def test_saved_3d_resize_content(self):
with tempfile.TemporaryDirectory() as tempdir:
saver = NiftiSaver(output_dir=tempdir,
output_postfix="seg",
output_ext=".nii.gz",
dtype=np.float32)
meta_data = {
"filename_or_obj":
["testfile" + str(i) + ".nii.gz" for i in range(8)],
"spatial_shape": [(10, 10, 2)] * 8,
"affine": [np.diag(np.ones(4)) * 5] * 8,
"original_affine": [np.diag(np.ones(4)) * 1.0] * 8,
}
saver.save_batch(torch.randint(0, 255, (8, 8, 1, 2, 2)), meta_data)
for i in range(8):
filepath = os.path.join("testfile" + str(i),
"testfile" + str(i) + "_seg.nii.gz")
self.assertTrue(os.path.exists(os.path.join(tempdir,
filepath)))
def main():
images = sorted(glob(os.path.join(IMAGE_FOLDER, "case*.nii.gz")))
val_files = [{"img": img} for img in images]
# define transforms for image and segmentation
infer_transforms = Compose(
[
LoadNiftid("img"),
AddChanneld("img"),
Orientationd("img", "SPL"), # coplenet works on the plane defined by the last two axes
ToTensord("img"),
]
)
test_ds = monai.data.Dataset(data=val_files, transform=infer_transforms)
# sliding window inference need to input 1 image in every iteration
data_loader = torch.utils.data.DataLoader(
test_ds, batch_size=1, num_workers=0, pin_memory=torch.cuda.is_available()
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CopleNet().to(device)
model.load_state_dict(torch.load(MODEL_FILE)["model_state_dict"])
model.eval()
with torch.no_grad():
saver = NiftiSaver(output_dir=OUTPUT_FOLDER)
for idx, val_data in enumerate(data_loader):
print(f"Inference on {idx+1} of {len(data_loader)}")
val_images = val_data["img"].to(device)
# define sliding window size and batch size for windows inference
slice_shape = np.ceil(np.asarray(val_images.shape[3:]) / 32) * 32
roi_size = (20, int(slice_shape[0]), int(slice_shape[1]))
sw_batch_size = 2
val_outputs = sliding_window_inference(
val_images, roi_size, sw_batch_size, model, 0.0, padding_mode="circular"
)
# val_outputs = (val_outputs.sigmoid() >= 0.5).float()
val_outputs = val_outputs.argmax(dim=1, keepdim=True)
saver.save_batch(val_outputs, val_data["img_meta_dict"])
def test_saved_3d_no_resize_content(self):
with tempfile.TemporaryDirectory() as tempdir:
saver = NiftiSaver(output_dir=tempdir,
output_postfix="seg",
output_ext=".nii.gz",
dtype=np.float32,
resample=False)
meta_data = {
"filename_or_obj":
["testfile" + str(i) + ".nii.gz" for i in range(8)],
"spatial_shape": [(10, 10, 2)] * 8,
"affine": [np.diag(np.ones(4)) * 5] * 8,
"original_affine": [np.diag(np.ones(4)) * 1.0] * 8,
}
saver.save_batch(torch.randint(0, 255, (8, 8, 1, 2, 2)), meta_data)
for i in range(8):
filepath = os.path.join(tempdir, "testfile" + str(i),
"testfile" + str(i) + "_seg.nii.gz")
img, _ = LoadImage("nibabelreader")(filepath)
self.assertEqual(img.shape, (1, 2, 2, 8))
def test_saved_3d_resize_content(self):
default_dir = os.path.join(".", "tempdir")
shutil.rmtree(default_dir, ignore_errors=True)
saver = NiftiSaver(output_dir=default_dir,
output_postfix="seg",
output_ext=".nii.gz",
dtype=np.float32)
meta_data = {
"filename_or_obj": ["testfile" + str(i) for i in range(8)],
"spatial_shape": [(10, 10, 2)] * 8,
"affine": [np.diag(np.ones(4)) * 5] * 8,
"original_affine": [np.diag(np.ones(4)) * 1.0] * 8,
}
saver.save_batch(torch.randint(0, 255, (8, 8, 1, 2, 2)), meta_data)
for i in range(8):
filepath = os.path.join("testfile" + str(i),
"testfile" + str(i) + "_seg.nii.gz")
self.assertTrue(os.path.exists(os.path.join(default_dir,
filepath)))
shutil.rmtree(default_dir)
def main():
config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
tempdir = tempfile.mkdtemp()
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
# define transforms for image and segmentation
imtrans = Compose([ScaleIntensity(), AddChannel(), ToTensor()])
segtrans = Compose([AddChannel(), ToTensor()])
val_ds = NiftiDataset(images,
segs,
transform=imtrans,
seg_transform=segtrans,
image_only=False)
# sliding window inference for one image at every iteration
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=1,
pin_memory=torch.cuda.is_available())
device = torch.device("cuda:0")
model = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model.load_state_dict(torch.load("best_metric_model.pth"))
model.eval()
with torch.no_grad():
metric_sum = 0.0
metric_count = 0
saver = NiftiSaver(output_dir="./output")
for val_data in val_loader:
val_images, val_labels = val_data[0].to(device), val_data[1].to(
device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
value = compute_meandice(y_pred=val_outputs,
y=val_labels,
include_background=True,
to_onehot_y=False,
add_sigmoid=True)
metric_count += len(value)
metric_sum += value.sum().item()
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
saver.save_batch(val_outputs, val_data[2])
metric = metric_sum / metric_count
print("evaluation metric:", metric)
shutil.rmtree(tempdir)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
tempdir = tempfile.mkdtemp()
print('generating synthetic data to {} (this may take a while)'.format(
tempdir))
for i in range(5):
im, seg = create_test_image_3d(128,
128,
128,
num_seg_classes=1,
channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
val_files = [{'img': img, 'seg': seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadNiftid(keys=['img', 'seg']),
AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1),
ScaleIntensityd(keys=['img', 'seg']),
ToTensord(keys=['img', 'seg'])
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inference need to input 1 image in every iteration
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available())
device = torch.device('cuda:0')
model = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
metric_sum = 0.
