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Max Ehrlich
cdcnn
Commits
8d890409
Verified
Commit
8d890409
authored
Dec 14, 2018
by
Max Ehrlich
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Updates to spatial throughput test
parent
81bf349d
Changes
3
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Showing
3 changed files
with
19 additions
and
12 deletions
+19
-12
experiments/jpeg_throughput.py
experiments/jpeg_throughput.py
+4
-4
experiments/spatial_throughput.py
experiments/spatial_throughput.py
+4
-4
models/utils.py
models/utils.py
+11
-4
No files found.
experiments/jpeg_throughput.py
View file @
8d890409
...
...
@@ -13,7 +13,7 @@ parser.add_argument('--batch_size', type=int, help='Batch size')
parser
.
add_argument
(
'--data'
,
help
=
'Root folder for the dataset'
)
args
=
parser
.
parse_args
()
spatial_
dataset
=
data
.
jpeg_dataset_map
[
args
.
dataset
](
args
.
batch_size
,
args
.
data
)
dataset
=
data
.
jpeg_dataset_map
[
args
.
dataset
](
args
.
batch_size
,
args
.
data
)
dataset_info
=
data
.
dataset_info
[
args
.
dataset
]
spatial_model
=
models
.
SpatialResNet
(
dataset_info
[
'channels'
],
dataset_info
[
'classes'
])
jpeg_model
=
models
.
JpegResNet
(
spatial_model
,
n_freqs
=
6
).
to
(
device
)
...
...
@@ -21,18 +21,18 @@ optimizer = optim.Adam(jpeg_model.parameters())
t0
=
time
.
perf_counter
()
models
.
train
(
jpeg_model
,
device
,
spatial_
dataset
[
0
],
optimizer
,
0
)
models
.
train
(
jpeg_model
,
device
,
dataset
[
0
],
optimizer
,
0
)
torch
.
cuda
.
synchronize
()
t1
=
time
.
perf_counter
()
training_time
=
t1
-
t0
t0
=
time
.
perf_counter
()
models
.
test
(
jpeg_model
,
device
,
spatial_
dataset
[
1
])
models
.
test
(
jpeg_model
,
device
,
dataset
[
1
])
torch
.
cuda
.
synchronize
()
t1
=
time
.
perf_counter
()
testing_time
=
t1
-
t0
with
open
(
'{}_jpeg_throughput.csv'
.
format
(
args
.
dataset
),
'w'
)
as
f
:
f
.
write
(
'Training, Testing
\n
'
)
f
.
write
(
'{}, {}
\n
'
.
format
(
training_time
/
len
(
spatial_dataset
[
0
]),
testing_time
/
len
(
spatial_
dataset
[
1
])))
f
.
write
(
'{}, {}
\n
'
.
format
(
training_time
/
len
(
dataset
[
0
]),
testing_time
/
len
(
dataset
[
1
])))
experiments/spatial_throughput.py
View file @
8d890409
...
...
@@ -14,25 +14,25 @@ parser.add_argument('--batch_size', type=int, help='Batch size')
parser
.
add_argument
(
'--data'
,
help
=
'Root folder for the dataset'
)
args
=
parser
.
parse_args
()
spatial_dataset
=
data
.
spatial
_dataset_map
[
args
.
dataset
](
args
.
batch_size
,
args
.
data
)
dataset
=
data
.
jpeg
_dataset_map
[
args
.
dataset
](
args
.
batch_size
,
args
.
data
)
dataset_info
=
data
.
dataset_info
[
args
.
dataset
]
spatial_model
=
models
.
SpatialResNet
(
dataset_info
[
'channels'
],
dataset_info
[
'classes'
]).
to
(
device
)
optimizer
=
optim
.
Adam
(
spatial_model
.
parameters
())
t0
=
time
.
perf_counter
()
models
.
train
(
spatial_model
,
device
,
spatial_dataset
[
0
],
optimizer
,
0
)
models
.
train
(
spatial_model
,
device
,
dataset
[
0
],
optimizer
,
0
,
do_decode
=
True
)
torch
.
cuda
.
synchronize
()
t1
=
time
.
perf_counter
()
training_time
=
t1
-
t0
t0
=
time
.
perf_counter
()
models
.
test
(
spatial_model
,
device
,
spatial_dataset
[
1
]
)
models
.
test
(
spatial_model
,
device
,
dataset
[
1
],
do_decode
=
True
)
torch
.
cuda
.
synchronize
()
t1
=
time
.
perf_counter
()
testing_time
=
t1
-
t0
with
open
(
'{}_spatial_throughput.csv'
.
format
(
args
.
dataset
),
'w'
)
as
f
:
f
.
write
(
'Training, Testing
\n
'
)
f
.
write
(
'{}, {}
\n
'
.
format
(
training_time
/
len
(
spatial_dataset
[
0
]),
testing_time
/
len
(
spatial_
dataset
[
1
])))
f
.
write
(
'{}, {}
\n
'
.
format
(
training_time
/
len
(
dataset
[
0
]),
testing_time
/
len
(
dataset
[
1
])))
models/utils.py
View file @
8d890409
import
torch
import
torch.nn.functional
as
F
from
jpeg_codec
import
decode
def
train
(
model
,
device
,
train_loader
,
optimizer
,
epoch
):
def
train
(
model
,
device
,
train_loader
,
optimizer
,
epoch
,
do_decode
=
False
):
model
.
train
()
for
batch_idx
,
(
data
,
target
)
in
enumerate
(
train_loader
):
data
,
target
=
data
.
to
(
device
),
target
.
to
(
device
)
if
do_decode
:
data
,
target
=
decode
(
data
).
to
(
device
),
target
.
to
(
device
)
else
:
data
,
target
=
data
.
to
(
device
),
target
.
to
(
device
)
optimizer
.
zero_grad
()
output
=
model
(
data
)
loss
=
F
.
cross_entropy
(
output
,
target
)
...
...
@@ -17,13 +21,16 @@ def train(model, device, train_loader, optimizer, epoch):
100.
*
batch_idx
/
len
(
train_loader
),
loss
.
item
()))
def
test
(
model
,
device
,
test_loader
):
def
test
(
model
,
device
,
test_loader
,
do_decode
=
False
):
model
.
eval
()
test_loss
=
0
correct
=
0
with
torch
.
no_grad
():
for
data
,
target
in
test_loader
:
data
,
target
=
data
.
to
(
device
),
target
.
to
(
device
)
if
do_decode
:
data
,
target
=
decode
(
data
).
to
(
device
),
target
.
to
(
device
)
else
:
data
,
target
=
data
.
to
(
device
),
target
.
to
(
device
)
output
=
model
(
data
)
test_loss
+=
F
.
cross_entropy
(
output
,
target
,
reduction
=
'sum'
).
item
()
pred
=
output
.
max
(
1
,
keepdim
=
True
)[
1
]
...
...
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