SYMBOL INDEX (209 symbols across 18 files)

FILE: innvestigator.py
  class InnvestigateModel (line 8) | class InnvestigateModel(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, the_model, lrp_exponent=1, beta=.5, epsilon=1e-6,
    method cuda (line 69) | def cuda(self, device=None):
    method cpu (line 74) | def cpu(self):
    method register_hooks (line 79) | def register_hooks(self, parent_module):
    method relu_hook_function (line 103) | def relu_hook_function(module, grad_in, grad_out):
    method __call__ (line 109) | def __call__(self, in_tensor):
    method evaluate (line 123) | def evaluate(self, in_tensor):
    method get_r_values_per_layer (line 140) | def get_r_values_per_layer(self):
    method innvestigate (line 146) | def innvestigate(self, in_tensor=None, rel_for_class=None):
    method forward (line 224) | def forward(self, in_tensor):
    method extra_repr (line 227) | def extra_repr(self):

FILE: inverter_util.py
  function module_tracker (line 7) | def module_tracker(fwd_hook_func):
  class RelevancePropagator (line 25) | class RelevancePropagator:
    method __init__ (line 47) | def __init__(self, lrp_exponent, beta, method, epsilon, device):
    method reset_module_list (line 61) | def reset_module_list(self):
    method compute_propagated_relevance (line 75) | def compute_propagated_relevance(self, layer, relevance):
    method get_layer_fwd_hook (line 124) | def get_layer_fwd_hook(self, layer):
    method get_conv_method (line 159) | def get_conv_method(conv_module):
    method get_inv_conv_method (line 183) | def get_inv_conv_method(conv_module):
    method silent_pass (line 207) | def silent_pass(self, m, in_tensor: torch.Tensor,
    method get_inv_max_pool_method (line 214) | def get_inv_max_pool_method(max_pool_instance):
    method linear_inverse (line 237) | def linear_inverse(self, m, relevance_in):
    method linear_fwd_hook (line 342) | def linear_fwd_hook(self, m, in_tensor: torch.Tensor,
    method max_pool_nd_inverse (line 349) | def max_pool_nd_inverse(self, layer_instance, relevance_in):
    method max_pool_nd_fwd_hook (line 364) | def max_pool_nd_fwd_hook(self, m, in_tensor: torch.Tensor,
    method conv_nd_inverse (line 378) | def conv_nd_inverse(self, m, relevance_in):
    method conv_nd_fwd_hook (line 511) | def conv_nd_fwd_hook(self, m, in_tensor: torch.Tensor,

FILE: jrieke/datasets.py
  function load_data_table (line 30) | def load_data_table(table, image_dir, corrupt_images=None):
  function load_data_table_3T (line 65) | def load_data_table_3T():
  function load_data_table_15T (line 70) | def load_data_table_15T():
  function load_data_table_both (line 75) | def load_data_table_both():
  function get_image_filepath (line 83) | def get_image_filepath(df_row, root_dir=''):
  class ADNIDataset (line 96) | class ADNIDataset(Dataset):
    method __init__ (line 107) | def __init__(self, filenames, labels, mask=None, transform=None):
    method __len__ (line 121) | def __len__(self):
    method __getitem__ (line 124) | def __getitem__(self, idx):
    method image_shape (line 140) | def image_shape(self):
    method fit_normalization (line 144) | def fit_normalization(self, num_sample=None, show_progress=False):
    method get_raw_image (line 171) | def get_raw_image(self, idx):
  function print_df_stats (line 176) | def print_df_stats(df, df_train, df_val):
  function build_datasets (line 196) | def build_datasets(df, patients_train, patients_val, print_stats=True, n...
  function build_loaders (line 239) | def build_loaders(train_dataset, val_dataset):

