def init_evaluator() -> eval_.Evaluator: """Initializes an evaluator. Args: directory (str): The directory for the results file. result_file_name (str): The result file name (CSV file). Returns: eval.Evaluator: An evaluator. """ # os.makedirs(directory, exist_ok=True) # generate result directory, if it does not exists # evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5)) evaluation_metrics = [metric.DiceCoefficient(), metric.HausdorffDistance()] # evaluation_metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(), metric.Accuracy(), metric.CohenKappaCoefficient(), metric.ProbabilisticDistance()] evaluation_metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(95), metric.CohenKappaCoefficient(), metric.Accuracy(), metric.JaccardCoefficient(), metric.MutualInformation(), metric.Precision(), metric.VolumeSimilarity(), metric.AreaUnderCurve(), metric.FalseNegative(),metric.FalsePositive(), metric.TruePositive(), metric.TrueNegative(),metric.Sensitivity(),metric.Specificity()] evaluation_metrics=[metric.DiceCoefficient(),metric.JaccardCoefficient(),metric.SurfaceDiceOverlap(),metric.Accuracy(), metric.FMeasure(),metric.CohenKappaCoefficient(),metric.VolumeSimilarity(),metric.MutualInformation(),metric.AreaUnderCurve(), metric.HausdorffDistance()] evaluator = eval_.SegmentationEvaluator(evaluation_metrics,{}) # evaluator.add_writer(eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name))) evaluator.add_label(1, 'WhiteMatter') evaluator.add_label(2, 'GreyMatter') evaluator.add_label(3, 'Hippocampus') evaluator.add_label(4, 'Amygdala') evaluator.add_label(5, 'Thalamus') # warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?') # you should add more metrics than just the Hausdorff distance! return evaluator
def main(): print(f'Welcome to MIALab {datetime.now().year}!') # --- scikit-learn sk_ensemble.RandomForestClassifier(max_depth=2, random_state=0) # --- SimpleITK image = sitk.Image(256, 128, 64, sitk.sitkInt16) print('Image dimension:', image.GetDimension()) print('Voxel intensity before setting:', image.GetPixel(0, 0, 0)) image.SetPixel(0, 0, 0, 1) print('Voxel intensity after setting:', image.GetPixel(0, 0, 0)) # --- numpy and matplotlib array = np.array([1, 23, 2, 4]) plt.plot(array) plt.ylabel('Some meaningful numbers') plt.xlabel('The x-axis') plt.title('Wohoo') plt.show() # --- pymia pymia_eval.SegmentationEvaluator([], {}) print('Everything seems to work fine!')
def main(data_dir: str, result_file: str, result_summary_file: str): # initialize metrics metrics = [ metric.DiceCoefficient(), metric.HausdorffDistance(percentile=95, metric='HDRFDST95'), metric.VolumeSimilarity() ] # define the labels to evaluate labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 5: 'THALAMUS'} evaluator = eval_.SegmentationEvaluator(metrics, labels) # get subjects to evaluate subject_dirs = [ subject for subject in glob.glob(os.path.join(data_dir, '*')) if os.path.isdir(subject) and os.path.basename(subject).startswith('Subject') ] for subject_dir in subject_dirs: subject_id = os.path.basename(subject_dir) print(f'Evaluating {subject_id}...') # load ground truth image and create artificial prediction by erosion ground_truth = sitk.ReadImage( os.path.join(subject_dir, f'{subject_id}_GT.mha')) prediction = ground_truth for label_val in labels.keys(): # erode each label we are going to evaluate prediction = sitk.BinaryErode(prediction, 1, sitk.sitkBall, 0, label_val) # evaluate the "prediction" against the ground truth evaluator.evaluate(prediction, ground_truth, subject_id) # use two writers to report the results writer.CSVWriter(result_file).write(evaluator.results) print('\nSubject-wise results...') writer.ConsoleWriter().write(evaluator.results) # report also mean and standard deviation among all subjects functions = {'MEAN': np.mean, 'STD': np.std} writer.CSVStatisticsWriter(result_summary_file, functions=functions).write(evaluator.results) print('\nAggregated statistic results...') writer.ConsoleStatisticsWriter(functions=functions).write( evaluator.results) # clear results such that the evaluator is ready for the next evaluation evaluator.clear()
