From 801495821d62307f6466566588bad1b4ba35f219 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Fri, 1 Oct 2021 10:26:41 +0200
Subject: [PATCH] Module to train CNN using cross-validation.
---
climax/main/downscale_train_cv.py | 174 ++++++++++++++++++++++++++++++
1 file changed, 174 insertions(+)
create mode 100644 climax/main/downscale_train_cv.py
diff --git a/climax/main/downscale_train_cv.py b/climax/main/downscale_train_cv.py
new file mode 100644
index 0000000..5aaec33
--- /dev/null
+++ b/climax/main/downscale_train_cv.py
@@ -0,0 +1,174 @@
+"""Dynamical climate downscaling using deep convolutional neural networks."""
+
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# builtins
+import time
+import logging
+from datetime import timedelta
+from logging.config import dictConfig
+
+# externals
+import torch
+import xarray as xr
+from sklearn.model_selection import TimeSeriesSplit
+from torch.utils.data import DataLoader
+
+# locals
+from pysegcnn.core.utils import search_files
+from pysegcnn.core.trainer import NetworkTrainer, LogConfig
+from pysegcnn.core.models import Network
+from pysegcnn.core.logging import log_conf
+from climax.core.dataset import ERA5Dataset, NetCDFDataset
+from climax.core.loss import BernoulliGammaLoss
+from climax.main.config import (ERA5_PLEVELS, ERA5_PREDICTORS, PREDICTAND,
+ CALIB_PERIOD, DOY, SHUFFLE, BATCH_SIZE, LR,
+ LAMBDA, NORM, TRAIN_CONFIG, NET, LOSS, FILTERS,
+ OVERWRITE, DEM, DEM_FEATURES)
+from climax.main.io import ERA5_PATH, OBS_PATH, DEM_PATH, MODEL_PATH
+
+# module level logger
+LOGGER = logging.getLogger(__name__)
+
+
+if __name__ == '__main__':
+
+ # initialize timing
+ start_time = time.monotonic()
+
+ # initialize network filename
+ state_file = ERA5Dataset.state_file(
+ NET, PREDICTAND, ERA5_PREDICTORS, ERA5_PLEVELS, dem=DEM,
+ dem_features=DEM_FEATURES, doy=DOY)
+
+ # adjust statefile name for precipitation
+ if PREDICTAND == 'pr':
+ if isinstance(LOSS, BernoulliGammaLoss):
+ state_file = state_file.replace('.pt', '_{}mm_{}.pt'.format(
+ str(LOSS.min_amount).replace('.', ''),
+ repr(LOSS).strip('()')))
+ else:
+ state_file = state_file.replace('.pt', '_{}.pt'.format(
+ repr(LOSS).strip('()')))
+
+ # add suffix for training with cross-validation
+ state_file = state_file.replace('.pt', '_cv.pt')
+
+ # path to model state
+ state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
+
+ # initialize logging
+ log_file = MODEL_PATH.joinpath(PREDICTAND,
+ state_file.name.replace('.pt', '_log.txt'))
+ if log_file.exists():
+ log_file.unlink()
+ dictConfig(log_conf(log_file))
+
+ # initialize downscaling
+ LogConfig.init_log('Initializing downscaling for period: {}'.format(
+ ' - '.join([str(CALIB_PERIOD[0]), str(CALIB_PERIOD[-1])])))
+
+ # check if model exists
+ if state_file.exists() and not OVERWRITE:
+ # load pretrained network
+ net, _ = Network.load_pretrained_model(state_file, NET)
+ else:
+ # initialize ERA5 predictor dataset
+ LogConfig.init_log('Initializing ERA5 predictors.')