FILE: jrieke/interpretation.py
  function sensitivity_analysis (line 13) | def sensitivity_analysis(model, image_tensor, target_class=None, postpro...
  function guided_backprop (line 73) | def guided_backprop(model, image_tensor, target_class=None, postprocess=...
  function occlusion (line 134) | def occlusion(model, image_tensor, target_class=None, size=50, stride=25...
  function area_occlusion (line 258) | def area_occlusion(model, image_tensor, area_masks, target_class=None, o...
  function all_children (line 315) | def all_children(model):
  function grad_cam (line 323) | def grad_cam(model, image_tensor, target_class=None, last_conv_layer=Non...
  function average_over_dataset (line 438) | def average_over_dataset(interpretation_method, model, dataset, num_samp...
  function heatmap_distance (line 488) | def heatmap_distance(a, b):

FILE: jrieke/models.py
  class ClassificationModel3D_2Node (line 3) | class ClassificationModel3D_2Node(nn.Module):
    method __init__ (line 6) | def __init__(self, dropout=0.4, dropout2=0.4):
    method forward (line 26) | def forward(self, x):

FILE: jrieke/utils.py
  function save_history (line 12) | def save_history(filename, trainer):
  function load_history (line 18) | def load_history(filename):
  function plot_learning_curve (line 24) | def plot_learning_curve(history):
  function load_nifti (line 58) | def load_nifti(file_path, mask=None, z_factor=None, remove_nan=True):
  function save_nifti (line 73) | def save_nifti(file_path, struct_arr):
  function plot_slices (line 92) | def plot_slices(struct_arr, num_slices=7, cmap='gray', vmin=None, vmax=N...
  function animate_slices (line 138) | def animate_slices(struct_arr, overlay=None, axis=0, reverse_direction=F...
  function resize_image (line 183) | def resize_image(img, size, interpolation=0):

FILE: mnist_test.py
  class Net (line 9) | class Net(nn.Module):
    method __init__ (line 10) | def __init__(self):
    method forward (line 22) | def forward(self, x):
  function train (line 32) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 47) | def test(args, model, device, test_loader):

FILE: nitorch/callbacks.py
  class Callback (line 16) | class Callback:
    method __init__ (line 21) | def __init__(self):
    method __call__ (line 24) | def __call__(self):
    method reset (line 27) | def reset(self):
    method final (line 30) | def final(self, **kwargs):
  class ModelCheckpoint (line 33) | class ModelCheckpoint(Callback):
    method __init__ (line 50) | def __init__(
    method __call__ (line 80) | def __call__(self, trainer, epoch, val_metrics):
    method reset (line 112) | def reset(self):
    method final (line 120) | def final(self, **kwargs):
    method save_model (line 137) | def save_model(self, trainer, full_path):
    method has_improved (line 144) | def has_improved(self, res):
  class EarlyStopping (line 157) | class EarlyStopping(Callback):
    method __init__ (line 172) | def __init__(self, patience, retain_metric, mode, ignore_before=0):
    method __call__ (line 181) | def __call__(self, trainer, epoch, val_metrics):
    method has_improved (line 195) | def has_improved(self, res):
    method reset (line 207) | def reset(self):
    method final (line 212) | def final(self, **kwargs):
  function visualize_feature_maps (line 218) | def visualize_feature_maps(features, return_fig=False):
  class CAE_VisualizeTraining (line 263) | class CAE_VisualizeTraining(Callback):
    method __init__ (line 272) | def __init__(self, model, max_train_iters, show_epochs_list=[], plotFe...
    method __call__ (line 296) | def __call__(self, inputs, labels, train_iter, epoch):

FILE: nitorch/data.py
  function load_nifti (line 8) | def load_nifti(file_path, dtype=np.float32, incl_header=False, z_factor=...
  function show_brain (line 56) | def show_brain(img, cut_coords=None,

FILE: nitorch/inference.py
  function predict (line 5) | def predict(
  function bce_with_logits_inference (line 69) | def bce_with_logits_inference(
  function bce_inference (line 88) | def bce_inference(
  function crossentropy_inference (line 105) | def crossentropy_inference(
  function regression_inference (line 118) | def regression_inference(
  function variational_inference (line 151) | def variational_inference(

