def test_nvtx_transfroms_array(self, input): # with prob == 0.0 transforms = Compose([ RandMark("Mark: Transforms Start!"), RandRangePush("Range: RandFlip"), RandFlip(prob=0.0), RandRangePop(), RangePush("Range: ToTensor"), ToTensor(), RangePop(), Mark("Mark: Transforms End!"), ]) output = transforms(input) self.assertIsInstance(output, torch.Tensor) np.testing.assert_array_equal(input, output) # with prob == 1.0 transforms = Compose([ RandMark("Mark: Transforms Start!"), RandRangePush("Range: RandFlip"), RandFlip(prob=1.0), RandRangePop(), RangePush("Range: ToTensor"), ToTensor(), RangePop(), Mark("Mark: Transforms End!"), ]) output = transforms(input) self.assertIsInstance(output, torch.Tensor) np.testing.assert_array_equal(input, Flip()(output.numpy()))
def test_correct_results(self, _, spatial_axis): flip = RandFlip(prob=1.0, spatial_axis=spatial_axis) expected = list() for channel in self.imt[0]: expected.append(np.flip(channel, spatial_axis)) expected = np.stack(expected) self.assertTrue(np.allclose(expected, flip(self.imt[0])))
def test_correct_results(self, _, spatial_axis): for p in TEST_NDARRAYS: im = p(self.imt[0]) flip = RandFlip(prob=1.0, spatial_axis=spatial_axis) expected = [ np.flip(channel, spatial_axis) for channel in self.imt[0] ] expected = np.stack(expected) result = flip(im) assert_allclose(result, p(expected))
def test_correct_results(self, _, spatial_axis): for p in TEST_NDARRAYS_ALL: im = p(self.imt[0]) flip = RandFlip(prob=1.0, spatial_axis=spatial_axis) set_track_meta(False) result = flip(im) self.assertNotIsInstance(result, MetaTensor) self.assertIsInstance(result, torch.Tensor) set_track_meta(True) expected = [ np.flip(channel, spatial_axis) for channel in self.imt[0] ] expected = np.stack(expected) result = flip(im) assert_allclose(result, p(expected), type_test="tensor") test_local_inversion(flip, result, im)
def test_invert(self): set_determinism(seed=0) im_fname = make_nifti_image(create_test_image_3d(101, 100, 107, noise_max=100)[1]) # label image, discrete data = [im_fname for _ in range(12)] transform = Compose( [ LoadImage(image_only=True), EnsureChannelFirst(), Orientation("RPS"), Spacing(pixdim=(1.2, 1.01, 0.9), mode="bilinear", dtype=np.float32), RandFlip(prob=0.5, spatial_axis=[1, 2]), RandAxisFlip(prob=0.5), RandRotate90(prob=0, spatial_axes=(1, 2)), RandZoom(prob=0.5, min_zoom=0.5, max_zoom=1.1, keep_size=True), RandRotate(prob=0.5, range_x=np.pi, mode="bilinear", align_corners=True, dtype=np.float64), RandAffine(prob=0.5, rotate_range=np.pi, mode="nearest"), ResizeWithPadOrCrop(100), CastToType(dtype=torch.uint8), ] ) # num workers = 0 for mac or gpu transforms num_workers = 0 if sys.platform != "linux" or torch.cuda.is_available() else 2 dataset = Dataset(data, transform=transform) self.assertIsInstance(transform.inverse(dataset[0]), MetaTensor) loader = DataLoader(dataset, num_workers=num_workers, batch_size=1) inverter = Invert(transform=transform, nearest_interp=True, device="cpu") for d in loader: d = decollate_batch(d) for item in d: orig = deepcopy(item) i = inverter(item) self.assertTupleEqual(orig.shape[1:], (100, 100, 100)) # check the nearest interpolation mode torch.testing.assert_allclose(i.to(torch.uint8).to(torch.float), i.to(torch.float)) self.assertTupleEqual(i.shape[1:], (100, 101, 107)) # check labels match reverted = i.detach().cpu().numpy().astype(np.int32) original = LoadImage(image_only=True)(data[-1]) n_good = np.sum(np.isclose(reverted, original.numpy(), atol=1e-3)) reverted_name = i.meta["filename_or_obj"] original_name = original.meta["filename_or_obj"] self.assertEqual(reverted_name, original_name) print("invert diff", reverted.size - n_good) self.assertTrue((reverted.size - n_good) < 300000, f"diff. {reverted.size - n_good}") set_determinism(seed=None)
