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import
pytest
import
arrayfire_wrapper
.
dtypes
as
dtype
import
arrayfire_wrapper
.
lib
as
wrapper
import
arrayfire_wrapper
.
lib
.
signal_processing
.
convolutions
as
convolutions
from
arrayfire_wrapper
.
lib
.
_constants
import
ConvDomain
,
ConvMode
from
tests
.
utility_functions
import
check_type_supported
,
get_all_types
,
get_float_types
# Parameterization for input shapes
@
pytest
.
mark
.
parametrize
(
"inputShape"
,
[
(
7
,
7
),
(
8
,
8
),
(
9
,
9
),
(
10
,
10
),
],
)
# Second parameterization for dtypes
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_float_types
())
def
test_convolve2_gradient_data
(
inputShape
:
tuple
[
int
,
int
],
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test if convolve gradient returns the correct shape with varying data type and grad type."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
original_signal
=
wrapper
.
randu
(
inputShape
,
dtypes
)
original_kernel
=
wrapper
.
randu
((
3
,
3
),
dtypes
)
strides
=
(
1
,
1
)
padding
=
(
1
,
1
)
dilation
=
(
1
,
1
)
result
=
convolutions
.
convolve2_nn
(
original_signal
,
original_kernel
,
strides
,
padding
,
dilation
)
match
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
inputShape
==
match
,
f"Failed for input shape:
{
inputShape
}
, Failed for dtype:
{
dtypes
}
"
# Third Parameterization for invalid dtypes
@
pytest
.
mark
.
parametrize
(
"invdtypes"
,
[
dtype
.
int32
,
# Integer 32-bit
dtype
.
uint32
,
# Unsigned Integer 32-bit
dtype
.
complex32
,
# Complex number with float 32-bit real and imaginary
],
)
def
test_convolve2_nn_invalid_data
(
invdtypes
:
dtype
.
Dtype
)
->
None
:
"""Test if convolve gradient returns the correct shape with varying data type and grad type."""
with
pytest
.
raises
(
RuntimeError
):
original_signal
=
wrapper
.
randu
((
8
,
8
),
invdtypes
)
original_kernel
=
wrapper
.
randu
((
3
,
3
),
invdtypes
)
padding
=
(
1
,
1
)
strides
=
(
1
,
1
)
dilation
=
(
1
,
1
)
result
=
convolutions
.
convolve2_nn
(
original_signal
,
original_kernel
,
strides
,
padding
,
dilation
)
match
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
match
==
result
,
f"Failed for dtype:
{
invdtypes
}
"
@
pytest
.
mark
.
parametrize
(
"padding"
, [(
0
,
0
), (
1
,
1
), (
2
,
2
)])
def
test_convolve2_nn_padding_variation
(
padding
:
tuple
[
int
,
int
])
->
None
:
"""Test convolution with varying padding sizes."""
original_signal
=
wrapper
.
randu
((
8
,
8
),
dtype
.
float32
)
original_kernel
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
strides
=
(
1
,
1
)
dilation
=
(
1
,
1
)
result
=
convolutions
.
convolve2_nn
(
original_signal
,
original_kernel
,
strides
,
padding
,
dilation
)
expected_height
=
((
5
+
2
*
padding
[
0
])
//
strides
[
0
])
+
1
expected_width
=
((
5
+
2
*
padding
[
1
])
//
strides
[
1
])
+
1
expected_shape
=
(
expected_height
,
expected_width
)
match
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
match
==
expected_shape
,
f"Failed for padding:
{
padding
}
"
@
pytest
.
mark
.
parametrize
(
"strides"
, [(
1
,
1
), (
2
,
2
), (
3
,
3
)])
def
test_convolve2_nn_strides_variation
(
strides
:
tuple
[
int
,
int
])
->
None
:
"""Test convolution with varying stride sizes."""
original_signal
=
wrapper
.
randu
((
8
,
8
),
dtype
.
float32
)
original_kernel
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
padding
=
(
1
,
1
)
dilation
=
(
1
,
1
)
result
=
convolutions
.
