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Tom Rink
python
Commits
8d0e7183
Commit
8d0e7183
authored
1 year ago
by
tomrink
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1 changed file
modules/deeplearning/cloud_fraction_fcn_abi.py
+39
-28
39 additions, 28 deletions
modules/deeplearning/cloud_fraction_fcn_abi.py
with
39 additions
and
28 deletions
modules/deeplearning/cloud_fraction_fcn_abi.py
+
39
−
28
View file @
8d0e7183
import
tensorflow
as
tf
import
tensorflow
as
tf
from
util.plot_cm
import
confusion_matrix_values
from
util.plot_cm
import
confusion_matrix_values
from
util.augment
import
augment_image
from
util.setup_cloud_fraction
import
logdir
,
modeldir
,
now
,
ancillary_path
from
util.setup_cloud_fraction
import
logdir
,
modeldir
,
now
,
ancillary_path
from
util.util
import
EarlyStop
,
normalize
,
denormalize
,
get_grid_values_all
from
util.util
import
EarlyStop
,
normalize
,
denormalize
,
get_grid_values_all
,
make_tf_callable_generator
import
glob
import
glob
import
os
,
datetime
import
os
,
datetime
import
numpy
as
np
import
numpy
as
np
...
@@ -39,6 +40,9 @@ DO_SMOOTH = False
...
@@ -39,6 +40,9 @@ DO_SMOOTH = False
SIGMA
=
1.0
SIGMA
=
1.0
DO_ZERO_OUT
=
False
DO_ZERO_OUT
=
False
# CACHE_FILE = '/scratch/long/rink/cld_opd_abi_128x128_cache'
CACHE_FILE
=
''
# setup scaling parameters dictionary
# setup scaling parameters dictionary
mean_std_dct
=
{}
mean_std_dct
=
{}
mean_std_file
=
ancillary_path
+
'
mean_std_lo_hi_l2.pkl
'
mean_std_file
=
ancillary_path
+
'
mean_std_lo_hi_l2.pkl
'
...
@@ -164,11 +168,11 @@ def get_label_data_5cat(grd_k):
...
@@ -164,11 +168,11 @@ def get_label_data_5cat(grd_k):
grd_k
[:,
0
::
4
,
2
::
4
]
+
grd_k
[:,
1
::
4
,
2
::
4
]
+
grd_k
[:,
2
::
4
,
2
::
4
]
+
grd_k
[:,
3
::
4
,
2
::
4
]
+
\
grd_k
[:,
0
::
4
,
2
::
4
]
+
grd_k
[:,
1
::
4
,
2
::
4
]
+
grd_k
[:,
2
::
4
,
2
::
4
]
+
grd_k
[:,
3
::
4
,
2
::
4
]
+
\
grd_k
[:,
0
::
4
,
3
::
4
]
+
grd_k
[:,
1
::
4
,
3
::
4
]
+
grd_k
[:,
2
::
4
,
3
::
4
]
+
grd_k
[:,
3
::
4
,
3
::
4
]
grd_k
[:,
0
::
4
,
3
::
4
]
+
grd_k
[:,
1
::
4
,
3
::
4
]
+
grd_k
[:,
2
::
4
,
3
::
4
]
+
grd_k
[:,
3
::
4
,
3
::
4
]
cat_0
=
np
.
logical_and
(
s
>=
0
,
s
<
2
)
cat_0
=
np
.
logical_and
(
s
>=
0
,
s
<
1
)
cat_1
=
np
.
logical_and
(
s
>=
2
,
s
<
6
)
cat_1
=
np
.
logical_and
(
s
>=
1
,
s
<
6
)
cat_2
=
np
.
logical_and
(
s
>=
6
,
s
<
11
)
cat_2
=
np
.
logical_and
(
s
>=
6
,
s
<
11
)
cat_3
=
np
.
logical_and
(
s
>=
11
,
s
<
15
)
cat_3
=
np
.
logical_and
(
s
>=
11
,
s
<
=
15
)
cat_4
=
np
.
logical_and
(
s
>
=
15
,
s
<=
16
)
cat_4
=
np
.
logical_and
(
s
>
15
,
s
<=
16
)
s
[
cat_0
]
=
0
s
[
cat_0
]
=
0
s
[
cat_1
]
=
1
s
[
cat_1
]
=
1
...
@@ -381,24 +385,37 @@ class SRCNN:
...
@@ -381,24 +385,37 @@ class SRCNN:
out
=
tf
.
numpy_function
(
self
.
get_in_mem_data_batch_test
,
[
indexes
],
[
tf
.
float32
,
tf
.
float32
])
out
=
tf
.
numpy_function
(
self
.
get_in_mem_data_batch_test
,
[
indexes
],
[
tf
.
float32
,
tf
.
float32
])
return
out
return
out
def
get_train_dataset
(
self
,
indexes
):
def
get_train_dataset
(
self
,
num_files
):
indexes
=
list
(
indexes
)
def
integer_gen
(
limit
):
n
=
0
while
n
<
limit
:
yield
n
n
+=
1
num_gen
=
integer_gen
(
num_files
)
gen
=
make_tf_callable_generator
(
num_gen
)
dataset
=
tf
.
data
.
Dataset
.
from_
t
en
sor_slices
(
indexes
)
dataset
=
tf
.
data
.
