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OpenDAS
vision
Commits
3d70e4bb
Unverified
Commit
3d70e4bb
authored
Jun 19, 2023
by
Philip Meier
Committed by
GitHub
Jun 19, 2023
Browse files
remove obsolete transforms tests (#7678)
parent
8324c481
Changes
1
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-34
test/test_functional_tensor.py
test/test_functional_tensor.py
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-34
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test/test_functional_tensor.py
View file @
3d70e4bb
...
...
@@ -609,21 +609,6 @@ def test_resize_antialias(device, dt, size, interpolation):
assert_equal
(
resized_tensor
,
resize_result
)
@
needs_cuda
@
pytest
.
mark
.
parametrize
(
"interpolation"
,
[
BILINEAR
,
BICUBIC
])
def
test_assert_resize_antialias
(
interpolation
):
# Checks implementation on very large scales
# and catch TORCH_CHECK inside PyTorch implementation
torch
.
manual_seed
(
12
)
tensor
,
_
=
_create_data
(
1000
,
1000
,
device
=
"cuda"
)
# Error message is not yet updated in pytorch nightly
# with pytest.raises(RuntimeError, match=r"Provided interpolation parameters can not be handled"):
with
pytest
.
raises
(
RuntimeError
,
match
=
r
"Too much shared memory required"
):
F
.
resize
(
tensor
,
size
=
(
5
,
5
),
interpolation
=
interpolation
,
antialias
=
True
)
def
test_resize_antialias_default_warning
():
img
=
torch
.
randint
(
0
,
256
,
size
=
(
3
,
44
,
56
),
dtype
=
torch
.
uint8
)
...
...
@@ -641,25 +626,6 @@ def test_resize_antialias_default_warning():
F
.
resized_crop
(
img
,
0
,
0
,
10
,
10
,
size
=
(
20
,
20
),
interpolation
=
NEAREST
)
@
pytest
.
mark
.
parametrize
(
"device"
,
cpu_and_gpu
())
@
pytest
.
mark
.
parametrize
(
"dt"
,
[
torch
.
float32
,
torch
.
float64
,
torch
.
float16
])
@
pytest
.
mark
.
parametrize
(
"size"
,
[[
10
,
7
],
[
10
,
42
],
[
42
,
7
]])
@
pytest
.
mark
.
parametrize
(
"interpolation"
,
[
BILINEAR
,
BICUBIC
])
def
test_interpolate_antialias_backward
(
device
,
dt
,
size
,
interpolation
):
if
dt
==
torch
.
float16
and
device
==
"cpu"
:
# skip float16 on CPU case
return
torch
.
manual_seed
(
12
)
x
=
(
torch
.
rand
(
1
,
32
,
29
,
3
,
dtype
=
torch
.
double
,
device
=
device
).
permute
(
0
,
3
,
1
,
2
).
requires_grad_
(
True
),)
resize
=
partial
(
F
.
resize
,
size
=
size
,
interpolation
=
interpolation
,
antialias
=
True
)
assert
torch
.
autograd
.
gradcheck
(
resize
,
x
,
eps
=
1e-8
,
atol
=
1e-6
,
rtol
=
1e-6
,
fast_mode
=
False
)
x
=
(
torch
.
rand
(
1
,
3
,
32
,
29
,
dtype
=
torch
.
double
,
device
=
device
,
requires_grad
=
True
),)
assert
torch
.
autograd
.
gradcheck
(
resize
,
x
,
eps
=
1e-8
,
atol
=
1e-6
,
rtol
=
1e-6
,
fast_mode
=
False
)
def
check_functional_vs_PIL_vs_scripted
(
fn
,
fn_pil
,
fn_t
,
config
,
device
,
dtype
,
channels
=
3
,
tol
=
2.0
+
1e-10
,
agg_method
=
"max"
):
...
...
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