metric_count = 0
saver = NiftiSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data['img'].to(
device), val_data['seg'].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
value = compute_meandice(y_pred=val_outputs,
y=val_labels,
include_background=True,
to_onehot_y=False,
add_sigmoid=True)
metric_count += len(value)
metric_sum += value.sum().item()
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
saver.save_batch(
val_outputs, {
'filename_or_obj': val_data['img.filename_or_obj'],
'affine': val_data['img.affine']
})
metric = metric_sum / metric_count
print('evaluation metric:', metric)
shutil.rmtree(tempdir)
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadNiftid(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
ToTensord(keys=["img", "seg"]),
]
)
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inference need to input 1 image in every iteration
val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate)
dice_metric = DiceMetric(include_background=True, reduction="mean")
post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])
# try to use all the available GPUs
devices = get_devices_spec(None)
model = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(devices[0])
model.load_state_dict(torch.load("best_metric_model_segmentation3d_dict.pth"))
# if we have multiple GPUs, set data parallel to execute sliding window inference
if len(devices) > 1:
model = torch.nn.DataParallel(model, device_ids=devices)
model.eval()
with torch.no_grad():
metric_sum = 0.0
metric_count = 0
saver = NiftiSaver(output_dir="./output")
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(devices[0]), val_data["seg"].to(devices[0])
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)
val_outputs = post_trans(val_outputs)
value, _ = dice_metric(y_pred=val_outputs, y=val_labels)
metric_count += len(value)
metric_sum += value.item() * len(value)
saver.save_batch(val_outputs, val_data["img_meta_dict"])
metric = metric_sum / metric_count
print("evaluation metric:", metric)
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
metric_sum = 0.
metric_count = 0
saver = NiftiSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data[0].to(device), val_data[1].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
value = compute_meandice(y_pred=val_outputs,
y=val_labels,
include_background=True,
to_onehot_y=False,
add_sigmoid=True)
metric_count += len(value)
metric_sum += value.sum().item()
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
saver.save_batch(val_outputs, val_data[2])
metric = metric_sum / metric_count
print('evaluation metric:', metric)
shutil.rmtree(tempdir)
def main():
"""
Read input and configuration parameters
"""
parser = argparse.ArgumentParser(
description='Run inference with basic UNet with MONAI.')
parser.add_argument('--config',
dest='config',
metavar='config',
type=str,
help='config file')
args = parser.parse_args()
with open(args.config) as f:
config_info = yaml.load(f, Loader=yaml.FullLoader)
# print to log the parameter setups
print(yaml.dump(config_info))
# GPU params
cuda_device = config_info['device']['cuda_device']
num_workers = config_info['device']['num_workers']
# inference params
batch_size_inference = config_info['inference']['batch_size_inference']
# temporary check as sliding window inference does not accept higher batch size
assert batch_size_inference == 1
prob_thr = config_info['inference']['probability_threshold']
model_to_load = config_info['inference']['model_to_load']
if not os.path.exists(model_to_load):
raise IOError('Trained model not found')
# data params
data_root = config_info['data']['data_root']
inference_list = config_info['data']['inference_list']
# output saving
out_dir = config_info['output']['out_dir']
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
torch.cuda.set_device(cuda_device)
"""
Data Preparation
"""
val_files = create_data_list(data_folder_list=data_root,
subject_list=inference_list,
img_postfix='_Image',
is_inference=True)
print(len(val_files))
print(val_files[0])
print(val_files[-1])
# data preprocessing for inference:
# - convert data to right format [batch, channel, dim, dim, dim]
# - apply whitening
# - NOTE: resizing needs to be applied afterwards, otherwise it cannot be remapped back to original size
val_transforms = Compose([
LoadNiftid(keys=['img']),
AddChanneld(keys=['img']),
NormalizeIntensityd(keys=['img']),
ToTensord(keys=['img'])
])
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=batch_size_inference,
num_workers=num_workers)
"""
Network preparation
"""
device = torch.cuda.current_device()
