def __init__(
self,
root_dir: str,
section: str,
transform: Callable[..., Any] = LoadPNGd("image"),
download: bool = False,
seed: int = 0,
val_frac: float = 0.1,
test_frac: float = 0.1,
cache_num: int = sys.maxsize,
cache_rate: float = 1.0,
num_workers: int = 0,
):
if not os.path.isdir(root_dir):
raise ValueError("root_dir must be a directory.")
self.section = section
self.val_frac = val_frac
self.test_frac = test_frac
self.set_random_state(seed=seed)
tarfile_name = os.path.join(root_dir, self.compressed_file_name)
dataset_dir = os.path.join(root_dir, self.dataset_folder_name)
if download:
download_and_extract(self.resource, tarfile_name, root_dir,
self.md5)
if not os.path.exists(dataset_dir):
raise RuntimeError(
f"can not find dataset directory: {dataset_dir}, please use download=True to download it."
)
data = self._generate_data_list(dataset_dir)
super().__init__(data,
transform,
cache_num=cache_num,
cache_rate=cache_rate,
num_workers=num_workers)
def test_shape(self, input_param, expected_shape):
test_image = np.random.randint(0, 256, size=[128, 128, 3])
with tempfile.TemporaryDirectory() as tempdir:
test_data = dict()
for key in KEYS:
Image.fromarray(test_image.astype("uint8")).save(
os.path.join(tempdir, key + ".png"))
test_data.update({key: os.path.join(tempdir, key + ".png")})
result = LoadPNGd(**input_param)(test_data)
for key in KEYS:
self.assertTupleEqual(result[key].shape, expected_shape)
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
transform = Compose([
LoadPNGd(keys="image"),
AddChanneld(keys="image"),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(len(dataset), 5986)
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue("image_meta_dict" in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64))
try: # will start downloading if testing_dir doesn't have the MedNIST files
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=True)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
print(str(e))
if isinstance(e, RuntimeError):
# FIXME: skip MD5 check as current downloading method may fail
self.assertTrue(str(e).startswith("md5 check"))
return # skipping this test due the network connection errors
_test_dataset(data)
# testing from
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False)
_test_dataset(data)
data = MedNISTDataset(root_dir=testing_dir,
section="test",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (64, 64))
shutil.rmtree(os.path.join(testing_dir, "MedNIST"))
try:
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False)
except RuntimeError as e:
print(str(e))
self.assertTrue(str(e).startswith("Cannot find dataset directory"))
def test_values(self):
tempdir = tempfile.mkdtemp()
transform = Compose([
LoadPNGd(keys="image"),
AddChanneld(keys="image"),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(len(dataset), 5986)
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue("image_meta_dict" in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64))
data = MedNISTDataset(root_dir=tempdir,
transform=transform,
section="test",
download=True)
_test_dataset(data)
data = MedNISTDataset(root_dir=tempdir,
transform=transform,
section="test",
download=False)
_test_dataset(data)
data = MedNISTDataset(root_dir=tempdir, section="test", download=False)
self.assertTupleEqual(data[0]["image"].shape, (64, 64))
shutil.rmtree(os.path.join(tempdir, "MedNIST"))
try:
data = MedNISTDataset(root_dir=tempdir,
transform=transform,
section="test",
download=False)
except RuntimeError as e:
print(str(e))
self.assertTrue(
str(e).startswith("can not find dataset directory"))
shutil.rmtree(tempdir)
def run(file, inputMount, outputMount):
inputPath = os.path.join(inputMount, file.split('/')[-1])
outputPath = os.path.join(outputMount, file.split('/')[-1])
print("attempting to infer cell clusters")
try:
# open the image only to find its size
with Image.open(inputPath) as input_img:
input_bbox = input_img.getbbox()
input_width = input_bbox[2] - input_bbox[0]
input_height = input_bbox[3] - input_bbox[1]
#print('input image size:',input_width,input_height)
# instantiate the model
# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer
#print('checking for cuda device')
device = torch.device('cuda:0')
model = monai.networks.nets.UNet(dimensions=2,
in_channels=3,
out_channels=2,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
norm=Norm.BATCH).to(device)
print('attempting load of pretrained network from /tmp directory')
# read in the pretrained model
model.load_state_dict(
torch.load('/tmp/unet_368x368_segment_model.pth'))
#print('loading complete')
model.eval()
NETWORK_IMAGE_SIZE = 368
# define xforms in MONAI to prepare imagery for PyTorch inferencing
infer_transforms = Compose([
LoadPNGd(keys=['image']),
AsChannelFirstd(keys=['image']),
Resized(keys=['image'],
spatial_size=(NETWORK_IMAGE_SIZE, NETWORK_IMAGE_SIZE),
mode='bilinear',
align_corners=False),
CastToTyped(keys=['image'], dtype='float32'),
ScaleIntensityd(keys=['image'], minv=0.0, maxv=1.0),
ToTensord(keys=['image'])
])
# create and load a Monai dataset composed of the single image, because the transforms
# are performed automatically in MONAI. A spec for the image is needed by the Dataset definition
infer_files = [{'image': inputPath}]
infer_ds = monai.data.Dataset(infer_files, transform=infer_transforms)
infer_loader = monai.data.DataLoader(infer_ds, batch_size=1)
# get the transformed single image out of the dataset
input_data = monai.utils.misc.first(infer_loader)
# move the tensor to the GPU
input_tensor = input_data['image'].to(device)
# run the forward prediction
predict_tensor = model(input_tensor)
#print('inference complete. preparing output')
# get the result back from the GPU and drop the first dimension
infer_array = predict_tensor.detach().cpu().squeeze()
# rearrange to num channels last and make it a single channel binary image
pred_array = torch.argmax(np.transpose(infer_array, (1, 2, 0)), dim=2)
# convert type back from torch to numpy
prediction = pred_array.numpy()
# write the file out in a viewable way, the output image is resized to match the
# input image size for convenience, even though inferencing is always done at the
# size of the pretrained network
outimg = Image.fromarray(prediction.astype('uint8') * 255)
resized = outimg.resize((input_width, input_height))
print('saving segmentation to:', outputPath)
resized.save(outputPath)
except OSError:
print("cannot create inference image for", file)