FILE: nitorch/initialization.py
  function xavier (line 5) | def xavier(x):
  function xavier_uniform (line 8) | def xavier_uniform(x):
  function he (line 11) | def he(x):
  function he_uniform (line 14) | def he_uniform(x):
  function weights_init (line 18) | def weights_init(m, func=he_uniform):

FILE: nitorch/loss.py
  class BCE_KL_loss (line 4) | class BCE_KL_loss(torch.nn.Module):
    method __init__ (line 16) | def __init__(self):
    method forward (line 19) | def forward(self, outputs, target):
  class MSE_KL_loss (line 31) | class MSE_KL_loss(torch.nn.Module):
    method __init__ (line 43) | def __init__(self):
    method forward (line 46) | def forward(self, outputs, target):
  class Multihead_loss (line 58) | class Multihead_loss(torch.nn.Module):
    method __init__ (line 69) | def __init__(self, loss_function, weights=[1]):
    method forward (line 75) | def forward(self, outputs, target):

FILE: nitorch/metrics.py
  function specificity (line 3) | def specificity(y_true, y_pred):
  function sensitivity (line 7) | def sensitivity(y_true, y_pred):
  function balanced_accuracy (line 11) | def balanced_accuracy(y_true, y_pred):
  function auc_score (line 16) | def auc_score(y_true, y_pred):

FILE: nitorch/models.py
  class _CAE_3D (line 12) | class _CAE_3D(nn.Module):
    method __init__ (line 17) | def __init__(self, conv_channels):
    method _format_channels (line 32) | def _format_channels(self, conv_channels, is_nested_conv = False):
    method assign_parameter (line 55) | def assign_parameter(self, parameter, param_name, enable_nested = True):
    method add_conv_with_activation (line 89) | def add_conv_with_activation(self, inp_channels, out_channels, kernel_...
    method add_deconv_with_activation (line 96) | def add_deconv_with_activation(self, inp_channels, out_channels, kerne...
    method add_pool (line 104) | def add_pool(self, pool_type, kernel_size, padding, stride):
    method add_unpool (line 121) | def add_unpool(self, pool_type, kernel_size, padding, stride):
    method nested_reverse (line 137) | def nested_reverse(self, mylist):
  class CAE_3D (line 148) | class CAE_3D(_CAE_3D):
    method __init__ (line 153) | def __init__(self
    method set_debug (line 262) | def set_debug(self, bool_val):
    method set_return_encoder_out (line 265) | def set_return_encoder_out(self, bool_val):
    method forward (line 268) | def forward(self, x):
  class CAE_3D_with_pooling (line 297) | class CAE_3D_with_pooling(_CAE_3D):
    method __init__ (line 301) | def __init__(self
    method set_debug (line 437) | def set_debug(self, bool_val):
    method set_return_encoder_out (line 440) | def set_return_encoder_out(self, bool_val):
    method forward (line 443) | def forward(self, x):
  class MLP (line 492) | class MLP(nn.Module):
    method __init__ (line 496) | def __init__(self
    method set_debug (line 524) | def set_debug(self, bool_val):
    method _format_channels (line 527) | def _format_channels(self, layers):
    method add_linear_with_Relu (line 533) | def add_linear_with_Relu(self, layer):
    method forward (line 539) | def forward(self, x):

FILE: nitorch/trainer.py
  class Trainer (line 12) | class Trainer:
    method __init__ (line 13) | def __init__(
    method train_model (line 72) | def train_model(
    method finish_training (line 310) | def finish_training(self, train_metrics, val_metrics, epoch):
    method visualize_training (line 342) | def visualize_training(self, report, metrics=None, save_fig_path=""):
    method evaluate_model (line 364) | def evaluate_model(
    method report_metrics (line 445) | def report_metrics(
    method estimate_metrics (line 489) | def estimate_metrics(
    method _on_epoch_end (line 513) | def _on_epoch_end(