convolve2_nn
(
original_signal
,
original_kernel
,
strides
,
padding
,
dilation
)
expected_height
=
((
5
+
2
*
padding
[
0
])
//
strides
[
0
])
+
1
expected_width
=
((
5
+
2
*
padding
[
1
])
//
strides
[
1
])
+
1
expected_shape
=
(
expected_height
,
expected_width
)
match
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
match
==
expected_shape
,
f"Failed for stride:
{
strides
}
"
# Parameterize for different dilation sizes
@
pytest
.
mark
.
parametrize
(
"dilation"
, [(
1
,
1
), (
2
,
2
), (
3
,
3
)])
def
test_convolve2_nn_dilation_variation
(
dilation
:
tuple
[
int
,
int
])
->
None
:
"""Test convolution with varying dilation sizes."""
original_signal
=
wrapper
.
randu
((
8
,
8
),
dtype
.
float32
)
original_kernel
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
strides
=
(
1
,
1
)
padding
=
(
1
,
1
)
result
=
convolutions
.
convolve2_nn
(
original_signal
,
original_kernel
,
strides
,
padding
,
dilation
)
# The calculation of the expected shape might need adjustment based on how dilation affects your convolutions
expected_height
=
(
(
wrapper
.
get_dims
(
original_signal
)[
0
]
+
2
*
padding
[
0
]
-
dilation
[
0
]
*
(
wrapper
.
get_dims
(
original_kernel
)[
0
]
-
1
)
-
1
)
//
strides
[
0
]
)
+
1
expected_width
=
(
(
wrapper
.
get_dims
(
original_signal
)[
1
]
+
2
*
padding
[
1
]
-
dilation
[
1
]
*
(
wrapper
.
get_dims
(
original_kernel
)[
1
]
-
1
)
-
1
)
//
strides
[
1
]
)
+
1
expected_shape
=
(
expected_height
,
expected_width
)
match
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
match
==
expected_shape
,
f"Failed for dilation:
{
dilation
}
"
@
pytest
.
mark
.
parametrize
(
"kernel_size"
, [(
3
,
3
), (
5
,
5
), (
7
,
7
)])
def
test_convolve2_nn_kernel_size_variation
(
kernel_size
:
tuple
[
int
,
int
])
->
None
:
"""Test convolution with varying kernel sizes."""
original_signal
=
wrapper
.
randu
((
8
,
8
),
dtype
.
float32
)
original_kernel
=
wrapper
.
randu
(
kernel_size
,
dtype
.
float32
)
strides
=
(
1
,
1
)
padding
=
(
1
,
1
)
dilation
=
(
1
,
1
)
result
=
convolutions
.
convolve2_nn
(
original_signal
,
original_kernel
,
strides
,
padding
,
dilation
)
expected_height
=
(
(
wrapper
.
get_dims
(
original_signal
)[
0
]
+
2
*
padding
[
0
]
-
dilation
[
0
]
*
(
wrapper
.
get_dims
(
original_kernel
)[
0
]
-
1
)
-
1
)
//
strides
[
0
]
)
+
1
expected_width
=
(
(
wrapper
.
get_dims
(
original_signal
)[
1
]
+
2
*
padding
[
1
]
-
dilation
[
1
]
*
(
wrapper
.
get_dims
(
original_kernel
)[
1
]
-
1
)
-
1
)
//
strides
[
1
]
)
+
1
expected_shape
=
(
expected_height
,
expected_width
)
match
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
match
==
expected_shape
,
f"Failed for kernel size:
{
kernel_size
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,), (
12
,
12
), (
10
,
10
,
10
)])
def
test_convolve1_input_size
(
input_size
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve1 with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,),
dtype
.
float32
)
result
=
convolutions
.
convolve1
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output_size
=
wrapper
.
get_dims
(
signal
)[
0
]
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"filter_size"
, [(
3
,), (
5
,), (
7
,)])
def
test_convolve1_filter_size
(
filter_size
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve1 with varying filter sizes."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
(
filter_size
,
dtype
.
float32
)
result
=
convolutions
.