Dataset
.
from_
g
en
erator
(
gen
,
output_types
=
tf
.
int32
)
dataset
=
dataset
.
batch
(
PROC_BATCH_SIZE
)
dataset
=
dataset
.
batch
(
PROC_BATCH_SIZE
)
dataset
=
dataset
.
map
(
self
.
data_function
,
num_parallel_calls
=
AUTOTUNE
)
dataset
=
dataset
.
map
(
self
.
data_function
,
num_parallel_calls
=
8
)
dataset
=
dataset
.
cache
()
dataset
=
dataset
.
cache
(
filename
=
CACHE_FILE
)
dataset
=
dataset
.
shuffle
(
PROC_BATCH_BUFFER_SIZE
,
reshuffle_each_iteration
=
True
)
if
DO_AUGMENT
:
if
DO_AUGMENT
:
dataset
=
dataset
.
shuffle
(
PROC_BATCH_BUFFER_SIZE
)
dataset
=
dataset
.
map
(
augment_image
(),
num_parallel_calls
=
8
)
dataset
=
dataset
.
prefetch
(
buffer_size
=
AUTOTUNE
)
dataset
=
dataset
.
prefetch
(
buffer_size
=
1
)
self
.
train_dataset
=
dataset
self
.
train_dataset
=
dataset
def
get_test_dataset
(
self
,
indexes
):
def
get_test_dataset
(
self
,
num_files
):
indexes
=
list
(
indexes
)
def
integer_gen
(
limit
):
n
=
0
while
n
<
limit
:
yield
n
n
+=
1
num_gen
=
integer_gen
(
num_files
)
gen
=
make_tf_callable_generator
(
num_gen
)
dataset
=
tf
.
data
.
Dataset
.
from_
t
en
sor_slices
(
indexes
)
dataset
=
tf
.
data
.
Dataset
.
from_
g
en
erator
(
gen
,
output_types
=
tf
.
int32
)
dataset
=
dataset
.
batch
(
PROC_BATCH_SIZE
)
dataset
=
dataset
.
batch
(
PROC_BATCH_SIZE
)
dataset
=
dataset
.
map
(
self
.
data_function_test
,
num_parallel_calls
=
AUTOTUNE
)
dataset
=
dataset
.
map
(
self
.
data_function_test
,
num_parallel_calls
=
8
)
dataset
=
dataset
.
cache
()
dataset
=
dataset
.
cache
()
self
.
test_dataset
=
dataset
self
.
test_dataset
=
dataset
...
@@ -408,29 +425,23 @@ class SRCNN:
...
@@ -408,29 +425,23 @@ class SRCNN:
self
.
test_data_files
=
test_data_files
self
.
test_data_files
=
test_data_files
self
.
test_label_files
=
test_label_files
self
.
test_label_files
=
test_label_files
trn_idxs
=
np
.
arange
(
len
(
train_data_files
))
self
.
get_train_dataset
(
len
(
train_data_files
))
np
.
random
.
shuffle
(
trn_idxs
)
self
.
get_test_dataset
(
len
(
test_data_files
))
tst_idxs
=
np
.
arange
(
len
(
test_data_files
))
self
.
get_train_dataset
(
trn_idxs
)
self
.
get_test_dataset
(
tst_idxs
)
self
.
num_data_samples
=
num_train_samples
# approximately
self
.
num_data_samples
=
num_train_samples
# approximately
print
(
'
datetime:
'
,
now
)
print
(
'
datetime:
'
,
now
)
print
(
'
training and test data:
'
)
print
(
'
training and test data:
'
)
print
(
'
---------------------------
'
)
print
(
'
---------------------------
'
)
print
(
'
num train
samp
les:
'
,
self
.
num
_data_
samp
les
)
print
(
'
num train
fi
les:
'
,
len
(
train
_data_
fi
les
)
)
print
(
'
BATCH SIZE:
'
,
BATCH_SIZE
)
print
(
'
BATCH SIZE:
'
,
BATCH_SIZE
)
print
(
'
num test
samp
les:
'
,
tst_idxs
.
shape
[
0
]
)
print
(
'
num test
fi
les:
'
,
len
(
test_data_files
)
)
print
(
'
setup_pipeline: Done
'
)
print
(
'
setup_pipeline: Done
'
)
def
setup_test_pipeline
(
self
,
test_data_files
,
test_label_files
):
def
setup_test_pipeline
(
self
,
test_data_files
,
test_label_files
):
self
.
test_data_files
=
test_data_files
self
.
test_data_files
=
test_data_files
self
.
test_label_files
=
test_label_files
self
.
test_label_files
=
test_label_files
tst_idxs
=
np
.
arange
(
len
(
test_data_files
))
self
.
get_test_dataset
(
len
(
test_data_files
))
self
.
get_test_dataset
(
tst_idxs
)
print
(
'
setup_test_pipeline: Done
'
)
print
(
'
setup_test_pipeline: Done
'
)
def
build_srcnn
(
self
,
do_drop_out
=
False
,
do_batch_norm
=
False
,
drop_rate
=
0.5
,
factor
=
2
):
def
build_srcnn
(
self
,
do_drop_out
=
False
,
do_batch_norm
=
False
,
drop_rate
=
0.5
,
factor
=
2
):
...
@@ -742,7 +753,7 @@ class SRCNN:
...
@@ -742,7 +753,7 @@ class SRCNN:
self
.
num_data_samples
=
1000
self
.
num_data_samples
=
1000
valid_data_files
=
glob
.
glob
(
directory
+
'
valid*mres*.npy
'
)
valid_data_files
=
glob
.
glob
(
directory
+
'
valid*mres*.npy
'
)
valid_label_files
=
glob
.
glob
(
directory
+
'
valid*ires*.npy
'
)
valid_label_files
=
[
f
.
replace
(
'
mres
'
,
'
ires
'
)
for
f
in
valid_data_files
]
self
.
setup_test_pipeline
(
valid_data_files
,
valid_label_files
)
self
.
setup_test_pipeline
(
valid_data_files
,
valid_label_files
)
self
.
build_model
()
self
.
build_model
()
...
...
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