FILE: nitorch/transforms.py
  function normalize_float (line 8) | def normalize_float(x, min=-1):
  function normalize_float_torch (line 23) | def normalize_float_torch(x, min=-1):
  function normalization_factors (line 40) | def normalization_factors(data, train_idx, shape, mode="slice"):
  class CenterCrop (line 64) | class CenterCrop(object):
    method __init__ (line 71) | def __init__(self, size):
    method __call__ (line 79) | def __call__(self, img):
    method __repr__ (line 110) | def __repr__(self):
  class Normalize (line 114) | class Normalize(object):
    method __init__ (line 121) | def __init__(self, mean, std=1, masked=True, eps=1e-10):
    method __call__ (line 128) | def __call__(self, image):
    method denormalize (line 135) | def denormalize(self, image):
    method apply_transform (line 139) | def apply_transform(self, image):
    method zero_masked_transform (line 142) | def zero_masked_transform(self, image):
  class IntensityRescale (line 151) | class IntensityRescale:
    method __init__ (line 162) | def __init__(self, masked=True, on_gpu=False):
    method __call__ (line 166) | def __call__(self, image):
    method apply_transform (line 174) | def apply_transform(self, image):
    method zero_masked_transform (line 180) | def zero_masked_transform(self, image):
  class ToTensor (line 193) | class ToTensor(object):
    method __call__ (line 201) | def __call__(self, image):
  class Flip (line 207) | class Flip:
    method __init__ (line 216) | def __init__(self, axis=0, prob=0.5):
    method __call__ (line 220) | def __call__(self, image):
  class SagittalFlip (line 229) | class SagittalFlip(Flip):
    method __init__ (line 234) | def __init__(self, prob=0.5):
    method __call__ (line 237) | def __call__(self, image):
  class CoronalFlip (line 241) | class CoronalFlip(Flip):
    method __init__ (line 246) | def __init__(self, prob=0.5):
    method __call__ (line 249) | def __call__(self, image):
  class AxialFlip (line 254) | class AxialFlip(Flip):
    method __init__ (line 259) | def __init__(self, prob=0.5):
    method __call__ (line 262) | def __call__(self, image):
  class Rotate (line 267) | class Rotate:
    method __init__ (line 278) | def __init__(self, axis=0, deg=(-3, 3)):
    method __call__ (line 294) | def __call__(self, image):
  class SagittalRotate (line 305) | class SagittalRotate(Rotate):
    method __init__ (line 310) | def __init__(self, deg=(-3, 3)):
  class CoronalRotate (line 314) | class CoronalRotate(Rotate):
    method __init__ (line 319) | def __init__(self, deg=(-3, 3)):
  class AxialRotate (line 323) | class AxialRotate(Rotate):
    method __init__ (line 328) | def __init__(self, deg=(-3, 3)):
  class Translate (line 332) | class Translate:
    method __init__ (line 343) | def __init__(self, axis=0, dist=(-3, 3)):
    method __call__ (line 354) | def __call__(self, image):
  class SagittalTranslate (line 381) | class SagittalTranslate(Translate):
    method __init__ (line 386) | def __init__(self, dist=(-3, 3)):
  class CoronalTranslate (line 390) | class CoronalTranslate(Translate):
    method __init__ (line 395) | def __init__(self, dist=(-3, 3)):
  class AxialTranslate (line 399) | class AxialTranslate(Translate):
    method __init__ (line 404) | def __init__(self, dist=(-3, 3)):

FILE: nitorch/utils.py
  function dataset_length (line 6) | def dataset_length(data_loader):
  function count_parameters (line 32) | def count_parameters(model):
  function plot_grad_flow (line 36) | def plot_grad_flow(named_parameters):
  function is_bad_grad (line 66) | def is_bad_grad(grad_output):

FILE: utils.py
  function pprint (line 8) | def pprint(*args):
  class Flatten (line 13) | class Flatten(torch.nn.Module):
    method __init__ (line 14) | def __init__(self):
    method forward (line 17) | def forward(self, in_tensor):
  function load_data (line 21) | def load_data():
  function scale_mask (line 44) | def scale_mask(mask, shape):