SYMBOL INDEX (322 symbols across 17 files)

FILE: datasets.py
  function load_train_val (line 26) | def load_train_val(train_tasks, val_tasks=None, fast=False,
  function load_all (line 60) | def load_all(tasks, buildings=None, batch_size=64, split_file="data/spli...
  function load_test (line 75) | def load_test(all_tasks, buildings=["almena", "albertville"], sample=4):
  function load_ood (line 96) | def load_ood(tasks=[tasks.rgb], ood_path=OOD_DIR, sample=21):
  class TaskDataset (line 107) | class TaskDataset(Dataset):
    method __init__ (line 109) | def __init__(self, buildings, tasks=[get_task("rgb"), get_task("normal...
    method reset_unpaired (line 141) | def reset_unpaired(self):
    method building_files (line 145) | def building_files(self, task, building):
    method building_files_raid (line 149) | def building_files_raid(self, task, building):
    method convert_path (line 152) | def convert_path(self, source_file, task):
    method convert_path_raid (line 165) | def convert_path_raid(self, full_file, task):
    method __len__ (line 173) | def __len__(self):
    method __getitem__ (line 176) | def __getitem__(self, idx):
  class TrainTaskDataset (line 196) | class TrainTaskDataset(TaskDataset):
    method __getitem__ (line 198) | def __getitem__(self, idx):
  class ImageDataset (line 220) | class ImageDataset(Dataset):
    method __init__ (line 222) | def __init__(
    method __len__ (line 243) | def __len__(self):
    method __getitem__ (line 246) | def __getitem__(self, idx):

FILE: demo.py
  function save_outputs (line 52) | def save_outputs(img_path, output_file_name):

FILE: energy.py
  function get_energy_loss (line 25) | def get_energy_loss(
  function generate_config (line 47) | def generate_config(perceptual_tasks, target_task=tasks.normal, tree_str...
  function coeff_hook (line 720) | def coeff_hook(coeff):
  class EnergyLoss (line 726) | class EnergyLoss(object):
    method __init__ (line 728) | def __init__(self, paths, losses, plots,
    method compute_paths (line 747) | def compute_paths(self, graph, reality=None, paths=None):
    method get_tasks (line 758) | def get_tasks(self, reality):
    method __call__ (line 773) | def __call__(self, graph, discriminator=None, realities=[], loss_types...
    method logger_hooks (line 826) | def logger_hooks(self, logger):
    method logger_update (line 850) | def logger_update(self, logger):
    method plot_paths (line 876) | def plot_paths(self, graph, logger, realities=[], plot_names=None, epo...
    method __repr__ (line 957) | def __repr__(self):
  class WinRateEnergyLoss (line 961) | class WinRateEnergyLoss(EnergyLoss):
    method __init__ (line 963) | def __init__(self, *args, **kwargs):
    method __call__ (line 975) | def __call__(self, graph, discriminator=None, realities=[], loss_types...
    method logger_update (line 1013) | def logger_update(self, logger):

FILE: graph.py
  class TaskGraph (line 21) | class TaskGraph(TrainableModel):
    method __init__ (line 25) | def __init__(
    method edge (line 82) | def edge(self, src_task, dest_task):
    method sample_path (line 88) | def sample_path(self, path, reality=None, use_cache=False, cache={}):
    method save (line 107) | def save(self, weights_file=None, weights_dir=None):
    method load_weights (line 130) | def load_weights(self, weights_file=None):

FILE: logger.py
  class BaseLogger (line 17) | class BaseLogger(object):
    method __init__ (line 19) | def __init__(self, name, verbose=True):
    method add_hook (line 28) | def add_hook(self, hook, feature='epoch', freq=40):
    method update (line 31) | def update(self, feature, x):
    method step (line 47) | def step(self):
    method text (line 61) | def text(self, text, end="\n"):
    method plot (line 64) | def plot(self, data, plot_name, opts={}):
    method images (line 67) | def images(self, data, image_name):
    method plot_feature (line 70) | def plot_feature(self, feature, opts={}):
    method plot_features (line 73) | def plot_features(self, features, name, opts={}):
  class Logger (line 78) | class Logger(BaseLogger):
    method __init__ (line 80) | def __init__(self, *args, **kwargs):
    method text (line 84) | def text(self, text, end='\n'):
    method plot (line 87) | def plot(self, data, plot_name, opts={}):
  class VisdomLogger (line 94) | class VisdomLogger(BaseLogger):
    method __init__ (line 96) | def __init__(self, *args, **kwargs):
    method text (line 111) | def text(self, text, end='\n'):
    method window (line 124) | def window(self, plot_name, plot_func, *args, **kwargs):
    method plot (line 136) | def plot(self, data, plot_name, opts={}):
    method histogram (line 141) | def histogram(self, data, plot_name, opts={}):
    method scatter (line 144) | def scatter(self, X, Y, plot_name, opts={}):
    method bar (line 147) | def bar(self, data, plot_name, opts={}):
    method save (line 150) | def save(self):
    method images (line 153) | def images(self, data, plot_name, opts={}, nrow=2, normalize=False, re...
    method images_grouped (line 163) | def images_grouped(self, image_groups, plot_name, **kwargs):

