SYMBOL INDEX (140 symbols across 19 files)

FILE: calc_metrics_test_set.py
  function parse_args (line 19) | def parse_args():
  function convert_tensors_to_python (line 38) | def convert_tensors_to_python(obj):
  function save_metrics (line 51) | def save_metrics(results, file_path, file_type="json"):
  function run_experiments (line 75) | def run_experiments(model_folder, data_file, threshold=0.5, denormalize=...
  function plot_metrics (line 124) | def plot_metrics(results, save_path=""):
  function load_metrics_from_file (line 164) | def load_metrics_from_file(file_path, file_type):
  function main (line 202) | def main():

FILE: create_datasets.py
  function create_dataset (line 8) | def create_dataset(input_length: int, image_ahead: int, rain_amount_thre...

FILE: metric/confusionmatrix.py
  class ConfusionMatrix (line 6) | class ConfusionMatrix(metric.Metric):
    method __init__ (line 16) | def __init__(self, num_classes, normalized=False):
    method reset (line 24) | def reset(self):
    method add (line 27) | def add(self, predicted, target):
    method value (line 73) | def value(self):

FILE: metric/iou.py
  class IoU (line 7) | class IoU(metric.Metric):
    method __init__ (line 22) | def __init__(self, num_classes, normalized=False, ignore_index=None):
    method reset (line 36) | def reset(self):
    method add (line 39) | def add(self, predicted, target):
    method value (line 64) | def value(self):

FILE: metric/metric.py
  class Metric (line 4) | class Metric(Protocol):
    method reset (line 7) | def reset(self):
    method add (line 10) | def add(self, predicted, target):
    method value (line 13) | def value(self):

FILE: metric/precipitation_metrics.py
  class PrecipitationMetrics (line 6) | class PrecipitationMetrics(Metric):
    method __init__ (line 12) | def __init__(self, threshold=0.5, denormalize=True, dist_sync_on_step=...
    method update (line 37) | def update(self, preds, target):
    method compute (line 97) | def compute(self):

FILE: models/SmaAt_UNet.py
  class SmaAt_UNet (line 7) | class SmaAt_UNet(nn.Module):
    method __init__ (line 8) | def __init__(
    method forward (line 41) | def forward(self, x):

FILE: models/layers.py
  class DepthToSpace (line 7) | class DepthToSpace(nn.Module):
    method __init__ (line 8) | def __init__(self, block_size):
    method forward (line 12) | def forward(self, x):
  class SpaceToDepth (line 20) | class SpaceToDepth(nn.Module):
    method __init__ (line 22) | def __init__(self, block_size):
    method forward (line 26) | def forward(self, x):
  class DepthwiseSeparableConv (line 34) | class DepthwiseSeparableConv(nn.Module):
    method __init__ (line 35) | def __init__(self, in_channels, output_channels, kernel_size, padding=...
    method forward (line 47) | def forward(self, x):
  class DoubleDense (line 53) | class DoubleDense(nn.Module):
    method __init__ (line 54) | def __init__(self, in_channels, hidden_neurons, output_channels):
    method forward (line 60) | def forward(self, x):
  class DoubleDSConv (line 67) | class DoubleDSConv(nn.Module):
    method __init__ (line 70) | def __init__(self, in_channels, out_channels):
    method forward (line 81) | def forward(self, x):
  class Flatten (line 85) | class Flatten(nn.Module):
    method forward (line 86) | def forward(self, x):
  class ChannelAttention (line 90) | class ChannelAttention(nn.Module):
    method __init__ (line 91) | def __init__(self, input_channels, reduction_ratio=16):
    method forward (line 105) | def forward(self, x):
  class SpatialAttention (line 114) | class SpatialAttention(nn.Module):
    method __init__ (line 115) | def __init__(self, kernel_size=7):
    method forward (line 122) | def forward(self, x):
  class CBAM (line 132) | class CBAM(nn.Module):
    method __init__ (line 133) | def __init__(self, input_channels, reduction_ratio=16, kernel_size=7):
    method forward (line 138) | def forward(self, x):

FILE: models/regression_lightning.py
  class UNetBase (line 12) | class UNetBase(pl.LightningModule):
    method add_model_specific_args (line 14) | def add_model_specific_args(parent_parser):
    method __init__ (line 31) | def __init__(self, hparams):
    method forward (line 44) | def forward(self, x):
    method configure_optimizers (line 47) | def configure_optimizers(self):
    method loss_func (line 57) | def loss_func(self, y_pred, y_true):
    method training_step (line 67) | def training_step(self, batch, batch_idx):
    method validation_step (line 79) | def validation_step(self, batch, batch_idx):
    method test_step (line 88) | def test_step(self, batch, batch_idx):
    method on_test_epoch_end (line 96) | def on_test_epoch_end(self):
    method on_train_epoch_end (line 106) | def on_train_epoch_end(self):
    method on_validation_epoch_end (line 113) | def on_validation_epoch_end(self):
  class PrecipRegressionBase (line 121) | class PrecipRegressionBase(UNetBase):
    method add_model_specific_args (line 123) | def add_model_specific_args(parent_parser):
    method __init__ (line 134) | def __init__(self, hparams):
    method prepare_data (line 141) | def prepare_data(self):
    method train_dataloader (line 177) | def train_dataloader(self):
    method val_dataloader (line 189) | def val_dataloader(self):
  class PersistenceModel (line 202) | class PersistenceModel(UNetBase):
    method forward (line 203) | def forward(self, x):

