# one of the 'TMAX', 'TMIN', 'TMEAN', 'PCT'
parser.add_argument('v', help='Downscaling variable name')
# one of the 'annual', 'summer', 'winter', 'djf', 'mam', 'jja', 'son'
parser.add_argument('s', help='Training seasons')
# a number of 1, 2, ..., 5
# but 3 is expected, the rest are trial-and-error options
parser.add_argument('c1', help='Number of input channels (1<c<5)')
# 1 is expected, other numbers are reserved for UNET-AE
parser.add_argument('c2', help='Number of output channels (1<c<3)')
args = vars(parser.parse_args())
# parser handling
VAR, seasons, input_flag, output_flag = tu.parser_handler(args)
N_input = int(np.sum(input_flag))
N_output = int(np.sum(output_flag))
if N_output > 1:
raise ValueError('Nest-Net accepts only one target')
# ---------------------------------------------------------- #
# number of filters based on the downscaling variable
if VAR == 'PCT':
print('PCT hidden layer setup')
N = [64, 96, 128, 160]
else:
print('T2 hidden layer setup')
N = [56, 112, 224, 448]
sys.path.insert(0, '/glade/u/home/ksha/WORKSPACE/DL_downscaling/utils/')
sys.path.insert(0, '/glade/u/home/ksha/WORKSPACE/DL_downscaling/')
from namelist import *
import model_utils as mu
import train_utils as tu
# parse user inputs
parser = argparse.ArgumentParser()
# positionals
parser.add_argument('v', help='Downscaling variable name')
parser.add_argument('s', help='Training seasons summer/winter')
parser.add_argument('c1', help='Number of input channels (1<c<5)')
parser.add_argument('c2', help='Number of output channels (1<c<3)')
args = vars(parser.parse_args())
# parser handling
VAR, seasons, input_flag, output_flag = tu.parser_handler(
args) # prefer seasons = 'annual'
N_input = int(np.sum(input_flag)) # number of input channels
N_output = int(np.sum(output_flag)) # number of (unsupervised) output channels
if N_output > 1:
raise ValueError('UNet accepts only one target')
# # hidden layer numbers (symetrical)
if VAR == 'PCT':
print('PCT hidden layer setup')
N = [64, 96, 128, 160]
else:
print('T2 hidden layer setup')
N = [48, 96, 192, 384]
activation = 'relu' # leakyReLU instead of ReLU
pool = False # stride convolution instead of maxpooling