def init_evaluator() -> eval_.Evaluator: """Initializes an evaluator. Returns: eval.Evaluator: An evaluator. """ # initialize metrics metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(95.0)] # define the labels to evaluate labels = { 1: 'WhiteMatter', 2: 'GreyMatter', 3: 'Hippocampus', 4: 'Amygdala', 5: 'Thalamus' } evaluator = eval_.SegmentationEvaluator(metrics, labels) return evaluator
def init_evaluator() -> eval_.Evaluator: """Initializes an evaluator. Returns: eval.Evaluator: An evaluator. """ # initialize metrics metrics = [metric.DiceCoefficient()] # todo: add hausdorff distance, 95th percentile (see metric.HausdorffDistance) warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?') # define the labels to evaluate labels = {1: 'WhiteMatter', 2: 'GreyMatter', 3: 'Hippocampus', 4: 'Amygdala', 5: 'Thalamus' } evaluator = eval_.SegmentationEvaluator(metrics, labels) return evaluator
def main(hdf_file, log_dir): # initialize the evaluator with the metrics and the labels to evaluate metrics = [metric.DiceCoefficient()] labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 3: 'HIPPOCAMPUS', 4: 'AMYGDALA', 5: 'THALAMUS'} evaluator = eval_.SegmentationEvaluator(metrics, labels) # we want to log the mean and standard deviation of the metrics among all subjects of the dataset functions = {'MEAN': np.mean, 'STD': np.std} statistics_aggregator = writer.StatisticsAggregator(functions=functions) console_writer = writer.ConsoleStatisticsWriter(functions=functions) # initialize TensorBoard writer tb = tensorboard.SummaryWriter(os.path.join(log_dir, 'logging-example-torch')) # setup the training datasource train_subjects, valid_subjects = ['Subject_1', 'Subject_2', 'Subject_3'], ['Subject_4'] extractor = extr.DataExtractor(categories=(defs.KEY_IMAGES, defs.KEY_LABELS)) indexing_strategy = extr.SliceIndexing() augmentation_transforms = [augm.RandomElasticDeformation(), augm.RandomMirror()] transforms = [tfm.Permute(permutation=(2, 0, 1)), tfm.Squeeze(entries=(defs.KEY_LABELS,))] train_transforms = tfm.ComposeTransform(augmentation_transforms + transforms) train_dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor, train_transforms, subject_subset=train_subjects) # setup the validation datasource valid_transforms = tfm.ComposeTransform([tfm.Permute(permutation=(2, 0, 1))]) valid_dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor, valid_transforms, subject_subset=valid_subjects) direct_extractor = extr.ComposeExtractor( [extr.SubjectExtractor(), extr.ImagePropertiesExtractor(), extr.DataExtractor(categories=(defs.KEY_LABELS,))] ) assembler = assm.SubjectAssembler(valid_dataset) # torch specific handling pytorch_train_dataset = pymia_torch.PytorchDatasetAdapter(train_dataset) train_loader = torch_data.dataloader.DataLoader(pytorch_train_dataset, batch_size=16, shuffle=True) pytorch_valid_dataset = pymia_torch.PytorchDatasetAdapter(valid_dataset) valid_loader = torch_data.dataloader.DataLoader(pytorch_valid_dataset, batch_size=16, shuffle=False) u_net = unet.UNetModel(ch_in=2, ch_out=6, n_channels=16, n_pooling=3).to(device) print(u_net) optimizer = optim.Adam(u_net.parameters(), lr=1e-3) train_batches = len(train_loader) # looping over the data in the dataset epochs = 100 for epoch in range(epochs): u_net.train() print(f'Epoch {epoch + 1}/{epochs}') # training print('training') for i, batch in enumerate(train_loader): x, y = batch[defs.KEY_IMAGES].to(device), batch[defs.KEY_LABELS].to(device).long() logits = u_net(x) optimizer.zero_grad() loss = F.cross_entropy(logits, y) loss.backward() optimizer.step() tb.add_scalar('train/loss', loss.item(), epoch*train_batches + i) print(f'[{i + 1}/{train_batches}]\tloss: {loss.item()}') # validation print('validation') with torch.no_grad(): u_net.eval() valid_batches = len(valid_loader) for