From fe8da3e19533a1bf2174f3b03da2b3f6ed771ac8 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 24 Oct 2023 12:52:39 -0500
Subject: [PATCH] snapshot...
---
modules/icing/util.py | 35 +++++++++++++++++++++++++++++++++--
1 file changed, 33 insertions(+), 2 deletions(-)
diff --git a/modules/icing/util.py b/modules/icing/util.py
index b946ff48..90991b97 100644
--- a/modules/icing/util.py
+++ b/modules/icing/util.py
@@ -1,6 +1,7 @@
import numpy as np
import deeplearning.icing_fcn as icing_fcn
import deeplearning.icing_cnn as icing_cnn
+from deeplearning.icing_fcn import IcingIntensityFCN
from icing.pirep_goes import setup, time_filter_3
from icing.moon_phase import moon_phase
from util.util import get_time_tuple_utc, is_day, check_oblique, get_median, homedir, write_icing_file_nc4,\
@@ -12,6 +13,8 @@ from util.setup import model_path_day, model_path_night
from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, CLAVRx_H08, CLAVRx_H09
import h5py
import datetime
+import tensorflow as tf
+import os
# from scipy.signal import medfilt2d
@@ -353,6 +356,34 @@ def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output
elif model_type == 'FCN':
model_module = icing_fcn
+ if day_model_path is not None:
+ ckpt_dir_s = os.listdir(day_model_path)
+ ckpt_dir = day_model_path + ckpt_dir_s[0]
+
+ day_model = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_andor_l2, satellite=satellite, use_flight_altitude=use_flight_altitude)
+ day_model.num_data_samples = 10000
+ day_model.build_model()
+ day_model.build_training()
+ day_model.build_evaluation()
+
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=day_model.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+ ckpt.restore(ckpt_manager.latest_checkpoint)
+
+ if night_model_path is not None:
+ ckpt_dir_s = os.listdir(night_model_path)
+ ckpt_dir = night_model_path + ckpt_dir_s[0]
+
+ night_model = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_andor_l2, satellite=satellite, use_flight_altitude=use_flight_altitude)
+ night_model.num_data_samples = 10000
+ night_model.build_model()
+ night_model.build_training()
+ night_model.build_evaluation()
+
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=night_model.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+ ckpt.restore(ckpt_manager.latest_checkpoint)
+
alt_lo, alt_hi = 0.0, 15000.0
if use_flight_altitude is True:
flight_levels = flight_levels
@@ -440,7 +471,7 @@ def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output
for ds_name in day_train_params:
day_grd_dct[ds_name] = np.stack(day_data_dct[ds_name])
- preds_day_dct, probs_day_dct = model_module.run_evaluate_static(day_grd_dct, num_day_tiles, day_model_path,
+ preds_day_dct, probs_day_dct = model_module.run_evaluate_static_2(day_model, day_grd_dct, num_day_tiles,
day_night='DAY', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
@@ -465,7 +496,7 @@ def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output
for ds_name in nght_train_params:
nght_grd_dct[ds_name] = np.stack(nght_data_dct[ds_name])
- preds_nght_dct, probs_nght_dct = model_module.run_evaluate_static(nght_grd_dct, num_nght_tiles, night_model_path,
+ preds_nght_dct, probs_nght_dct = model_module.run_evaluate_static_2(night_model, nght_grd_dct, num_nght_tiles,
day_night='NIGHT', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
--
GitLab