def test_admm_size():
for name in list(flags.FLAGS):
delattr(flags.FLAGS, name)
root_dir = '../dataset/ACDC-2D-All'
train_dataset = MedicalImageDataset(root_dir, 'train', transform=segment_transform((200, 200)), augment=augment)
val_dataset = MedicalImageDataset(root_dir, 'val', transform=segment_transform((200, 200)), augment=None)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)
AdmmSize.setup_arch_flags()
hparams = flags.FLAGS.flag_values_dict()
arch_hparam = extract_from_big_dict(hparams, AdmmSize.arch_hparam_keys)
torchnet = get_arch('enet', arch_hparam)
weight = torch.Tensor([0.1, 1])
criterion = get_loss_fn('cross_entropy', weight=weight)
test_admm = AdmmSize(torchnet, hparams)
val_score = test_admm.evaluate(val_loader)
print(val_score)
for i, (img, gt, wgt, _) in enumerate(train_loader):
if gt.sum() == 0 or wgt.sum() == 0:
continue
test_admm.reset(img)
for j in range(3):
test_admm.update((img, gt, wgt), criterion)
if i >= 3:
break
def test_admm_gc_size():
for name in list(flags.FLAGS):
delattr(flags.FLAGS, name)
root_dir = '../dataset/ACDC-2D-All'
train_dataset = MedicalImageDataset(root_dir, 'train', transform=segment_transform((128, 128)), augment=augment)
val_dataset = MedicalImageDataset(root_dir, 'val', transform=segment_transform((128, 128)), augment=None)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)
AdmmGCSize.setup_arch_flags()
hparams = flags.FLAGS.flag_values_dict()
torchnet = get_arch('enet', {'num_classes': 2})
# torchnet.load_state_dict(torch.load('/Users/jizong/workspace/DGA1033/checkpoints/weakly/enet_fdice_0.8906.pth',
# map_location=lambda storage, loc: storage))
weight = torch.Tensor([0, 1])
criterion = get_loss_fn('cross_entropy', weight=weight)
test_admm = AdmmGCSize(torchnet, hparams)
val_score = test_admm.evaluate(val_loader)
print(val_score)
for i, (img, gt, wgt, _) in enumerate(train_loader):
if gt.sum() == 0 or wgt.sum() == 0:
continue
test_admm.reset(img)
for j in range(2):
test_admm.update((img, gt, wgt), criterion)
if i >= 4:
break
def test_prostate_dataloader():
root_dir = '../dataset/PROSTATE'
train_dataset = MedicalImageDataset(root_dir,
'train',
transform=segment_transform(
(128, 128)),
augment=augment)
val_dataset = MedicalImageDataset(root_dir,
'val',
transform=segment_transform((128, 128)),
augment=None)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)
# for i, (Img, GT, wgt, _) in enumerate(train_loader):
# ToPILImage()(Img[0]).show()
# if i == 5:
# train_loader.dataset.set_mode('eval')
# ToPILImage()(Img[0]).show()
# if i == 10:
# break
#
# for i, (img, gt, wgt, _) in enumerate(val_loader):
# ToPILImage()(img[0]).show()
# if i == 5:
# val_loader.dataset.set_mode('eval')
# ToPILImage()(img[0]).show()
# if i == 10:
# break
assert train_dataset.__len__() == train_dataset.imgs.__len__()
def test_dataloader():
root_dir = '../dataset/ACDC-2D-All'
train_dataset = MedicalImageDataset(root_dir,
'train',
transform=segment_transform(
(500, 500)),
augment=augment)
val_dataset = MedicalImageDataset(root_dir,
'val',
transform=segment_transform((500, 500)),
augment=None)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)
for i, (Img, GT, wgt, _) in enumerate(train_loader):
if GT.sum() <= 0 or wgt.sum() <= 0:
continue
show_img_mask_weakmask(Img.numpy(), GT.numpy(), wgt.numpy())
# # ToPILImage()(Img[0]).show()
# if i == 5:
# train_loader.dataset.set_mode('eval')
# ToPILImage()(Img[0]).show()
# if i == 10:
# break
# for i, (img, gt, wgt, _) in enumerate(val_loader):
# ToPILImage()(img[0]).show()
# if i == 5:
# val_loader.dataset.set_mode('eval')
# ToPILImage()(img[0]).show()
# if i == 10:
# break
assert train_dataset.__len__() == train_dataset.imgs.__len__()
def test_visualization():
tensorbord_dir = 'runs_test'
root_dir = '../dataset/ACDC-2D-All'
train_dataset = MedicalImageDataset(root_dir, 'train', transform=segment_transform((200, 200)), augment=augment)
val_dataset = MedicalImageDataset(root_dir, 'val', transform=segment_transform((200, 200)), augment=None)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)
torchnet = get_arch('enet', {'num_classes': 2})
writer = Writter_tf(tensorbord_dir, torchnet, 40)
for i in range(2):
writer.add_images(train_loader, i)
## clean up
shutil.rmtree(tensorbord_dir)
def test_iter():
dataroot: str = '../dataset/ACDC-all'
train_set = MedicalImageDataset(
dataroot,
'train',
subfolders=['img', 'gt'],
transform=segment_transform((256, 256)),
augment=PILaugment,
equalize=None,
pin_memory=False,
metainfo="[classSizeCalulator, {'C':4,'foldernames':['gt','gt2']}]")
train_loader_1 = DataLoader(train_set, batch_size=10, num_workers=8)
train_loader_2 = DataLoader(train_set, batch_size=10, num_workers=8)
n_batches_1 = len(train_loader_1)
n_batches_2 = len(train_loader_2)
assert n_batches_1 == n_batches_2
train_loader_1_ = iterator_(train_loader_1)
train_loader_2_ = iterator_(train_loader_2)
output_list1 = []
output_list2 = []
for i in range(n_batches_2 + 1):
data1 = train_loader_1_.__next__()
data2 = train_loader_2_.__next__()
output_list1.extend(data1[2])
output_list2.extend(data2[2])
assert set(output_list1) == set(
[Path(x).stem for x in train_loader_1.dataset.filenames['img']])
assert set(output_list2) == set(
[Path(x).stem for x in train_loader_2.dataset.filenames['img']])
def test_dataset():
dataroot: str = '../dataset/ACDC-all'
train_set = MedicalImageDataset(
dataroot,
'train',
subfolders=['img', 'gt'],
transform=segment_transform((256, 256)),
augment=PILaugment,
equalize=None,
pin_memory=False,
metainfo="[classSizeCalulator, {'C':4,'foldernames':['gt','gt2']}]")
train_loader = DataLoader(train_set, batch_size=10, num_workers=8)
n_batches = len(train_loader)
for i, (imgs, metainfo, filenames) in enumerate(train_loader):
print(imgs)
print(metainfo)
print(filenames)
time.sleep(2)