def test_tranform_randomized(self, input): # Compose deterministic and randomized transforms transforms = Compose([ Range("flip")(Flip()), Rotate90(), Range()(RandAdjustContrast(prob=0.0)), Range("random flip")(RandFlip(prob=1.0)), ToTensor(), ]) # Apply transforms output = transforms(input) # Decorate with NVTX Range transforms1 = Range()(transforms) transforms2 = Range("Transforms2")(transforms) transforms3 = Range(name="Transforms3", methods="__call__")(transforms) # Apply transforms with Range output1 = transforms1(input) output2 = transforms2(input) output3 = transforms3(input) # Check if the outputs are equal self.assertIsInstance(output, torch.Tensor) self.assertIsInstance(output1, torch.Tensor) self.assertIsInstance(output2, torch.Tensor) self.assertIsInstance(output3, torch.Tensor) np.testing.assert_equal(output.numpy(), output1.numpy()) np.testing.assert_equal(output.numpy(), output2.numpy()) np.testing.assert_equal(output.numpy(), output3.numpy()) # Check if the first randomized is RandAdjustContrast for tran in transforms.transforms: if isinstance(tran, Randomizable): self.assertIsInstance(tran, RandAdjustContrast) break
def run_training_test(root_dir, train_x, train_y, val_x, val_y, device="cuda:0", num_workers=10): monai.config.print_config() # define transforms for image and classification train_transforms = Compose([ LoadPNG(image_only=True), AddChannel(), ScaleIntensity(), RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True), RandFlip(spatial_axis=0, prob=0.5), RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5), ToTensor(), ]) train_transforms.set_random_state(1234) val_transforms = Compose( [LoadPNG(image_only=True), AddChannel(), ScaleIntensity(), ToTensor()]) # create train, val data loaders train_ds = MedNISTDataset(train_x, train_y, train_transforms) train_loader = DataLoader(train_ds, batch_size=300, shuffle=True, num_workers=num_workers) val_ds = MedNISTDataset(val_x, val_y, val_transforms) val_loader = DataLoader(val_ds, batch_size=300, num_workers=num_workers) model = densenet121(spatial_dims=2, in_channels=1, out_channels=len(np.unique(train_y))).to(device) loss_function = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), 1e-5) epoch_num = 4 val_interval = 1 # start training validation best_metric = -1 best_metric_epoch = -1 epoch_loss_values = list() metric_values = list() model_filename = os.path.join(root_dir, "best_metric_model.pth") for epoch in range(epoch_num): print("-" * 10) print(f"Epoch {epoch + 1}/{epoch_num}") model.train() epoch_loss = 0 step = 0 for batch_data in train_loader: step += 1 inputs, labels = batch_data[0].to(device), batch_data[1].to(device) optimizer.zero_grad() outputs = model(inputs) loss = loss_function(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.item() epoch_loss /= step epoch_loss_values.append(epoch_loss) print(f"epoch {epoch + 1} average loss:{epoch_loss:0.4f}") if (epoch + 1) % val_interval == 0: model.eval() with torch.no_grad(): y_pred = torch.tensor([], dtype=torch.float32, device=device) y = torch.tensor([], dtype=torch.long, device=device) for val_data in val_loader: val_images, val_labels = val_data[0].to( device), val_data[1].to(device) y_pred = torch.cat([y_pred, model(val_images)], dim=0) y = torch.cat([y, val_labels], dim=0) auc_metric = compute_roc_auc(y_pred, y, to_onehot_y=True, softmax=True) metric_values.append(auc_metric) acc_value = torch.eq(y_pred.argmax(dim=1), y) acc_metric = acc_value.sum().item() / len(acc_value) if auc_metric > best_metric: best_metric = auc_metric best_metric_epoch = epoch + 1 torch.save(model.state_dict(), model_filename) print("saved new best metric model") print( f"current epoch {epoch +1} current AUC: {auc_metric:0.4f} " f"current accuracy: {acc_metric:0.4f} best AUC: {best_metric:0.4f} at epoch {best_metric_epoch}" ) print( f"train completed, best_metric: {best_metric:0.4f} at epoch: {best_metric_epoch}" ) return epoch_loss_values, best_metric, best_metric_epoch