FILE: models.py
  class AbstractModel (line 19) | class AbstractModel(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method compile (line 25) | def compile(self, optimizer=None, **kwargs):
    method predict_on_batch (line 38) | def predict_on_batch(self, data):
    method fit_on_batch (line 45) | def fit_on_batch(self, data, target, loss_fn=None, train=True):
    method step (line 74) | def step(self, loss, train=True):
    method load (line 88) | def load(cls, weights_file=None):
    method load_weights (line 97) | def load_weights(self, weights_file, backward_compatible=False):
    method save (line 103) | def save(self, weights_file):
    method loss (line 107) | def loss(self, pred, target):
    method forward (line 110) | def forward(self, x):
  class TrainableModel (line 119) | class TrainableModel(AbstractModel):
    method __init__ (line 120) | def __init__(self):
    method _process_data (line 124) | def _process_data(self, datagen, loss_fn=None, train=True, logger=None):
    method fit (line 136) | def fit(self, datagen, loss_fn=None, logger=None):
    method fit_with_data (line 140) | def fit_with_data(self, datagen, loss_fn=None, logger=None):
    method fit_with_metrics (line 148) | def fit_with_metrics(self, datagen, loss_fn=None, logger=None):
    method predict_with_data (line 157) | def predict_with_data(self, datagen, loss_fn=None, logger=None):
    method predict_with_metrics (line 167) | def predict_with_metrics(self, datagen, loss_fn=None, logger=None):
    method predict (line 176) | def predict(self, datagen):
  class DataParallelModel (line 182) | class DataParallelModel(TrainableModel):
    method __init__ (line 183) | def __init__(self, *args, **kwargs):
    method forward (line 187) | def forward(self, x):
    method loss (line 190) | def loss(self, x, preds):
    method module (line 194) | def module(self):
    method load (line 198) | def load(cls, model=TrainableModel(), weights_file=None):
  class WrapperModel (line 213) | class WrapperModel(TrainableModel):
    method __init__ (line 214) | def __init__(self, model):
    method forward (line 218) | def forward(self, x):
    method loss (line 221) | def loss(self, x, preds):
    method __getitem__ (line 224) | def __getitem__(self, i):
    method module (line 228) | def module(self):

FILE: modules/depth_nets.py
  class ResidualsNet (line 16) | class ResidualsNet(TrainableModel):
    method __init__ (line 17) | def __init__(self):
    method forward (line 36) | def forward(self, x):
    method loss (line 45) | def loss(self, pred, target):
  class UNet_up_block (line 50) | class UNet_up_block(nn.Module):
    method __init__ (line 51) | def __init__(self, prev_channel, input_channel, output_channel, up_sam...
    method forward (line 63) | def forward(self, prev_feature_map, x):
  class UNet_down_block (line 73) | class UNet_down_block(nn.Module):
    method __init__ (line 74) | def __init__(self, input_channel, output_channel, down_size=True):
    method forward (line 86) | def forward(self, x):
  class UNetDepth (line 96) | class UNetDepth(TrainableModel):
    method __init__ (line 97) | def __init__(self):
    method forward (line 127) | def forward(self, x):
    method loss (line 150) | def loss(self, pred, target):
  class ConvBlock (line 156) | class ConvBlock(nn.Module):
    method __init__ (line 157) | def __init__(self, f1, f2, use_groupnorm=True, groups=8, dilation=1, t...
    method forward (line 170) | def forward(self, x):
  class Bottleneck (line 180) | class Bottleneck(nn.Module):
    method __init__ (line 183) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 196) | def forward(self, x):
  class ResNetOriginal (line 218) | class ResNetOriginal(nn.Module):
    method __init__ (line 220) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 242) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 259) | def forward(self, x):
  class ResNetDepth (line 276) | class ResNetDepth(TrainableModel):
    method __init__ (line 277) | def __init__(self):
    method forward (line 296) | def forward(self, x):
    method loss (line 306) | def loss(self, pred, target):