convolve1
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
# Assuming ConvMode(0) maintains the input size
expected_output_size
=
wrapper
.
get_dims
(
signal
)[
0
]
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for filter_size:
{
filter_size
}
"
@
pytest
.
mark
.
parametrize
(
"conv_mode"
, [
0
,
1
])
# 0: AF_CONV_DEFAULT, 1: AF_CONV_EXPAND
def
test_convolve1_conv_mode
(
conv_mode
:
int
)
->
None
:
"""Test convolve1 with varying convolution modes."""
input_size
=
10
filter_size
=
3
signal
=
wrapper
.
randu
((
input_size
,
input_size
),
dtype
.
float32
)
filter
=
wrapper
.
randu
((
filter_size
,),
dtype
.
float32
)
result
=
convolutions
.
convolve1
(
signal
,
filter
,
ConvMode
(
conv_mode
),
ConvDomain
(
0
))
if
conv_mode
==
0
:
# AF_CONV_DEFAULT
expected_output_size
=
input_size
elif
conv_mode
==
1
:
# AF_CONV_EXPAND
expected_output_size
=
input_size
+
filter_size
-
1
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for conv_mode:
{
conv_mode
}
"
@
pytest
.
mark
.
parametrize
(
"conv_domain"
, [
0
,
1
,
2
])
# AUTO, SPATIAL, and FREQ
def
test_convolve1_conv_domain
(
conv_domain
:
int
)
->
None
:
"""Test convolve1 with varying convolution domains."""
input_size
=
10
filter_size
=
3
signal
=
wrapper
.
randu
((
input_size
,
input_size
),
dtype
.
float32
)
filter
=
wrapper
.
randu
((
filter_size
,),
dtype
.
float32
)
result
=
convolutions
.
convolve1
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
conv_domain
))
assert
wrapper
.
get_dims
(
result
)[
0
]
==
input_size
,
f"Failed for conv_domain:
{
ConvDomain
(
conv_domain
)
}
"
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_all_types
())
def
test_convolve1_valid
(
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test convolve1 with valid dtypes."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
input_size
=
10
filter_size
=
3
signal
=
wrapper
.
randu
((
input_size
,
input_size
),
dtypes
)
filter
=
wrapper
.
randu
((
filter_size
,),
dtypes
)
result
=
convolutions
.
convolve1
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
assert
wrapper
.
get_dims
(
result
)[
0
]
==
input_size
,
f"Failed for dtype:
{
dtypes
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,), (
12
,
12
), (
10
,
10
,
10
)])
def
test_fft_convolve1_input_size
(
input_size
:
tuple
[
int
,
int
])
->
None
:
"""Test fft_convolve1 with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,),
dtype
.
float32
)
result
=
convolutions
.
fft_convolve1
(
signal
,
filter
,
ConvMode
(
0
))
expected_output_size
=
wrapper
.
get_dims
(
result
)[
0
]
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"filter_size"
, [(
3
,), (
5
,), (
7
,)])
def
test_fft_convolve1_filter_size
(
filter_size
:
tuple
[
int
,
int
])
->
None
:
"""Test fft_convolve1 with varying filter sizes."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
(
filter_size
,
dtype
.
float32
)
result
=
convolutions
.
fft_convolve1
(
signal
,
filter
,
ConvMode
(
0
))
expected_output_size
=
wrapper
.
get_dims
(
signal
)[
0
]
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for filter_size:
{
filter_size
}
"
@
pytest
.
mark
.
parametrize
(
"conv_mode"
, [
0
,
1
])
def
test_fft_convolve1_conv_mode
(
conv_mode
:
int
)
->
None
:
"""Test fft_convolve1 with varying convolution modes."""
input_size
=
(
10
,
10
)
filter_size
=
3
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
float32
)
filter
=
wrapper
.
randu
((
filter_size
,),
dtype
.
float32
)
result
=
convolutions
.