from monai.transforms.spatial.dictionary import RandAffined, RandRotate90d from monai.utils import optional_import, set_determinism from monai.utils.enums import InverseKeys from tests.utils import make_nifti_image _, has_nib = optional_import("nibabel") KEYS = ["image"] TESTS_DICT: List[Tuple] = [] TESTS_DICT.append((SpatialPadd(KEYS, 150), RandFlipd(KEYS, prob=1.0, spatial_axis=1))) TESTS_DICT.append((RandRotate90d(KEYS, prob=0.0, max_k=1),)) TESTS_DICT.append((RandAffined(KEYS, prob=0.0, translate_range=10),)) TESTS_LIST: List[Tuple] = [] TESTS_LIST.append((SpatialPad(150), RandFlip(prob=1.0, spatial_axis=1))) TESTS_LIST.append((RandRotate90(prob=0.0, max_k=1),)) TESTS_LIST.append((RandAffine(prob=0.0, translate_range=10),)) TEST_BASIC = [ [("channel", "channel"), ["channel", "channel"]], [torch.Tensor([1, 2, 3]), [torch.tensor(1.0), torch.tensor(2.0), torch.tensor(3.0)]], [ [[torch.Tensor((1.0, 2.0, 3.0)), torch.Tensor((2.0, 3.0, 1.0))]], [ [[torch.tensor(1.0), torch.tensor(2.0)]], [[torch.tensor(2.0), torch.tensor(3.0)]], [[torch.tensor(3.0), torch.tensor(1.0)]], ], ],
def test_invalid_inputs(self, _, spatial_axis, raises): with self.assertRaises(raises): flip = RandFlip(prob=1.0, spatial_axis=spatial_axis) flip(self.imt[0])
train_indices = indices[val_split:] train_x = [image_files_list[i] for i in train_indices] train_y = [image_class[i] for i in train_indices] val_x = [image_files_list[i] for i in val_indices] val_y = [image_class[i] for i in val_indices] test_x = [image_files_list[i] for i in test_indices] test_y = [image_class[i] for i in test_indices] # MONAI transforms, Dataset and Dataloader for preprocessing train_transforms = Compose([ LoadImage(image_only=True), AddChannel(), ScaleIntensity(), RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True), RandFlip(spatial_axis=0, prob=0.5), RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5), ToTensor(), ]) val_transforms = Compose([ LoadImage(image_only=True), AddChannel(), ScaleIntensity(), ToTensor() ]) act = Activations(softmax=True) to_onehot = AsDiscrete(to_onehot=True, n_classes=num_class) class MedNISTDataset(torch.utils.data.Dataset):
TEST_CASE_DICT_0 = [{"image": np.random.randn(3, 3)}] TEST_CASE_DICT_1 = [{"image": np.random.randn(3, 10, 10)}] TEST_CASE_TORCH_0 = [torch.randn(3, 3)] TEST_CASE_TORCH_1 = [torch.randn(3, 10, 10)] TEST_CASE_WRAPPER = [np.random.randn(3, 10, 10)] TEST_CASE_RECURSIVE_0 = [ torch.randn(3, 3), Compose([ ToNumpy(), Flip(), RandAdjustContrast(prob=0.0), RandFlip(prob=1.0), ToTensor() ]), ] TEST_CASE_RECURSIVE_1 = [ torch.randn(3, 3), Compose([ ToNumpy(), Flip(), Compose([RandAdjustContrast(prob=0.0), RandFlip(prob=1.0)]), ToTensor() ]), ] TEST_CASE_RECURSIVE_2 = [ torch.randn(3, 3),
class Loader(): """Loader for different image datasets with built in split function and download if needed. Functions: load_IXIT1: Loads the IXIT1 3D brain MRI dataset. load_MedNIST: Loads the MedNIST 2D image dataset. """ ixi_train_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), RandRotate90()]) ixi_test_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96))]) mednist_train_transforms = Compose([LoadImage(image_only=True), AddChannel(), ScaleIntensity(), RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True), RandFlip(spatial_axis=0, prob=0.5), RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5)]) mednist_test_transforms = Compose([LoadImage(image_only=True), AddChannel(), ScaleIntensity()]) @staticmethod def load_IXIT1(download: bool = False, train_transforms: object = ixi_train_transforms, test_transforms: object = ixi_test_transforms, test_size: float = 0.2, val_size: float = 0.0, sample_size: float = 0.01, shuffle: bool = True): """Loads the IXIT1 3D Brain MRI dataset. Consists of ~566 images of 3D Brain MRI scans and labels (0) for male and (1) for female. Args: download (bool): If true, then data is downloaded before loading it as dataset. train_transforms (Compose): Specify the transformations to be applied to the training dataset. test_transforms (Compose): Specify the transformations to be applied to the test dataset. sample_size (float): Percentage of available images to be used. test_size (float): Precantage of