From e9e080f3817508640019b4c68d895bcf8842d7c5 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 23 Oct 2023 13:44:57 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 44 ++++++++++++++++---------------
1 file changed, 23 insertions(+), 21 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 89166211..f70d69d7 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -2,6 +2,7 @@ import tensorflow as tf
from util.setup import logdir, modeldir, cachepath, now, ancillary_path, home_dir
from util.util import EarlyStop, normalize, make_for_full_domain_predict, get_training_parameters
from util.geos_nav import get_navigation
+from util.augment import augment_image
import os, datetime
import numpy as np
@@ -315,26 +316,26 @@ class IcingIntensityFCN:
label = np.where(np.invert(np.logical_or(label == 0, label == 1)), 2, label)
label = label.reshape((label.shape[0], 1))
- if is_training and DO_AUGMENT:
- data_ud = np.flip(data, axis=1)
- data_alt_ud = np.copy(data_alt)
- label_ud = np.copy(label)
-
- data_lr = np.flip(data, axis=2)
- data_alt_lr = np.copy(data_alt)
- label_lr = np.copy(label)
-
- data_r1 = np.rot90(data, k=1, axes=(1, 2))
- data_alt_r1 = np.copy(data_alt)
- label_r1 = np.copy(label)
-
- data_r2 = np.rot90(data, k=1, axes=(1, 2))
- data_alt_r2 = np.copy(data_alt)
- label_r2 = np.copy(label)
-
- data = np.concatenate([data, data_ud, data_lr, data_r1, data_r2])
- data_alt = np.concatenate([data_alt, data_alt_ud, data_alt_lr, data_alt_r1, data_alt_r2])
- label = np.concatenate([label, label_ud, label_lr, label_r1, label_r2])
+ # if is_training and DO_AUGMENT:
+ # data_ud = np.flip(data, axis=1)
+ # data_alt_ud = np.copy(data_alt)
+ # label_ud = np.copy(label)
+ #
+ # data_lr = np.flip(data, axis=2)
+ # data_alt_lr = np.copy(data_alt)
+ # label_lr = np.copy(label)
+ #
+ # data_r1 = np.rot90(data, k=1, axes=(1, 2))
+ # data_alt_r1 = np.copy(data_alt)
+ # label_r1 = np.copy(label)
+ #
+ # data_r2 = np.rot90(data, k=1, axes=(1, 2))
+ # data_alt_r2 = np.copy(data_alt)
+ # label_r2 = np.copy(label)
+ #
+ # data = np.concatenate([data, data_ud, data_lr, data_r1, data_r2])
+ # data_alt = np.concatenate([data_alt, data_alt_ud, data_alt_lr, data_alt_r1, data_alt_r2])
+ # label = np.concatenate([label, label_ud, label_lr, label_r1, label_r2])
return data, data_alt, label
@@ -475,8 +476,9 @@ class IcingIntensityFCN:
dataset = dataset.batch(PROC_BATCH_SIZE)
dataset = dataset.map(self.data_function, num_parallel_calls=8)
dataset = dataset.cache()
+ dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE, reshuffle_each_iteration=True)
if DO_AUGMENT:
- dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE)
+ dataset = dataset.map(augment_image(), num_parallel_calls=8)
dataset = dataset.prefetch(buffer_size=1)
self.train_dataset = dataset
--
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