fft_convolve1
(
signal
,
filter
,
ConvMode
(
conv_mode
))
if
conv_mode
==
0
:
# AF_CONV_DEFAULT
expected_output_size
=
input_size
[
0
]
elif
conv_mode
==
1
:
# AF_CONV_EXPAND
expected_output_size
=
input_size
[
0
]
+
filter_size
-
1
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for conv_mode:
{
conv_mode
}
"
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_all_types
())
def
test_fft_convolve1_valid
(
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test fft_convolve1 with valid dtypes."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
signal
=
wrapper
.
randu
((
10
,
10
),
dtypes
)
filter
=
wrapper
.
randu
((
3
,),
dtypes
)
result
=
convolutions
.
fft_convolve1
(
signal
,
filter
,
ConvMode
(
0
))
expected_output_size
=
wrapper
.
get_dims
(
signal
)[
0
]
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for dtype:
{
dtypes
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,
8
), (
12
,
12
,
12
), (
10
,
10
)])
def
test_convolve2_input_size
(
input_size
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve2 with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
result
=
convolutions
.
convolve2
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
)
==
expected_output
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"filter_size"
, [(
3
,
3
), (
5
,), (
7
,
7
,
7
)])
def
test_convolve2_filter_size
(
filter_size
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve2 with varying filter sizes."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
(
filter_size
,
dtype
.
float32
)
result
=
convolutions
.
convolve2
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
])
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
)
==
expected_output
,
f"Failed for filter_size:
{
filter_size
}
"
@
pytest
.
mark
.
parametrize
(
"conv_mode"
, [
0
,
1
])
# 0: Default, 1: Expand
def
test_convolve2_conv_mode
(
conv_mode
:
int
)
->
None
:
"""Test convolve2 with varying convolution modes."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
result
=
convolutions
.
convolve2
(
signal
,
filter
,
ConvMode
(
conv_mode
),
ConvDomain
(
0
))
if
conv_mode
==
0
:
# Default
expected_output
=
(
10
,
10
)
else
:
# Expand
expected_output
=
(
12
,
12
)
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
)
==
expected_output
,
f"Failed for conv_mode:
{
conv_mode
}
"
@
pytest
.
mark
.
parametrize
(
"conv_domain"
, [
0
,
1
,
2
])
# 0: Auto, 1: Spatial, 2: Frequency
def
test_convolve2_conv_domain
(
conv_domain
:
int
)
->
None
:
"""Test convolve2 with varying convolution domains."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
result
=
convolutions
.
convolve2
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
conv_domain
))
expected_output
=
(
10
,
10
)
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
)
==
expected_output
,
f"Failed for conv_domain:
{
conv_domain
}
"
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_all_types
())
def
test_convolve2_conv_valid_dtype
(
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test fft_convolve1 with valid dtypes."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
signal
=
wrapper
.
randu
((
10
,
10
),
dtypes
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtypes
)
result
=
convolutions
.
convolve2
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output_size
=
wrapper
.
get_dims
(
signal
)[
0
]
assert
wrapper
.
get_dims
(
result
)[
0
]
==
expected_output_size
,
f"Failed for dtype:
{
dtypes
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,
8
), (
12
,
12
,
12
), (
10
,
10
)])
def
test_fftConvolve2_input_size
(
input_size
:
tuple
[
int
,
int
])
->
None
:
"""Test fftConvolve2 with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
result
=
convolutions
.
fft_convolve2
(
signal
,
filter
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
)
==
expected_output
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"filter_size"
, [(
3
,
3
), (
5
,
5
), (
7
,
7
,
7
)])
def
test_fftConvolve2_filter_size
(
filter_size
:
tuple
[
int
,
int
])
->
None
:
"""Test fftConvolve2 with varying filter sizes."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
(
filter_size
,
dtype
.
float32
)
result
=
convolutions
.
fft_convolve2
(
signal
,
filter
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
])
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
)
==
expected_output
,
f"Failed for filter_size:
{
filter_size
}
"
@
pytest
.
mark
.
parametrize
(
"conv_mode"
, [
0
,
1
])
# 0: Default, 1: Expand
def
test_fftConvolve2_conv_mode
(
conv_mode
:
int
)
->
None
:
"""Test fftConvolve2 with varying convolution modes."""
