def train():
# args = brats2019_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
(
training_generator,
val_generator,
full_volume,
affine,
) = medical_loaders.generate_datasets(args)
model, optimizer = medzoo.create_model(args)
val_criterion = DiceLoss(classes=11, skip_index_after=args.classes)
# criterion = DiceLoss(classes=3, skip_index_after=args.classes)
# criterion = DiceLoss(classes=args.classes)
criterion = torch.nn.CrossEntropyLoss()
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = train_module.Trainer(
args,
model,
criterion,
optimizer,
val_criterion=val_criterion,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None,
)
print("START TRAINING...")
trainer.training()
def main():
args = get_arguments()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
name_model = args.model + "_" + args.dataset_name + "_" + utils.datestr()
# TODO visual3D_temp.Basewriter package
writer = SummaryWriter(log_dir='./runs/' + name_model, comment=name_model)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
print("START TRAINING...")
for epoch in range(1, args.nEpochs + 1):
train(args, model, training_generator, optimizer, epoch, writer)
val_metrics, confusion_matrix = validation(args, model, val_generator,
epoch, writer)
def main():
args = get_arguments()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
utils.save_arguments(args, args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = create_loss('CrossEntropyLoss')
criterion = DiceLoss(classes=args.classes,
weight=torch.tensor([0.1, 1, 1, 1]).cuda())
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None)
print("START TRAINING...")
trainer.training()
def main():
args = get_arguments()
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
## FOR REPRODUCIBILITY OF RESULTS
seed = 1777777
utils.reproducibility(args, seed)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='./datasets')
model, optimizer = medzoo.create_model(args)
#
criterion = DiceLoss(classes=args.classes)
#
# ## TODO LOAD PRETRAINED MODEL
print(affine.shape)
model.restore_checkpoint(args.pretrained)
if args.cuda:
model = model.cuda()
full_volume = full_volume.cuda()
print("Model transferred in GPU.....")
x = torch.randn(3, 156, 240, 240).cuda()
print(full_volume.shape)
output = non_overlap_padding(args,
full_volume,
model,
criterion,
kernel_dim=(32, 32, 32))
def main():
args = get_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = train.Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator,
lr_scheduler=None)
print("START TRAINING...")
trainer.training()
visualize_3D_no_overlap_new(args, full_volume, affine, model, 10, args.dim)
def main():
args = get_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(args,
path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
model = model.cuda()
trainer = train.Trainer(args, model, criterion, optimizer, train_data_loader=training_generator,
valid_data_loader=val_generator)
trainer.training()
def main():
args = get_arguments()
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(
classes=args.classes
) # ,skip_index_after=2,weight=torch.tensor([0.00001,1,1,1]).cuda())
if args.cuda:
model = model.cuda()
print("Model transferred in GPU.....")
trainer = train.Trainer(args,
model,
criterion,
optimizer,
train_data_loader=training_generator,
valid_data_loader=val_generator)
print("START TRAINING...")
trainer.training()
def test():
# args = mrbrains9_arguments(loadData=True)
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
params = {"batch_size": args.batchSz, "shuffle": True, "num_workers": 2}
samples_train = args.samples_train
samples_val = args.samples_val
test_loader = MRIDatasetMRBRAINS2018(
args,
"test",
dataset_path=dataset_dir,
dim=args.dim,
split_id=0,
samples=samples_train,
load=args.loadData,
)
model_name = args.model
lr = args.lr
in_channels = args.inChannels
num_classes = args.classes
weight_decay = 0.0000000001
print("Building Model . . . . . . . ." + model_name)
model = UNet3D(in_channels=in_channels, n_classes=num_classes, base_n_filter=8)
print(
model_name,
"Number of params: {}".format(
sum([p.data.nelement() for p in model.parameters()])
),
)
model.restore_checkpoint(
"/home/kyle/results/UNET3D/mrbrains9_148_09-08_17-46/mrbrains9_148_09-08_17-46_BEST.pth"
)
# model = model.cuda()
# print("Model transferred in GPU.....")
print("TESTING...")
