From cd2d0cc79a810356a47d66a7c808d04f22d35ea6 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Fri, 22 Oct 2021 16:20:43 +0200
Subject: [PATCH] Check if target file already exists.
---
climax/main/downscale_infer.py | 29 +++++++++++++++++------------
1 file changed, 17 insertions(+), 12 deletions(-)
diff --git a/climax/main/downscale_infer.py b/climax/main/downscale_infer.py
index 5d26894..a023e00 100644
--- a/climax/main/downscale_infer.py
+++ b/climax/main/downscale_infer.py
@@ -24,7 +24,7 @@ from climax.core.utils import split_date_range
from climax.main.config import (ERA5_PREDICTORS, ERA5_PLEVELS, PREDICTAND, NET,
VALID_PERIOD, BATCH_SIZE, NORM, DOY, NYEARS,
DEM, DEM_FEATURES, LOSS, ANOMALIES, OPTIM,
- OPTIM_PARAMS, CHUNKS, LR_SCHEDULER)
+ OPTIM_PARAMS, CHUNKS, LR_SCHEDULER, OVERWRITE)
from climax.main.io import ERA5_PATH, DEM_PATH, MODEL_PATH, TARGET_PATH
# module level logger
@@ -46,6 +46,22 @@ if __name__ == '__main__':
# path to model state
state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
+ # load pretrained model
+ if state_file.exists():
+ # load pretrained network
+ net, _ = Network.load_pretrained_model(state_file, NET)
+ else:
+ # initialize OBS predictand dataset
+ LOGGER.info('{} does not exist.'.format(state_file))
+ sys.exit()
+
+ # check if target dataset already exists
+ target = TARGET_PATH.joinpath(PREDICTAND, net.state_file.name.replace(
+ net.state_file.suffix, '.nc'))
+ if target.exists() and not OVERWRITE:
+ LogConfig.init_log('{} already exists.'.format(target))
+ sys.exit()
+
# initialize logging
log_file = MODEL_PATH.joinpath(PREDICTAND,
state_file.name.replace('.pt', '_log.txt'))
@@ -78,15 +94,6 @@ if __name__ == '__main__':
# add dem to set of predictor variables
Era5_ds = xr.merge([Era5_ds, dem]).chunk(Era5_ds.chunks)
- # load pretrained model
- if state_file.exists():
- # load pretrained network
- net, _ = Network.load_pretrained_model(state_file, NET)
- else:
- # initialize OBS predictand dataset
- LOGGER.info('{} does not exist.'.format(state_file))
- sys.exit()
-
# subset to reference period and predict in NYEAR intervals
trg_ds = []
for dates in split_date_range(VALID_PERIOD[0], VALID_PERIOD[-1],
@@ -103,8 +110,6 @@ if __name__ == '__main__':
trg_ds = xr.concat(trg_ds, dim='time')
# save model predictions as NetCDF file
- target = TARGET_PATH.joinpath(PREDICTAND, net.state_file.name.replace(
- net.state_file.suffix, '.nc'))
if not target.parent.exists():
target.parent.mkdir(parents=True, exist_ok=True)
LOGGER.info('Saving network predictions: {}.'.format(target))
--
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