# Create UNet, DiceLoss and Adam optimizer.
net = monai.networks.nets.UNet(
dimensions=2,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
net.load_state_dict(torch.load(model_to_load))
net.eval()
"""
Run inference
"""
with torch.no_grad():
saver = NiftiSaver(output_dir=out_dir)
for val_data in val_loader:
val_images = val_data['img'].to(device)
orig_size = list(val_images.shape)
resized_size = copy.deepcopy(orig_size)
resized_size[2] = 96
resized_size[3] = 96
val_images_resize = torch.nn.functional.interpolate(
val_images, size=resized_size[2:], mode='trilinear')
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 1)
val_outputs = sliding_window_inference(val_images_resize, roi_size,
batch_size_inference, net)
val_outputs = (val_outputs.sigmoid() >= prob_thr).float()
val_outputs_resized = torch.nn.functional.interpolate(
val_outputs, size=orig_size[2:], mode='nearest')
# add post-processing
val_outputs_resized = val_outputs_resized.detach().cpu().numpy()
strt = ndimage.generate_binary_structure(3, 2)
post = padded_binary_closing(np.squeeze(val_outputs_resized), strt)
post = get_largest_component(post)
val_outputs_resized = val_outputs_resized * post
# out = np.zeros(img.shape[:-1], np.uint8)
# out = set_ND_volume_roi_with_bounding_box_range(out, bb_min, bb_max, out_roi)
saver.save_batch(
val_outputs_resized, {
'filename_or_obj': val_data['img.filename_or_obj'],
'affine': val_data['img.affine']
})
model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
metric_sum = 0.
metric_count = 0
saver = NiftiSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data['img'].to(
device), val_data['seg'].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
value = compute_meandice(y_pred=val_outputs,
y=val_labels,
include_background=True,
to_onehot_y=False,
add_sigmoid=True)
metric_count += len(value)
metric_sum += value.sum().item()
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
saver.save_batch(
val_outputs, {
'filename_or_obj': val_data['img.filename_or_obj'],
'affine': val_data['img.affine']
})
metric = metric_sum / metric_count
print('evaluation metric:', metric)
shutil.rmtree(tempdir)
logits = forward(model, inputs)
labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)
batch_mri = inputs
batch_label = labels
#slices = torch.cat((batch_mri, batch_label))
#slices = torch.cat((batch_mri, batch_label),dim=1)
#inf_path = 'inference.png'
#save_image(slices, inf_path, nrow=training_batch_size//2, normalize=True, scale_each=True, padding=0)
#display.Image(inf_path)
#saver = NiftiSaver(output_dir="./niftinferece",output_postfix = str(i))
#saver.save_batch(slices)
saver1 = NiftiSaver(output_dir="./inputsnifti", output_postfix=str(i))
saver2 = NiftiSaver(output_dir="./labelsnifti", output_postfix=str(i))
saver1.save_batch(inputs)
saver2.save_batch(labels)
#Dice score for inference slide
dice_score.append(
get_dice_score(F.softmax(logits, dim=CHANNELS_DIMENSION), targets))
#Dice loss for inference slide
dice_losses.append(
get_dice_loss(F.softmax(logits, dim=CHANNELS_DIMENSION), targets))
## experimental to output all dice scores -- WORKS!
dataset_iter = iter(validation_loader2)
for i in range(5):
try:
inputs, targets = prepare_batch(
next(dataset_iter), 16,
def run_inference_test(root_dir, device="cuda:0"):
images = sorted(glob(os.path.join(root_dir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
EnsureChannelFirstd(keys=["img", "seg"]),
# resampling with align_corners=True or dtype=float64 will generate
# slight different results between PyTorch 1.5 an 1.6
Spacingd(keys=["img", "seg"],
pixdim=[1.2, 0.8, 0.7],
mode=["bilinear", "nearest"],
dtype=np.float32),
ScaleIntensityd(keys="img"),
ToTensord(keys=["img", "seg"]),
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inference need to input 1 image in every iteration
val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
val_post_tran = Compose([
ToTensor(),
Activations(sigmoid=True),
AsDiscrete(threshold_values=True)
])
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
model = UNet(
spatial_dims=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model_filename = os.path.join(root_dir, "best_metric_model.pth")
model.load_state_dict(torch.load(model_filename))
with eval_mode(model):
# resampling with align_corners=True or dtype=float64 will generate
# slight different results between PyTorch 1.5 an 1.6
saver = NiftiSaver(output_dir=os.path.join(root_dir, "output"),
dtype=np.float32)
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["seg"].to(device)
# define sliding window size and batch size for windows inference
sw_batch_size, roi_size = 4, (96, 96, 96)
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
# decollate prediction into a list and execute post processing for every item
val_outputs = [
val_post_tran(i) for i in decollate_batch(val_outputs)
]
# compute metrics
dice_metric(y_pred=val_outputs, y=val_labels)
saver.save_batch(val_outputs, val_data["img_meta_dict"])
return dice_metric.aggregate().item()