FILE: modules/percep_nets.py
  class ConvBlock (line 17) | class ConvBlock(nn.Module):
    method __init__ (line 18) | def __init__(self, f1, f2, kernel_size=3, padding=1, use_groupnorm=Tru...
    method forward (line 31) | def forward(self, x):
  class DenseNet (line 42) | class DenseNet(TrainableModel):
    method __init__ (line 43) | def __init__(self):
    method forward (line 54) | def forward(self, x):
    method loss (line 58) | def loss(self, pred, target):
  class Dense1by1Net (line 63) | class Dense1by1Net(TrainableModel):
    method __init__ (line 64) | def __init__(self):
    method forward (line 77) | def forward(self, x):
    method loss (line 81) | def loss(self, pred, target):
  class Dense1by1end (line 85) | class Dense1by1end(TrainableModel):
    method __init__ (line 86) | def __init__(self):
    method forward (line 99) | def forward(self, x):
    method loss (line 103) | def loss(self, pred, target):
  class DenseKernelsNet (line 107) | class DenseKernelsNet(TrainableModel):
    method __init__ (line 108) | def __init__(self, kernel_size=7):
    method forward (line 122) | def forward(self, x):
    method loss (line 126) | def loss(self, pred, target):
  class DeepNet (line 131) | class DeepNet(TrainableModel):
    method __init__ (line 132) | def __init__(self):
    method forward (line 145) | def forward(self, x):
    method loss (line 149) | def loss(self, pred, target):
  class WideNet (line 154) | class WideNet(TrainableModel):
    method __init__ (line 155) | def __init__(self):
    method forward (line 166) | def forward(self, x):
    method loss (line 170) | def loss(self, pred, target):
  class PyramidNet (line 175) | class PyramidNet(TrainableModel):
    method __init__ (line 176) | def __init__(self):
    method forward (line 188) | def forward(self, x):
    method loss (line 192) | def loss(self, pred, target):
  class BaseNet (line 198) | class BaseNet(TrainableModel):
    method __init__ (line 199) | def __init__(self):
    method forward (line 209) | def forward(self, x):
    method loss (line 213) | def loss(self, pred, target):
  class ResidualsNet (line 218) | class ResidualsNet(TrainableModel):
    method __init__ (line 219) | def __init__(self):
    method forward (line 236) | def forward(self, x):
    method loss (line 245) | def loss(self, pred, target):
  class ResNet50 (line 249) | class ResNet50(TrainableModel):
    method __init__ (line 250) | def __init__(self, num_classes=365, in_channels=3):
    method forward (line 256) | def forward(self, x):
    method loss (line 260) | def loss(self, pred, target):

FILE: modules/resnet.py
  function conv3x3 (line 17) | def conv3x3(in_planes, out_planes, stride=1, groups=1):
  function conv1x1 (line 23) | def conv1x1(in_planes, out_planes, stride=1):
  class BasicBlock (line 29) | class BasicBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 46) | def forward(self, x):
  class Bottleneck (line 64) | class Bottleneck(nn.Module):
    method __init__ (line 67) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 80) | def forward(self, x):
  class ResNetOriginal (line 102) | class ResNetOriginal(nn.Module):
    method __init__ (line 104) | def __init__(self, block, layers, in_channels=3, num_classes=1000):
    method _make_layer (line 126) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 143) | def forward(self, x):
  class ResNet (line 160) | class ResNet(TrainableModel):
    method __init__ (line 161) | def __init__(self, in_channels=3, out_channels=1000):
    method forward (line 166) | def forward(self, x):
    method loss (line 175) | def loss(self, pred, target):
  class ResNetClass (line 180) | class ResNetClass(TrainableModel):
    method __init__ (line 181) | def __init__(self):
    method forward (line 185) | def forward(self, x):
    method loss (line 192) | def loss(self, pred, target):