sample to be used as test data. val_size (float): Percentage of sample to be used as validation data. shuffle (bool): Whether or not the data should be shuffled after loading. """ # Download data if needed if download: data_url = 'http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar' compressed_file = os.sep.join(['Data', 'IXI-T1.tar']) data_dir = os.sep.join(['Data', 'IXI-T1']) # Data download monai.apps.download_and_extract(data_url, compressed_file, './Data/IXI-T1') # Labels document download labels_url = 'http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI.xls' monai.apps.download_url(labels_url, './Data/IXI.xls') # Get all the images and corresponding Labels images = [impath for impath in os.listdir('./Data/IXI-T1')] df = pd.read_excel('./Data/IXI.xls') data = [] labels = [] for i in images: ixi_id = int(i[3:6]) row = df.loc[df['IXI_ID'] == ixi_id] if not row.empty: data.append(os.sep.join(['Data', 'IXI-T1', i])) labels.append(int(row.iat[0, 1] - 1)) # Sex labels are 1/2 but need to be 0/1 data, labels = data[:int(len(data) * sample_size)], labels[:int(len(data) * sample_size)] # Make train test validation split train_data, train_labels, test_data, test_labels, val_data, val_labels = _split(data, labels, test_size, val_size) # Construct and return Datasets train_ds = IXIT1Dataset(train_data, train_labels, train_transforms, shuffle) test_ds = IXIT1Dataset(test_data, test_labels, test_transforms, shuffle) if val_size == 0: return train_ds, test_ds else: val_ds = IXIT1Dataset(val_data, val_labels, test_transforms, shuffle) return train_ds, test_ds, val_ds @staticmethod def load_MedNIST(download: bool = False, train_transforms: object = mednist_train_transforms, test_transforms: object = mednist_test_transforms, test_size: float = 0.2, val_size: float = 0.0, sample_size: float = 0.01, shuffle: bool = True): """Loads the MedNIST 2D image dataset. Consists of ~60.000 2D images from 6 classes: AbdomenCT, BreastMRI, ChestCT, CXR, Hand, HeadCT. Args: download (bool): If true, then data is downloaded before loading it as dataset. train_transforms (Compose): Specify the transformations to be applied to the training dataset. test_transforms (Compose): Specify the transformations to be applied to the test dataset. sample_size (float): Percentage of available images to be used. test_size (float): Precantage of sample to be used as test data. val_size (float): Percentage of sample to be used as validation data. shuffle (bool): Whether or not the data should be shuffled after loading. """ root_dir = './Data' resource = "https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1" md5 = "0bc7306e7427e00ad1c5526a6677552d" compressed_file = os.path.join(root_dir, "MedNIST.tar.gz") data_dir = os.path.join(root_dir, "MedNIST") if download: monai.apps.download_and_extract(resource, compressed_file, root_dir, md5) # Reading image filenames from dataset folders and assigning labels class_names = sorted(x for x in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, x))) num_class = len(class_names) image_files = [ [ os.path.join(data_dir, class_names[i], x) for x in os.listdir(os.path.join(data_dir, class_names[i])) ] for i in range(num_class) ] image_files = [images[:int(len(images) * sample_size)] for images in image_files] # Constructing data and labels num_each = [len(image_files[i]) for i in range(num_class)] data = [] labels = [] for i in range(num_class): data.extend(image_files[i]) labels.extend([int(i)] * num_each[i]) if shuffle: np.random.seed(42) indicies = np.arange(len(data)) np.random.shuffle(indicies) data = [data[i] for i in indicies] labels = [labels[i] for i in indicies] # Make train test validation split train_data, train_labels, test_data, test_labels, val_data, val_labels = _split(data, labels, test_size, val_size) # Construct and return datasets train_ds = MedNISTDataset(train_data, train_labels, train_transforms, shuffle) test_ds = MedNISTDataset(test_data, test_labels, test_transforms, shuffle) if val_size == 0: return train_ds, test_ds else: val_ds = MedNISTDataset(val_data, val_labels, test_transforms, shuffle) return train_ds, test_ds, val_ds