i, batch in enumerate(valid_loader): x, sample_indices = batch[defs.KEY_IMAGES].to(device), batch[defs.KEY_SAMPLE_INDEX] logits = u_net(x) prediction = logits.argmax(dim=1, keepdim=True) numpy_prediction = prediction.cpu().numpy().transpose((0, 2, 3, 1)) is_last = i == valid_batches - 1 assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last) for subject_index in assembler.subjects_ready: subject_prediction = assembler.get_assembled_subject(subject_index) direct_sample = train_dataset.direct_extract(direct_extractor, subject_index) target, image_properties = direct_sample[defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES] # evaluate the prediction against the reference evaluator.evaluate(subject_prediction[..., 0], target[..., 0], direct_sample[defs.KEY_SUBJECT]) # calculate mean and standard deviation of each metric results = statistics_aggregator.calculate(evaluator.results) # log to TensorBoard into category train for result in results: tb.add_scalar(f'valid/{result.metric}-{result.id_}', result.value, epoch) console_writer.write(evaluator.results) # clear results such that the evaluator is ready for the next evaluation evaluator.clear()
def main(hdf_file: str, log_dir: str): # initialize the evaluator with the metrics and the labels to evaluate metrics = [metric.DiceCoefficient()] labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 5: 'THALAMUS'} evaluator = eval_.SegmentationEvaluator(metrics, labels) # we want to log the mean and standard deviation of the metrics among all subjects of the dataset functions = {'MEAN': np.mean, 'STD': np.std} statistics_aggregator = writer.StatisticsAggregator(functions=functions) # initialize TensorBoard writer # tb = tensorboard.SummaryWriter(os.path.join(log_dir, 'logging-example-torch')) tb = tf.summary.create_file_writer( os.path.join(log_dir, 'logging-example-tensorflow')) # initialize the data handling dataset = extr.PymiaDatasource( hdf_file, extr.SliceIndexing(), extr.DataExtractor(categories=(defs.KEY_IMAGES, ))) gen_fn = pymia_tf.get_tf_generator(dataset) tf_dataset = tf.data.Dataset.from_generator(generator=gen_fn, output_types={ defs.KEY_IMAGES: tf.float32, defs.KEY_SAMPLE_INDEX: tf.int64 }) loader = tf_dataset.batch(100) assembler = assm.SubjectAssembler(dataset) direct_extractor = extr.ComposeExtractor([ extr.SubjectExtractor(), # extraction of the subject name extr.ImagePropertiesExtractor( ), # Extraction of image properties (origin, spacing, etc.) for storage extr.DataExtractor( categories=(defs.KEY_LABELS, )) # Extraction of "labels" entries for evaluation ]) # initialize a dummy network, which returns a random prediction class DummyNetwork(tf.keras.Model): def call(self, inputs): return tf.random.uniform((*inputs.shape[:-1], 1), 0, 6, dtype=tf.int32) dummy_network = DummyNetwork() tf.random.set_seed(0) # set seed for reproducibility nb_batches = len(dataset) // 2 epochs = 10 for epoch in range(epochs): print(f'Epoch {epoch + 1}/{epochs}') for i, batch in enumerate(loader): # get the data from batch and predict x, sample_indices = batch[defs.KEY_IMAGES], batch[ defs.KEY_SAMPLE_INDEX] prediction = dummy_network(x) # translate the prediction to numpy numpy_prediction = prediction.numpy() # add the batch prediction to the assembler is_last = i == nb_batches - 1 assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last) # process the subjects/images that are fully assembled for subject_index in assembler.subjects_ready: subject_prediction = assembler.get_assembled_subject( subject_index) # extract the target and image properties via direct extract direct_sample = dataset.direct_extract(direct_extractor, subject_index) reference, image_properties = direct_sample[ defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES] # evaluate the prediction against the reference evaluator.evaluate(subject_prediction[..., 0], reference[..., 0], direct_sample[defs.KEY_SUBJECT]) # calculate mean and standard deviation of each metric results = statistics_aggregator.calculate(evaluator.results) # log to TensorBoard into category train for result in results: with tb.as_default(): tf.summary.scalar(f'train/{result.metric}-{result.id_}', result.value, epoch) # clear results such that the evaluator is ready for the next evaluation evaluator.clear()
