################################################################################
################# SOLARIS MODEL CONFIGURATION SKELETON #########################
################################################################################
# This skeleton lays out the required instructions for running a model using
# solaris. See the full documentation at [INCLUDE DOC LINK HERE] for details on
# options, required arguments, and sample usage.
model_name: # include the name of the model to be used here. See the docs
# for options.
model_path: # leave this blank unless you're using a custom model not
# native to solaris. solaris will automatically find your
# model.
train: true # set to false for inference only
infer: true # set to false for training only
pretrained: true # use pretrained weights associated with the model?
nn_framework: # if not using a model included with the package, use this
# argument to specify if it uses keras, pytorch, tf, etc.
batch_size: # size of each batch fed into nn.
data_specs:
width: # width of the input images taken in by the neural net.
height: # height of the input images taken in by the neural net.
dtype: # dtype of the inputs ingested by the neural net.
rescale: false # should image pixel values be rescaled before pre-processing?
# If so, the image will be rescaled to the pixel range defined
# by rescale_min and rescale_max below.
rescale_minima: auto # the minimum values to use in rescaling (if
# rescale=true). If 'auto', the minimum pixel intensity
# in each channel of the image will be subtracted. If
# a single value is provided, that value will be set to
# zero for each channel in the input image.
# if a list of values are provided, then minima in the
# separate channels (in that order) will be set to that
# value PRIOR to any augmentation.
rescale_maxima: auto # same as rescale_minima, but for the maximum value for
# each channel in the image.
channels: # number of channels in the input imagery.
label_type: mask # one of ['mask', 'bbox'] (CURRENTLY ONLY MASK IMPLEMENTED)
is_categorical: false # are the labels binary (default) or categorical?
mask_channels: 1 # number of channels in the training mask
val_holdout_frac: # if empty, assumes that separate data ref files define the
# training and validation dataset. If a float between 0 and
# 1, indicates the fraction of training data that's held
# out for validation (and validation_data_csv will be
# ignored)
data_workers: # number of cpu threads to use for loading and preprocessing
# input images.
# other_inputs: # this can provide a list of additional inputs to pass to the
# neural net for training. These inputs should be specified in
# extra columns of the csv files (denoted below), either as
# filepaths to additional data to load or as values to include.
# NOTE: This is not currently implemented.
training_data_csv: # path to the reference csv that defines training data.
# see the documentation for the specifications of this file.
validation_data_csv: # path to the validation ref csv. See the docs. If
# val_holdout_frac is specified (under data_specs), then
# this argument will be ignored.
inference_data_csv: # path to the reference csv that defines inference data.
# see the documentation for the specs of this file.
training_augmentation: # augmentations for use with training data
augmentations:
# include augmentations here. See the documentation for options and
# required arguments.
p: 1.0 # probability of applying the entire training augmentation pipeline.
shuffle: true # should the image order be shuffled in each epoch.
validation_augmentation: # augmentations for use with validation data
augmentations:
# include augmentations here
p: # probability of applying the full validation augmentation pipeline.
inference_augmentation: # this is optional. If not provided,
# validation_augmentation will be used instead.
training:
epochs: # number of epochs. A list can also be provided here indicating
# distinct sets of epochs at different learning rates, etc; if so,
# a list of equal length must be provided in the parameter that varies
# with the values for each set of epochs.
steps_per_epoch: # optional argument defining # steps/epoch. If not provided,
# each epoch will include the number of steps needed to go
# through the entire training dataset.
optimizer: # optimizer function name. see docs for options.
lr: # learning rate.
opt_args: # dictionary of values (e.g. alpha, gamma, momentum) specific to
# the optimizer.
loss: # loss function(s). See docs for options. This should be a list of loss
# names with sublists of loss function hyperparameters (if applicable).
# See the docs for more details and usage examples.
loss_weights: # (optional) weights to use for each loss function if using
# loss: composite. This must be a set of key:value pairs where
# defining the weight for each sub-loss within the composite.
# If using a composite and a value isn't provided here, all
# losses will be weighted equally.
metrics: # metrics to monitor on the training and validation sets.
training: # training set metrics.
validation: # validation set metrics.
checkpoint_frequency: # how frequently should checkpoints be saved?
# this can be an int, in which case every n epochs
# a checkpoint will be made, or a list, in which case
# checkpoints will be saved on those specific epochs.
# if not provided, only the final model is saved.
callbacks: # a list of callbacks to use.
model_dest_path: # path to save the trained model output and checkpoint(s)
# to. Should be a filename ending in .h5, .hdf5 for keras
# or .pth, .pt for torch. Epoch numbers will be appended
# for checkpoints.
verbose: true # verbose text output during training
inference:
window_step_size_x: # size of each step for the sliding window for inference.
# set to the same size as the input image size for zero
# overlap; to average predictions across multiple images,
# use a smaller step size.
window_step_size_y: # size of each step for the sliding window for inference.
# set to the same size as the input image size for zero
# overlap; to average predictions across multiple images,
# use a smaller step size.
stitching_method: # the method to use to stitch together tiles used during
# inference. defaults to average if not provided. see
# the documentation for sol.raster.image.stitch_images()
# for more.
output_dir: inference_out # the path to save inference outputs to.