+ Era5 = ERA5Dataset(ERA5_PATH.joinpath('ERA5'), ERA5_PREDICTORS,
+ plevels=ERA5_PLEVELS)
+ Era5_ds = Era5.merge(chunks=-1)
+
+ # initialize OBS predictand dataset
+ LogConfig.init_log('Initializing observations for predictand: {}'
+ .format(PREDICTAND))
+
+ # check whether to joinlty train tasmin and tasmax
+ if PREDICTAND == 'tas':
+ # read both tasmax and tasmin
+ tasmax = xr.open_dataset(
+ search_files(OBS_PATH.joinpath('tasmax'), '.nc$').pop())
+ tasmin = xr.open_dataset(
+ search_files(OBS_PATH.joinpath('tasmin'), '.nc$').pop())
+ Obs_ds = xr.merge([tasmax, tasmin])
+ else:
+ # read in-situ gridded observations
+ Obs_ds = search_files(OBS_PATH.joinpath(PREDICTAND), '.nc$').pop()
+ Obs_ds = xr.open_dataset(Obs_ds)
+
+ # whether to use digital elevation model
+ if DEM:
+ # digital elevation model: Copernicus EU-Dem v1.1
+ dem = search_files(DEM_PATH, '^eu_dem_v11_stt.nc$').pop()
+
+ # read elevation and compute slope and aspect
+ dem = ERA5Dataset.dem_features(
+ dem, {'y': Era5_ds.y, 'x': Era5_ds.x},
+ add_coord={'time': Era5_ds.time})
+
+ # check whether to use slope and aspect
+ if not DEM_FEATURES:
+ dem = dem.drop_vars(['slope', 'aspect'])
+
+ # add dem to set of predictor variables
+ Era5_ds = xr.merge([Era5_ds, dem])
+
+ # initialize network and optimizer
+ LogConfig.init_log('Initializing network and optimizer.')
+
+ # define number of output fields
+ # check whether modelling pr with probabilistic approach
+ outputs = len(Obs_ds.data_vars)
+ if PREDICTAND == 'pr' and isinstance(LOSS, BernoulliGammaLoss):
+ outputs = 3
+
+ # instanciate network
+ net = NET(state_file, len(Era5_ds.data_vars), outputs, filters=FILTERS)
+
+ # initialize optimizer
+ optimizer = torch.optim.Adam(net.parameters(), lr=LR,
+ weight_decay=LAMBDA)
+
+ # initialize training data
+ LogConfig.init_log('Initializing training data.')
+
+ # split calibration period using cross-validation TimeSeriesSplit
+ cv = TimeSeriesSplit()
+ for i, (train_idx, valid_idx) in enumerate(cv.split(CALIB_PERIOD)):
+
+ # time steps for training and validation set
+ train = CALIB_PERIOD[train_idx]
+ valid = CALIB_PERIOD[valid_idx]
+ LogConfig.init_log('Fold {}/{}: {} - {}'.format(
+ i + 1, cv.n_splits, str(train[0]), str(train[-1])))
+
+ # training and validation dataset
+ Era5_train, Obs_train = (Era5_ds.sel(time=train),
+ Obs_ds.sel(time=train))
+ Era5_valid, Obs_valid = (Era5_ds.sel(time=valid),
+ Obs_ds.sel(time=valid))
+
+ # create PyTorch compliant dataset and dataloader instances for
+ # model training
+ train_ds = NetCDFDataset(Era5_train, Obs_train, normalize=NORM,
+ doy=DOY)
+ valid_ds = NetCDFDataset(Era5_valid, Obs_valid, normalize=NORM,
+ doy=DOY)
+ train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE,
+ shuffle=SHUFFLE, drop_last=False)
+ valid_dl = DataLoader(valid_ds, batch_size=BATCH_SIZE,
+ shuffle=SHUFFLE, drop_last=False)
+
+ # initialize network trainer
+ trainer = NetworkTrainer(net, optimizer, net.state_file, train_dl,
+ valid_dl, loss_function=LOSS,
+ **TRAIN_CONFIG)
+
+ # train model
+ state = trainer.train()
+
+ # log execution time of script
+ LogConfig.init_log('Execution time of script {}: {}'
+ .format(__file__, timedelta(seconds=time.monotonic() -
+ start_time)))
--
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