sample_batched in enumerate(
zip(*testloader_list, *testloader_flip_list)):
print(ii)
# 1 0.5 0.75 1.25 1.5 1.75 ; flip:
sample1 = large_sample_batched[:6]
sample2 = large_sample_batched[6:]
for iii, sample_batched in enumerate(zip(sample1, sample2)):
inputs, labels = sample_batched[0]["image"], sample_batched[0][
"label"]
inputs_f, _ = sample_batched[1]["image"], sample_batched[1][
"label"]
inputs = torch.cat((inputs, inputs_f), dim=0)
if iii == 0:
_, _, h, w = inputs.size()
# assert inputs.size() == inputs_f.size()
# Forward pass of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=False), Variable(labels)
with torch.no_grad():
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
# outputs = net.forward(inputs)
# pdb.set_trace()
outputs = net.forward(inputs, adj1_test.cuda(),
adj3_test.cuda(), adj2_test.cuda())
outputs = (outputs[0] +
flip(flip_cihp(outputs[1]), dim=-1)) / 2
outputs = outputs.unsqueeze(0)
if iii > 0:
outputs = F.upsample(outputs,
size=(h, w),
mode="bilinear",
align_corners=True)
outputs_final = outputs_final + outputs
else:
outputs_final = outputs.clone()
################ plot pic
predictions = torch.max(outputs_final, 1)[1]
prob_predictions = torch.max(outputs_final, 1)[0]
results = predictions.cpu().numpy()
prob_results = prob_predictions.cpu().numpy()
vis_res = decode_labels(results)
parsing_im = Image.fromarray(vis_res[0])
parsing_im.save(opts.output_path +
"cihp_output_vis/{}.png".format(img_list[ii][:-1]))
cv2.imwrite(
opts.output_path + "cihp_output/{}.png".format(img_list[ii][:-1]),
results[0, :, :],
)
# np.save('../../cihp_prob_output/{}.npy'.format(img_list[ii][:-1]), prob_results[0, :, :])
# pred_list.append(predictions.cpu())
# label_list.append(labels.squeeze(1).cpu())
# loss = criterion(outputs, labels, batch_average=True)
# running_loss_ts += loss.item()
# total_iou += utils.get_iou(predictions, labels)
end_time = timeit.default_timer()
print("time use for " + str(ii) + " is :" + str(end_time - start_time))
# Eval
pred_path = opts.output_path + "cihp_output/"
eval_(
pred_path=pred_path,
gt_path=opts.gt_path,
classes=opts.classes,
txt_file=opts.txt_file,
)
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()