# [[37507023, 290552, 0, 25074, 30, 1323040, 134, 20823, 10884, 0],
# [256417, 16475613, 1592518, 1920243, 2259, 2491037, 67078, 61665, 497, 0],
# [60651, 3655997, 594069, 16095428, 2494541, 102916, 456685, 4495, 4150, 0],
# [1225472, 1183528, 24771, 74524, 614, 13215040, 2649646, 104672, 72727, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [91439, 131011, 492, 8209, 0, 784077, 1, 6204313, 249132, 0],
# [9185, 3047, 0, 13415, 0, 62690, 0, 12931, 1513371, 0],
# [1471, 46677, 0, 5422, 10476, 32999, 1025, 12, 0, 0],
model.eval()
confusion_matrix = [[0] * (num_classes * 2) for i in range(num_classes * 2)]
for batch_idx, input_tuple in enumerate(test_loader):
with torch.no_grad():
img_t1, img_t2, img_t3, target = input_tuple
target = torch.reshape(torch.from_numpy(target), (-1, 1, 48, 48, 48))
img_t1 = torch.reshape(torch.from_numpy(img_t1), (-1, 1, 48, 48, 48))
img_t2 = torch.reshape(torch.from_numpy(img_t2), (-1, 1, 48, 48, 48))
img_t3 = torch.reshape(torch.from_numpy(img_t3), (-1, 1, 48, 48, 48))
input_tensor = torch.cat((img_t1, img_t2, img_t3), dim=1)
input_tensor.requires_grad = False
output = model(input_tensor)
output = torch.argmax(output, dim=1)
output = torch.reshape(output, (-1, 1, 48, 48, 48))
assert target.size() == output.size()
output = torch.reshape(output, (-1,)).tolist()
target = torch.reshape(target, (-1,)).tolist()
assert len(output) == len(target)
for gt, pred in zip(target, output):
confusion_matrix[int(gt)][int(pred)] += 1
pprint(confusion_matrix)
def test():
# args = iseg2019_arguments()
print(args)
utils.reproducibility(args, seed)
utils.make_dirs(args.save)
params = {"batch_size": args.batchSz, "shuffle": True, "num_workers": 2}
print(params)
samples_train = args.samples_train
samples_val = args.samples_val
test_loader = MRIDatasetISEG2019(
args,
"test",
dataset_path=dataset_dir,
crop_dim=args.dim,
split_id=0,
samples=samples_train,
load=args.loadData,
)
model_name = args.model
lr = args.lr
in_channels = args.inChannels
num_classes = args.classes
weight_decay = 0.0000000001
print("Building Model . . . . . . . ." + model_name)
model = UNet3D(in_channels=in_channels, n_classes=num_classes, base_n_filter=8)
print(
model_name,
"Number of params: {}".format(
sum([p.data.nelement() for p in model.parameters()])
),
)
model.restore_checkpoint(
"/home/kyle/results/UNET3D/iseg2019_9_06-08_21-25/iseg2019_9_06-08_21-25_BEST.pth"
)
criterion = DiceLoss(classes=args.classes)
# model = model.cuda()
# print("Model transferred in GPU.....")
print("TESTING...")
model.eval()
confusion_matrix = [[0] * 4 for i in range(4)]
for batch_idx, input_tuple in enumerate(test_loader):
with torch.no_grad():
img_t1, img_t2, target = input_tuple
target = torch.reshape(target, (-1, 1, 64, 64, 64))
img_t1 = torch.reshape(img_t1, (-1, 1, 64, 64, 64))
img_t2 = torch.reshape(img_t2, (-1, 1, 64, 64, 64))
input_tensor = torch.cat((img_t1, img_t2), dim=1)
# print(input_tensor.size())
input_tensor.requires_grad = False
output = model(input_tensor)
output = torch.argmax(output, dim=1)
output = torch.reshape(output, (-1, 1, 64, 64, 64))
assert target.size() == output.size()
output = torch.reshape(output, (-1,)).tolist()
target = torch.reshape(target, (-1,)).tolist()
assert len(output) == len(target)
for gt, pred in zip(target, output):
confusion_matrix[int(gt)][int(pred)] += 1
pprint(confusion_matrix)
