From f73a7bc148e21d8b9cb558b45e2b4545b4fc1070 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 20 Apr 2023 13:03:48 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_fraction_fcn_viirs.py | 14 +++++++++-----
1 file changed, 9 insertions(+), 5 deletions(-)
diff --git a/modules/deeplearning/cloud_fraction_fcn_viirs.py b/modules/deeplearning/cloud_fraction_fcn_viirs.py
index 28f76db1..3417af38 100644
--- a/modules/deeplearning/cloud_fraction_fcn_viirs.py
+++ b/modules/deeplearning/cloud_fraction_fcn_viirs.py
@@ -278,6 +278,7 @@ class SRCNN:
self.test_labels = []
self.test_preds = []
self.test_probs = None
+ self.test_input = []
self.learningRateSchedule = None
self.num_data_samples = None
@@ -342,7 +343,7 @@ class SRCNN:
data_norm.append(lo[:, slc_y, slc_x])
data_norm.append(hi[:, slc_y, slc_x])
- data_norm.append(avg[:, slc_y, slc_x])
+ data_norm.append(std[:, slc_y, slc_x])
# ---------------------------------------------------
# If next uncommented, take out get_grid_cell_mean
# tmp = input_data[:, label_idx, :, :]
@@ -580,6 +581,7 @@ class SRCNN:
self.test_labels.append(labels)
self.test_preds.append(pred.numpy())
+ self.test_input.append(inputs[:, :, :, 4])
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
@@ -722,9 +724,10 @@ class SRCNN:
labels = np.concatenate(self.test_labels)
preds = np.concatenate(self.test_preds)
+ inputs = np.concatenate(self.test_input)
print(labels.shape, preds.shape)
- return labels, preds
+ return labels, preds, inputs
def do_evaluate(self, inputs, ckpt_dir):
@@ -775,10 +778,10 @@ class SRCNN:
def run_restore_static(directory, ckpt_dir, out_file=None):
nn = SRCNN()
- labels, preds = nn.run_restore(directory, ckpt_dir)
+ labels, preds, inputs = nn.run_restore(directory, ckpt_dir)
if out_file is not None:
np.save(out_file,
- [np.squeeze(labels), preds.argmax(axis=3)])
+ [np.squeeze(labels), preds.argmax(axis=3), np.squeeze(inputs)])
def run_evaluate_static(in_file, out_file, ckpt_dir):
@@ -799,7 +802,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
refl_lo, refl_hi, refl_std, refl_avg = get_min_max_std(refl)
refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
refl_hi = normalize(refl_hi, 'refl_0_65um_nom', mean_std_dct)
- refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
+ #refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
+ refl_avg = refl_std
refl_lo = np.squeeze(refl_lo)
refl_hi = np.squeeze(refl_hi)
refl_avg = np.squeeze(refl_avg)
--
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