config

Module Contents

config.str2num(s)

Converts parameter strings to appropriate types.

Parameters

s (str) – Parameter value

Returns

Appropriately converted value

Return type

Varying

config.get_hparams(init_features: ‘Architecture complexity [int]’, ‘option’ = 16, in_days: ‘Number of input days [int]’, ‘option’ = 2, out_days: ‘Number of output days [int]’, ‘option’ = 1, epochs: ‘Number of training epochs [int]’, ‘option’ = 100, learning_rate: ‘Maximum learning rate [float]’, ‘option’ = 0.001, batch_size: ‘Batch size of the input [int]’, ‘option’ = 1, split: ‘Test split fraction [float]’, ‘option’ = 0.2, use_16bit: ‘Use 16-bit precision for training (train only)’, ‘option’ = True, gpus: ‘Number of GPUs to use [int]’, ‘option’ = 1, optim: ‘Learning rate optimizer: one_cycle or cosine (train only) [str]’, ‘option’ = 'one_cycle', dry_run: ‘Use small amount of data for sanity check [Bool]’, ‘option’ = False, find_lr: ‘Automatically search for an ideal learning rate [Bool]’, ‘option’ = False, search_bs: ‘Scale the batch dynamically for full GPU usage [Bool]’, ‘option’ = False, case_study: ‘The case-study region to use for inference: australia, california, portugal, siberia, chile, uk [Bool/str]’, ‘option’ = False, clip_output: ‘Limit the inference to the datapoints within supplied range (e.g. 0.5,60) [Bool/list]’, ‘option’ = False, boxcox: ‘Apply boxcox transformation with specified lambda while training and the inverse boxcox transformation during the inference. [Bool/float]’, ‘option’ = 0.1182, binned: ‘Show the extended metrics for supplied comma separated binned FWI value range (e.g. 0,15,70) [Bool/list]’, ‘option’ = '0,5.2,11.2,21.3,38.0,50', round_to_zero: ‘Round off the target values below the specified threshold to zero [Bool/float]’, ‘option’ = False, isolate_frp: ‘Exclude the isolated datapoints with FRP > 0 [Bool]’, ‘option’ = False, date_range: ‘Filter the data with specified date range in YYYY-MM-DD format. E.g. 2019-04-01,2019-05-01 [Bool/str]’, ‘option’ = False, cb_loss: ‘Use Class-Balanced loss with the supplied beta parameter [Bool/float]’, ‘option’ = False, chronological_split: ‘Do chronological train-test split in the specified ratio [Bool/float]’, ‘option’ = False, undersample: ‘Undersample the datapoints with smaller than specified FWI [Bool/float]’, ‘option’ = False, model: ‘Model to use: unet, unet_downsampled, unet_snipped, unet_tapered, unet_interpolated [str]’, ‘option’ = 'unet_tapered', out: ‘Output data for training: fwi_reanalysis or gfas_frp [str]’, ‘option’ = 'fwi_reanalysis', benchmark: ‘Benchmark the FWI-Forecast data against FWI-Reanalysis [Bool]’, ‘option’ = False, smos_input: ‘Use soil-moisture input data [Bool]’, ‘option’ = 'False', forecast_dir: ‘Directory containing the forecast data. Alternatively set $FORECAST_DIR [str]’, ‘option’ = os.environ.get('FORECAST_DIR'), forcings_dir: ‘Directory containing the forcings data Alternatively set $FORCINGS_DIR [str]’, ‘option’ = os.environ.get('FORCINGS_DIR'), smos_dir: ‘Directory containing the soil-moisture data Alternatively set $SMOS_DIR [str]’, ‘option’ = os.environ.get('SMOS_DIR'), reanalysis_dir: ‘Directory containing the reanalysis data. Alternatively set $REANALYSIS_DIR. [str]’, ‘option’ = os.environ.get('REANALYSIS_DIR'), frp_dir: ‘Directory containing the FRP data. Alternatively set $FRP_DIR. [str]’, ‘option’ = os.environ.get('FRP_DIR'), mask: ‘File containing the mask stored as the numpy array [str]’, ‘option’ = 'src/dataloader/mask/reanalysis_mask.npy', comment: ‘Used for logging [str]’, ‘option’ = False, checkpoint_file: ‘Path to the test model checkpoint [Bool/str]’, ‘option’ = False)

Process and print the dictionary of project wide arguments.

Returns

Dictionary containing configuration options.

Return type

dict

config.create_config()

Generates config file in yaml format from parsed arguments and saves to src/config/ dir.

Returns

hparams in Namespace

Return type

Namespace