is_val_split=is_val_split) # data preprocessing/augmentation trans_train = MozartTheComposer([ #ScaleIntensity(), # AddChannel(), # RandSpatialCrop(roi_size=256, random_size=False), #CenterSpatialCrop(roi_size=2154), # 2154 # RandScaleIntensity(factors=0.25, prob=aug_prob), RandRotate(range_x=15, prob=aug_prob, keep_size=True, padding_mode="reflection"), RandRotate90(prob=aug_prob, spatial_axes=(1, 2)), RandFlip(spatial_axis=(1, 2), prob=aug_prob), ToTensor() ]) trans_val = MozartTheComposer([ # LoadImage(PILReader(), image_only=True), #ScaleIntensity(), # AddChannel(), # RandSpatialCrop(roi_size=256, random_size=False), #CenterSpatialCrop(roi_size=2154), ToTensor() ]) # create dataset class train_dataset = OurDataset(data=train_split, data_reader=PILReader(),
def transform_and_copy(data, cahce_dir): copy_dir = os.path.join(cahce_dir, 'copied_images') if not os.path.exists(copy_dir): os.mkdir(copy_dir) copy_list_path = os.path.join(copy_dir, 'copied_images.npy') if not os.path.exists(copy_list_path): print("transforming and copying images...") imageLoader = LoadImage() to_copy_list = [x for x in data if int(x['_label']) == 1] mul = 1 #int(len(data)/len(to_copy_list) - 1) rand_x_flip = RandFlip(spatial_axis=0, prob=0.50) rand_y_flip = RandFlip(spatial_axis=1, prob=0.50) rand_z_flip = RandFlip(spatial_axis=2, prob=0.50) rand_affine = RandAffine(prob=1.0, rotate_range=(0, 0, np.pi / 10), shear_range=(0.12, 0.12, 0.0), translate_range=(0, 0, 0), scale_range=(0.12, 0.12, 0.0), padding_mode="zeros") rand_gaussian_noise = RandGaussianNoise(prob=0.5, mean=0.0, std=0.05) transform = Compose([ AddChannel(), rand_x_flip, rand_y_flip, rand_z_flip, rand_affine, SqueezeDim(), ]) copy_list = [] n = len(to_copy_list) for i in range(len(to_copy_list)): print(f'Copying image {i+1}/{n}', end="\r") to_copy = to_copy_list[i] image_file = to_copy['image'] _image_file = replace_suffix(image_file, '.nii.gz', '') label = to_copy['label'] _label = to_copy['_label'] image_data, _ = imageLoader(image_file) seg_file = to_copy['seg'] seg_data, _ = nrrd.read(seg_file) for i in range(mul): rand_seed = np.random.randint(1e8) transform.set_random_state(seed=rand_seed) new_image_data = rand_gaussian_noise( np.array(transform(image_data))) transform.set_random_state(seed=rand_seed) new_seg_data = np.array(transform(seg_data)) #multi_slice_viewer(image_data, image_file) #multi_slice_viewer(seg_data, seg_file) #seg_image = MaskIntensity(seg_data)(image_data) #multi_slice_viewer(seg_image, seg_file) image_basename = os.path.basename(_image_file) seg_basename = image_basename + f'_seg_{i}.nrrd' image_basename = image_basename + f'_{i}.nii.gz' new_image_file = os.path.join(copy_dir, image_basename) write_nifti(new_image_data, new_image_file, resample=False) new_seg_file = os.path.join(copy_dir, seg_basename) nrrd.write(new_seg_file, new_seg_data) copy_list.append({ 'image': new_image_file, 'seg': new_seg_file, 'label': label, '_label': _label }) np.save(copy_list_path, copy_list) print("done transforming and copying!") copy_list = np.load(copy_list_path, allow_pickle=True) return copy_list
for i, elem in enumerate(image_files_list): if elem in list_all_images: image_files_list_updated.append(elem) image_class_list.append(image_class[i]) """ transforms """ train_transforms = Compose( [ LoadPNG(image_only=True), AddChannel(), ScaleIntensity(), RandRotate(range_x=15, prob=0.1, keep_size=True), # low probability for rotation RandFlip(spatial_axis=0, prob=0.5),# left right flip RandFlip(spatial_axis=1, prob=0.5), # horizontal flip RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5), ToTensor(), Lambda(lambda x: torch.cat([x, x, x], 0)), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ] ) val_transforms = Compose( [ LoadPNG(image_only=True), # Resize((480,640)), AddChannel(), ScaleIntensity(), ToTensor(),