FILE: modules/unet.py
  class UNet_up_block (line 17) | class UNet_up_block(nn.Module):
    method __init__ (line 18) | def __init__(self, prev_channel, input_channel, output_channel, up_sam...
    method forward (line 30) | def forward(self, prev_feature_map, x):
  class UNet_down_block (line 40) | class UNet_down_block(nn.Module):
    method __init__ (line 41) | def __init__(self, input_channel, output_channel, down_size=True):
    method forward (line 53) | def forward(self, x):
  class UNet (line 62) | class UNet(TrainableModel):
    method __init__ (line 63) | def __init__(self,  downsample=6, in_channels=3, out_channels=3):
    method forward (line 89) | def forward(self, x):
    method loss (line 107) | def loss(self, pred, target):
  class UNetReshade (line 111) | class UNetReshade(TrainableModel):
    method __init__ (line 112) | def __init__(self,  downsample=6, in_channels=3, out_channels=3):
    method forward (line 138) | def forward(self, x):
    method loss (line 158) | def loss(self, pred, target):
  class UNetOld (line 163) | class UNetOld(TrainableModel):
    method __init__ (line 164) | def __init__(self, in_channels=3, out_channels=3):
    method forward (line 195) | def forward(self, x):
    method loss (line 218) | def loss(self, pred, target):
  class ConvBlock (line 223) | class ConvBlock(nn.Module):
    method __init__ (line 224) | def __init__(self, f1, f2, kernel_size=3, padding=1, use_groupnorm=Tru...
    method forward (line 237) | def forward(self, x):
  class UNetOld2 (line 247) | class UNetOld2(TrainableModel):
    method __init__ (line 248) | def __init__(self, in_channels=3, out_channels=3):
    method forward (line 283) | def forward(self, x):
    method loss (line 307) | def loss(self, pred, target):

FILE: modules/unet_mirrored.py
  class UNet_up_block (line 17) | class UNet_up_block(nn.Module):
    method __init__ (line 18) | def __init__(self, prev_channel, input_channel, output_channel, up_sam...
    method forward (line 30) | def forward(self, prev_feature_map, x):
  class UNet_down_block (line 40) | class UNet_down_block(nn.Module):
    method __init__ (line 41) | def __init__(self, input_channel, output_channel, down_size=True):
    method forward (line 53) | def forward(self, x):
  class UNet (line 62) | class UNet(TrainableModel):
    method __init__ (line 63) | def __init__(self, downsample=6, in_channels=3, out_channels=3):
    method forward (line 89) | def forward(self, x):
    method loss (line 114) | def loss(self, pred, target):
  class UNetReshade (line 118) | class UNetReshade(TrainableModel):
    method __init__ (line 119) | def __init__(self,  downsample=6, in_channels=3, out_channels=3):
    method forward (line 145) | def forward(self, x):
    method loss (line 165) | def loss(self, pred, target):
  class UNetOld (line 170) | class UNetOld(TrainableModel):
    method __init__ (line 171) | def __init__(self, in_channels=3, out_channels=3):
    method forward (line 202) | def forward(self, x):
    method loss (line 225) | def loss(self, pred, target):
  class ConvBlock (line 230) | class ConvBlock(nn.Module):
    method __init__ (line 231) | def __init__(self, f1, f2, kernel_size=3, padding=1, use_groupnorm=Tru...
    method forward (line 244) | def forward(self, x):
  class UNetOld2 (line 254) | class UNetOld2(TrainableModel):
    method __init__ (line 255) | def __init__(self, in_channels=3, out_channels=3):
    method forward (line 290) | def forward(self, x):
    method loss (line 314) | def loss(self, pred, target):

FILE: plotting.py
  function jointplot (line 3) | def jointplot(logger, data, loss_type="mse_loss"):
  function get_running_means_w_std_bounds_and_legend_on_diff_prev_time_step (line 7) | def get_running_means_w_std_bounds_and_legend_on_diff_prev_time_step(lis...
  function get_running_means_w_std_bounds_and_legend (line 23) | def get_running_means_w_std_bounds_and_legend(list_of_list_values):
  function get_running_std (line 35) | def get_running_std(list_of_list_values):
  function get_running_p_coeffs (line 39) | def get_running_p_coeffs(list_of_list_values_1, list_of_list_values_2):
  function mseplots (line 54) | def mseplots(data, logger):
  function curvatureplots (line 69) | def curvatureplots(data, logger):
  function depthplots (line 85) | def depthplots(data, logger):
  function covarianceplot (line 100) | def covarianceplot(data, logger):