FILE: models/unet_parts.py
  class DoubleConv (line 8) | class DoubleConv(nn.Module):
    method __init__ (line 11) | def __init__(self, in_channels, out_channels, mid_channels=None):
    method forward (line 24) | def forward(self, x):
  class Down (line 28) | class Down(nn.Module):
    method __init__ (line 31) | def __init__(self, in_channels, out_channels):
    method forward (line 35) | def forward(self, x):
  class Up (line 39) | class Up(nn.Module):
    method __init__ (line 42) | def __init__(self, in_channels, out_channels, bilinear=True):
    method forward (line 53) | def forward(self, x1, x2):
  class OutConv (line 67) | class OutConv(nn.Module):
    method __init__ (line 68) | def __init__(self, in_channels, out_channels):
    method forward (line 72) | def forward(self, x):

FILE: models/unet_parts_depthwise_separable.py
  class DoubleConvDS (line 10) | class DoubleConvDS(nn.Module):
    method __init__ (line 13) | def __init__(self, in_channels, out_channels, mid_channels=None, kerne...
    method forward (line 38) | def forward(self, x):
  class DownDS (line 42) | class DownDS(nn.Module):
    method __init__ (line 45) | def __init__(self, in_channels, out_channels, kernels_per_layer=1):
    method forward (line 52) | def forward(self, x):
  class UpDS (line 56) | class UpDS(nn.Module):
    method __init__ (line 59) | def __init__(self, in_channels, out_channels, bilinear=True, kernels_p...
    method forward (line 75) | def forward(self, x1, x2):
  class OutConv (line 89) | class OutConv(nn.Module):
    method __init__ (line 90) | def __init__(self, in_channels, out_channels):
    method forward (line 94) | def forward(self, x):

FILE: models/unet_precip_regression_lightning.py
  class UNet (line 7) | class UNet(PrecipRegressionBase):
    method __init__ (line 8) | def __init__(self, hparams):
    method forward (line 27) | def forward(self, x):
  class UNetAttention (line 41) | class UNetAttention(PrecipRegressionBase):
    method __init__ (line 42) | def __init__(self, hparams):
    method forward (line 67) | def forward(self, x):
  class UNetDS (line 86) | class UNetDS(PrecipRegressionBase):
    method __init__ (line 87) | def __init__(self, hparams):
    method forward (line 107) | def forward(self, x):
  class UNetDSAttention (line 121) | class UNetDSAttention(PrecipRegressionBase):
    method __init__ (line 122) | def __init__(self, hparams):
    method forward (line 148) | def forward(self, x):
  class UNetDSAttention4CBAMs (line 167) | class UNetDSAttention4CBAMs(PrecipRegressionBase):
    method __init__ (line 168) | def __init__(self, hparams):
    method forward (line 193) | def forward(self, x):

FILE: train_SmaAtUNet.py
  function get_lr (line 18) | def get_lr(optimizer):
  function fit (line 23) | def fit(

FILE: train_precip_lightning.py
  function train_regression (line 15) | def train_regression(hparams, find_batch_size_automatically: bool = False):

FILE: utils/data_loader_precip.py
  function get_train_valid_loader (line 10) | def get_train_valid_loader(
  function get_test_loader (line 141) | def get_test_loader(

FILE: utils/dataset_VOC.py
  function get_pascal_labels (line 12) | def get_pascal_labels():
  function decode_segmap (line 44) | def decode_segmap(label_mask, plot=False):
  class VOCSegmentation (line 73) | class VOCSegmentation(Dataset):
    method __init__ (line 98) | def __init__(self, root: Path, image_set="train", transformations=None...
    method __getitem__ (line 119) | def __getitem__(self, index):
    method __len__ (line 147) | def __len__(self):
    method apply_augmentations (line 150) | def apply_augmentations(self, img, target):

FILE: utils/dataset_precip.py
  class precipitation_maps_h5 (line 6) | class precipitation_maps_h5(Dataset):
    method __init__ (line 7) | def __init__(self, in_file, num_input_images, num_output_images, train...
    method __getitem__ (line 24) | def __getitem__(self, index):
    method __len__ (line 44) | def __len__(self):
  class precipitation_maps_oversampled_h5 (line 48) | class precipitation_maps_oversampled_h5(Dataset):
    method __init__ (line 49) | def __init__(self, in_file, num_input_images, num_output_images, train...
    method __getitem__ (line 63) | def __getitem__(self, index):
    method __len__ (line 79) | def __len__(self):
  class precipitation_maps_classification_h5 (line 83) | class precipitation_maps_classification_h5(Dataset):
    method __init__ (line 84) | def __init__(self, in_file, num_input_images, img_to_predict, train=Tr...
    method __getitem__ (line 102) | def __getitem__(self, index):
    method __len__ (line 126) | def __len__(self):

FILE: utils/formatting.py
  function make_metrics_str (line 5) | def make_metrics_str(metrics_dict):

FILE: utils/model_classes.py
  function get_model_class (line 5) | def get_model_class(model_file) -> tuple[type[pl.LightningModule], str]: