update paths to /srv/satnogs

master 0.76
ml server 2020-01-26 17:27:12 -07:00
parent 777c8b6106
commit d102e6dacf
17 changed files with 33 additions and 21 deletions

2
wut
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@ -9,6 +9,8 @@
# Example:
# wut 1456893
cd /srv/satnogs
OBSID="$1"
rm -rf data/test

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@ -29,7 +29,7 @@
# XXX Should check input is sane...
APIURL="https://network.satnogs.org/api"
DOWNDIR="download"
DOWNDIR="/srv/satnogs/download"
OBSIDMIN="$1"
OBSIDMAX="$2"
OBSID=$OBSIDMIN

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@ -22,6 +22,8 @@ OBSIDMIN="$1"
OBSIDMAX="$2"
OBSID=$OBSIDMIN
cd /srv/satnogs
# Enable the following if you want to download waterfalls in this range:
#echo "Downloading Waterfalls"
#./wut-water-range $OBSIDMIN $OBSIDMAX

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@ -20,6 +20,8 @@
#
# Possible vetted_status: bad, failed, good, null, unknown.
cd /srv/satnogs
OBSTX="$1"
OBSIDMIN="$2"
OBSIDMAX="$3"

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@ -23,6 +23,8 @@
#
# Possible vetted_status: bad, failed, good, null, unknown.
cd /srv/satnogs
OBSENC="$1"
OBSIDMIN="$2"
OBSIDMAX="$3"

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@ -8,6 +8,8 @@
# Example:
# wut-files
cd /srv/satnogs
echo
DF=`df -h download/`
echo "$DF"

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@ -8,6 +8,8 @@
# Example:
# wut-files
cd /srv/satnogs
TRAIN=`find data/train -type f | wc -l`
echo
echo "Training Files: $TRAIN"

6
wut-ml
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@ -68,9 +68,9 @@ datagen = ImageDataGenerator(
dtype='float32')
print("datagen.flow")
train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
train_it = datagen.flow_from_directory('/srv/satnogs/data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('/srv/satnogs/data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('/srv/satnogs/data/test/', class_mode='binary')
print("train_it.next()")
trainX, trainY = train_it.next()

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@ -68,9 +68,9 @@ datagen = ImageDataGenerator(
dtype='float32')
print("datagen.flow")
train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
train_it = datagen.flow_from_directory('/srv/satnogs/data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('/srv/satnogs/data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('/srv/satnogs/data/test/', class_mode='binary')

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@ -30,7 +30,7 @@ from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.preprocessing.image import load_img
from tensorflow.python.keras.preprocessing.image import img_to_array
model = load_model('data/wut.h5')
model = load_model('/srv/satnogs/data/wut.h5')
img_width=256
img_height=256
model = Sequential()

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@ -30,9 +30,9 @@ from tensorflow.python.keras.preprocessing.image import load_img
from tensorflow.python.keras.preprocessing.image import img_to_array
datagen = ImageDataGenerator()
train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
train_it = datagen.flow_from_directory('/srv/satnogs/data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('/srv/satnogs/data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('/srv/satnogs/data/test/', class_mode='binary')
batchX, batchy = train_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))
img_width=256

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@ -7,7 +7,7 @@
# Download Observation: JSON. Not waterfall, audio, or data files.
APIURL="https://network.satnogs.org/api"
DOWNDIR="download"
DOWNDIR="/srv/satnogs/download"
cd $DOWNDIR || exit

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@ -1,7 +1,7 @@
#!/bin/bash
# wut-review-staging
# Go through all the images in data/staging and review them.
cd data/staging || exit
cd /srv/satnogs/data/staging || exit
for i in *.png
do echo $i
rm ../test/unvetted/*.png

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@ -22,7 +22,7 @@ KEEP=100
# this is so bad no one should ever run it again
#exit 0
# XXX Delete data in this directory! XXX
cd data/test/unvetted/ || exit
cd /srv/satnogs/data/test/unvetted/ || exit
TOTALFILES=`ls waterfall_*.png | wc -l`
for wf in waterfall_*.png

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@ -7,7 +7,7 @@
# Download Observation: JSON and waterfall. Not audio or data files.
APIURL="https://network.satnogs.org/api"
DOWNDIR="download"
DOWNDIR="/srv/satnogs/download"
cd $DOWNDIR || exit

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@ -21,7 +21,7 @@
# XXX Should check input is sane...
APIURL="https://network.satnogs.org/api"
DOWNDIR="download"
DOWNDIR="/srv/satnogs/download"
OBSIDMIN="$1"
OBSIDMAX="$2"
OBSID=$OBSIDMIN

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@ -52,8 +52,8 @@ NUM_WORKERS = 6
GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS
# XXX
POSITIVE_DIRECTORY = '/home/jebba/devel/spacecruft/satnogs-wut/data/pos'
pos_dir = '/home/jebba/devel/spacecruft/satnogs-wut/data/posdir'
POSITIVE_DIRECTORY = '/srv/satnogs/data/pos'
pos_dir = '/srv/satnogs/data/posdir'
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
tf.distribute.experimental.CollectiveCommunication.RING)
@ -92,7 +92,7 @@ def process_image(image_bytes, label):
AUTOTUNE = tf.data.experimental.AUTOTUNE
NUM_TOTAL_IMAGES=100
data_root = "/home/jebba/devel/spacecruft/satnogs-wut/data"
data_root = "/srv/satnogs/data"
profile_dir = os.path.join(data_root, "profiles")
dataset = tf.data.Dataset.list_files(data_root)
dataset = dataset.shuffle(NUM_TOTAL_IMAGES)
@ -174,8 +174,8 @@ def handle_batching():
yield concat(batch)
batch.reset()
train_dir = os.path.join('data/', 'train')
val_dir = os.path.join('data/', 'val')
train_dir = os.path.join('/srv/satnogs/data/', 'train')
val_dir = os.path.join('/srv/satnogs/data/', 'val')
train_good_dir = os.path.join(train_dir, 'good')
train_bad_dir = os.path.join(train_dir, 'bad')
val_good_dir = os.path.join(val_dir, 'good')