FILE: scripts/energy_calc.py
  function main (line 30) | def main(

FILE: task_configs.py
  class GaussianBulr (line 30) | class GaussianBulr(object):
    method __init__ (line 31) | def __init__(self, radius):
    method __call__ (line 35) | def __call__(self, im):
    method __repr__ (line 38) | def __repr__(self):
  function get_model (line 56) | def get_model(src_task, dest_task):
  class Task (line 82) | class Task(object):
    method __init__ (line 94) | def __init__(self, name,
    method norm (line 108) | def norm(self, pred, target, batch_mean=True, compute_mse=True):
    method __call__ (line 117) | def __call__(self, size=256):
    method plot_func (line 121) | def plot_func(self, data, name, logger, **kwargs):
    method file_loader (line 125) | def file_loader(self, path, resize=None, seed=0, T=0):
    method __eq__ (line 128) | def __eq__(self, other):
    method __repr__ (line 131) | def __repr__(self):
    method __hash__ (line 134) | def __hash__(self):
  class RealityTask (line 143) | class RealityTask(Task):
    method __init__ (line 146) | def __init__(self, name, dataset, tasks=None, use_dataset=True, shuffl...
    method from_dataloader (line 163) | def from_dataloader(cls, name, loader, tasks):
    method from_static (line 172) | def from_static(cls, name, data, tasks):
    method norm (line 178) | def norm(self, pred, target, batch_mean=True):
    method step (line 182) | def step(self):
    method reload (line 185) | def reload(self):
  class ImageTask (line 192) | class ImageTask(Task):
    method __init__ (line 195) | def __init__(self, *args, **kwargs):
    method build_mask (line 206) | def build_mask(target, val=0.0, tol=1e-3):
    method norm (line 219) | def norm(self, pred, target, batch_mean=True, compute_mask=0, compute_...
    method __call__ (line 226) | def __call__(self, size=256, blur_radius=None):
    method plot_func (line 235) | def plot_func(self, data, name, logger, resize=None, nrow=2):
    method file_loader (line 238) | def file_loader(self, path, resize=None, crop=None, seed=0, jitter=Fal...
    method load_image_transform (line 242) | def load_image_transform(self, resize=None, crop=None, seed=0, jitter=...
  class ImageClassTask (line 259) | class ImageClassTask(ImageTask):
    method __init__ (line 262) | def __init__(self, *args, **kwargs):
    method norm (line 267) | def norm(self, pred, target):
    method plot_func (line 271) | def plot_func(self, data, name, logger, resize=None):
    method file_loader (line 277) | def file_loader(self, path, resize=None):
  class PointInfoTask (line 285) | class PointInfoTask(Task):
    method __init__ (line 288) | def __init__(self, *args, **kwargs):
    method plot_func (line 294) | def plot_func(self, data, name, logger):
    method file_loader (line 297) | def file_loader(self, path, resize=None):
  function clamp_maximum_transform (line 309) | def clamp_maximum_transform(x, max_val=8000.0):
  function crop_transform (line 313) | def crop_transform(x, max_val=8000.0):
  function sobel_transform (line 317) | def sobel_transform(x):
  function blur_transform (line 326) | def blur_transform(x, max_val=4000.0):
  function get_task (line 337) | def get_task(task_name):

FILE: train.py
  function main (line 55) | def main(

FILE: transfers.py
  class Transfer (line 117) | class Transfer(nn.Module):
    method __init__ (line 119) | def __init__(self, src_task, dest_task,
    method load_model (line 168) | def load_model(self):
    method __call__ (line 180) | def __call__(self, x):
    method __repr__ (line 186) | def __repr__(self):
  class RealityTransfer (line 190) | class RealityTransfer(Transfer):
    method __init__ (line 192) | def __init__(self, src_task, dest_task):
    method load_model (line 195) | def load_model(self, optimizer=True):
    method __call__ (line 198) | def __call__(self, x):
  class FineTunedTransfer (line 203) | class FineTunedTransfer(Transfer):
    method __init__ (line 205) | def __init__(self, transfer):
    method load_model (line 209) | def load_model(self, parents=[]):
    method __call__ (line 224) | def __call__(self, x):
  function get_transfer_name (line 287) | def get_transfer_name(transfer):

FILE: utils.py
  function both (line 34) | def both(x, y):
  function elapsed (line 39) | def elapsed(last_time=[time.time()]):
  function cycle (line 46) | def cycle(iterable):
  function average (line 52) | def average(arr):
  function get_files (line 64) | def get_files(exp, data_dirs=DATA_DIRS, recursive=False):
  function get_finetuned_model_path (line 82) | def get_finetuned_model_path(parents):
  function plot_images (line 89) | def plot_images(model, logger, test_set, dest_task="normal",
  function gaussian_filter (line 121) | def gaussian_filter(channels=3, kernel_size=5, sigma=1.0, device=0):
  function motion_blur_filter (line 140) | def motion_blur_filter(kernel_size=15):
  function sobel_kernel (line 150) | def sobel_kernel(x):
  class SobelKernel (line 164) | class SobelKernel(nn.Module):
    method __init__ (line 165) | def __init__(self):
    method forward (line 168) | def forward(self, x):
  function set_seed (line 171) | def set_seed(seed):