def main(hdf_file: str, log_dir: str): # initialize the evaluator with the metrics and the labels to evaluate metrics = [metric.DiceCoefficient()] labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 5: 'THALAMUS'} evaluator = eval_.SegmentationEvaluator(metrics, labels) # we want to log the mean and standard deviation of the metrics among all subjects of the dataset functions = {'MEAN': np.mean, 'STD': np.std} statistics_aggregator = writer.StatisticsAggregator(functions=functions) # initialize TensorBoard writer tb = tensorboard.SummaryWriter( os.path.join(log_dir, 'logging-example-torch')) # initialize the data handling transform = tfm.Permute(permutation=(2, 0, 1), entries=(defs.KEY_IMAGES, )) dataset = extr.PymiaDatasource( hdf_file, extr.SliceIndexing(), extr.DataExtractor(categories=(defs.KEY_IMAGES, )), transform) pytorch_dataset = pymia_torch.PytorchDatasetAdapter(dataset) loader = torch_data.dataloader.DataLoader(pytorch_dataset, batch_size=100, shuffle=False) assembler = assm.SubjectAssembler(dataset) direct_extractor = extr.ComposeExtractor([ extr.SubjectExtractor(), # extraction of the subject name extr.ImagePropertiesExtractor( ), # Extraction of image properties (origin, spacing, etc.) for storage extr.DataExtractor( categories=(defs.KEY_LABELS, )) # Extraction of "labels" entries for evaluation ]) # initialize a dummy network, which returns a random prediction class DummyNetwork(nn.Module): def forward(self, x): return torch.randint(0, 6, (x.size(0), 1, *x.size()[2:])) dummy_network = DummyNetwork() torch.manual_seed(0) # set seed for reproducibility nb_batches = len(loader) epochs = 10 for epoch in range(epochs): print(f'Epoch {epoch + 1}/{epochs}') for i, batch in enumerate(loader): # get the data from batch and predict x, sample_indices = batch[defs.KEY_IMAGES], batch[ defs.KEY_SAMPLE_INDEX] prediction = dummy_network(x) # translate the prediction to numpy and back to (B)HWC (channel last) numpy_prediction = prediction.numpy().transpose((0, 2, 3, 1)) # add the batch prediction to the assembler is_last = i == nb_batches - 1 assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last) # process the subjects/images that are fully assembled for subject_index in assembler.subjects_ready: subject_prediction = assembler.get_assembled_subject( subject_index) # extract the target and image properties via direct extract direct_sample = dataset.direct_extract(direct_extractor, subject_index) reference, image_properties = direct_sample[ defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES] # evaluate the prediction against the reference evaluator.evaluate(subject_prediction[..., 0], reference[..., 0], direct_sample[defs.KEY_SUBJECT]) # calculate mean and standard deviation of each metric results = statistics_aggregator.calculate(evaluator.results) # log to TensorBoard into category train for result in results: tb.add_scalar(f'train/{result.metric}-{result.id_}', result.value, epoch) # clear results such that the evaluator is ready for the next evaluation evaluator.clear()
def main(hdf_file, log_dir): # initialize the evaluator with the metrics and the labels to evaluate metrics = [metric.DiceCoefficient()] labels = { 1: 'WHITEMATTER', 2: 'GREYMATTER', 3: 'HIPPOCAMPUS', 4: 'AMYGDALA', 5: 'THALAMUS' } evaluator = eval_.SegmentationEvaluator(metrics, labels) # we want to log the mean and standard deviation of the metrics among all subjects of the dataset functions = {'MEAN': np.mean, 'STD': np.std} statistics_aggregator = writer.StatisticsAggregator(functions=functions) console_writer = writer.ConsoleStatisticsWriter(functions=functions) # initialize TensorBoard writer summary_writer = tf.summary.create_file_writer( os.path.join(log_dir, 'logging-example-tensorflow')) # setup the training datasource train_subjects, valid_subjects = ['Subject_1', 'Subject_2', 'Subject_3'], ['Subject_4'] extractor = extr.DataExtractor(categories=(defs.KEY_IMAGES, defs.KEY_LABELS)) indexing_strategy = extr.SliceIndexing() augmentation_transforms = [ augm.RandomElasticDeformation(), augm.RandomMirror() ] transforms = [tfm.Squeeze(entries=(defs.KEY_LABELS, ))] train_transforms = tfm.ComposeTransform(augmentation_transforms + transforms) train_dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor, train_transforms, subject_subset=train_subjects) # setup the validation datasource batch_size = 16 valid_transforms = tfm.ComposeTransform([]) valid_dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor, valid_transforms, subject_subset=valid_subjects) direct_extractor = extr.ComposeExtractor([ extr.SubjectExtractor(), extr.ImagePropertiesExtractor(), extr.DataExtractor(categories=(defs.KEY_LABELS, )) ]) assembler = assm.SubjectAssembler(valid_dataset) # tensorflow specific handling train_gen_fn = pymia_tf.get_tf_generator(train_dataset) tf_train_dataset = tf.data.Dataset.from_generator( generator=train_gen_fn, output_types={ defs.KEY_IMAGES: tf.float32, defs.KEY_LABELS: tf.int64, defs.KEY_SAMPLE_INDEX: tf.int64 }) tf_train_dataset = tf_train_dataset.batch(batch_size).shuffle( len(train_dataset)) valid_gen_fn = pymia_tf.get_tf_generator(valid_dataset) tf_valid_dataset = tf.data.Dataset.from_generator( generator=valid_gen_fn, output_types={ defs.KEY_IMAGES: tf.float32, defs.KEY_LABELS: tf.int64, defs.KEY_SAMPLE_INDEX: tf.int64 }) tf_valid_dataset = tf_valid_dataset.batch(batch_size) u_net = unet.build_model(channels=2, num_classes=6, layer_depth=3, filters_root=16) optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3) train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32) train_batches = len(train_dataset) // batch_size # looping over the data in the dataset epochs = 100 for epoch in range(epochs): print(f'Epoch {epoch + 1}/{epochs}') # training print('training') for i, batch in enumerate(tf_train_dataset): x, y = batch[defs.KEY_IMAGES], batch[defs.KEY_LABELS] with tf.GradientTape() as tape: logits = u_net(x, training=True) loss = tf.keras.losses.sparse_categorical_crossentropy( y, logits, from_logits=True) grads = tape.gradient(loss, u_net.trainable_variables) optimizer.apply_gradients(zip(grads, u_net.trainable_variables)) train_loss(loss) with summary_writer.as_default(): tf.summary.scalar('train/loss', train_loss.result(), step=epoch * train_batches + i) print( f'[{i + 1}/{train_batches}]\tloss: {train_loss.result().numpy()}' ) # validation print('validation') valid_batches = len(valid_dataset) // batch_size for i, batch in enumerate(tf_valid_dataset): x, sample_indices = batch[defs.KEY_IMAGES], batch[ defs.KEY_SAMPLE_INDEX] logits = u_net(x) prediction = tf.expand_dims(tf.math.argmax(logits, -1), -1) numpy_prediction = prediction.numpy() is_last = i == valid_batches - 1 assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last) for subject_index in assembler.subjects_ready: subject_prediction = assembler.get_assembled_subject( subject_index) direct_sample = train_dataset.direct_extract( direct_extractor, subject_index) target, image_properties = direct_sample[ defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES] # evaluate the prediction against the reference evaluator.evaluate(subject_prediction[..., 0], target[..., 0], direct_sample[defs.KEY_SUBJECT]) # calculate mean and standard deviation of each metric results = statistics_aggregator.calculate(evaluator.results) # log to TensorBoard into category train with summary_writer.as_default(): for result in results: tf.summary.scalar(f'valid/{result.metric}-{result.id_}', result.value, epoch) console_writer.write(evaluator.results) # clear results such that the evaluator is ready for the next evaluation evaluator.clear()