signal
=
wrapper
.
randu
((
10
,
10
),
dtype
.
float32
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtype
.
float32
)
result
=
convolutions
.
fft_convolve2
(
signal
,
filter
,
ConvMode
(
conv_mode
))
if
conv_mode
==
0
:
# Default
expected_output
=
(
10
,
10
)
else
:
# Expand
expected_output
=
(
12
,
12
)
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
)
==
expected_output
,
f"Failed for conv_mode:
{
conv_mode
}
"
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_all_types
())
def
test_fftConvolve2_valid_dtype
(
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test fft_convolve1 with valid dtypes."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
signal
=
wrapper
.
randu
((
10
,
10
),
dtypes
)
filter
=
wrapper
.
randu
((
3
,
3
),
dtypes
)
result
=
convolutions
.
fft_convolve2
(
signal
,
filter
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
])
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
)
==
expected_output
,
f"Failed for dtype:
{
dtypes
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,
8
), (
12
,
12
,
12
), (
10
,
10
)])
def
test_convolve2_sep_input_size
(
input_size
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve2_sep with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
float32
)
col_filter
=
wrapper
.
randu
((
3
,
1
),
dtype
.
float32
)
row_filter
=
wrapper
.
randu
((
3
,
1
),
dtype
.
float32
)
result
=
convolutions
.
convolve2_sep
(
col_filter
,
row_filter
,
signal
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
)
==
expected_output
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"col_vector"
, [(
3
,
1
), (
4
,
1
), (
5
,
1
)])
def
test_convolve2_sep_col_vector
(
col_vector
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve2_sep with varying column vector sizes."""
signal
=
wrapper
.
randu
((
8
,
8
),
dtype
.
float32
)
col_filter
=
wrapper
.
randu
(
col_vector
,
dtype
.
float32
)
row_filter
=
wrapper
.
randu
((
3
,
1
),
dtype
.
float32
)
result
=
convolutions
.
convolve2_sep
(
col_filter
,
row_filter
,
signal
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
)
==
expected_output
,
f"Failed for column vector:
{
col_vector
}
"
@
pytest
.
mark
.
parametrize
(
"row_vector"
, [(
3
,
1
), (
4
,
1
), (
5
,
1
)])
def
test_convolve2_sep_row_vector
(
row_vector
:
tuple
[
int
,
int
])
->
None
:
"""Test convolve2_sep with varying row vector sizes."""
signal
=
wrapper
.
randu
((
8
,
8
),
dtype
.
float32
)
col_filter
=
wrapper
.
randu
((
3
,
1
),
dtype
.
float32
)
row_filter
=
wrapper
.
randu
(
row_vector
,
dtype
.
float32
)
result
=
convolutions
.
convolve2_sep
(
col_filter
,
row_filter
,
signal
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
)
==
expected_output
,
f"Failed for column vector:
{
row_vector
}
"
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_all_types
())
def
test_convolve2_valid_dtypes
(
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test convolve2_sep with varying invalid data types."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
signal
=
wrapper
.
randu
((
8
,
8
),
dtypes
)
col_filter
=
wrapper
.
randu
((
3
,
1
),
dtypes
)
row_filter
=
wrapper
.
randu
((
3
,
1
),
dtypes
)
result
=
convolutions
.
convolve2_sep
(
col_filter
,
row_filter
,
signal
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
)
==
expected_output
,
f"Failed for data type:
{
dtypes
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,
8
,
8
), (
12
,
12
,
12
), (
10
,
10
,
10
,
10
)])
def
test_convolve3_input_size
(
input_size
:
tuple
[
int
,
int
,
int
])
->
None
:
"""Test convolve3 with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
f32
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
dtype
.
f32
)
result
=
convolutions
.
convolve3
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
wrapper
.
get_dims
(
signal
)[
2
],
)
==
expected_output
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"filter_size"
,
[
(
3
,
3
,
3
),
(
5
,
5
,
),
(
2
,
2
,
2
,
2
),
],
)
def
test_convolve3_filter_size
(
filter_size
:
tuple
[
int
,
int
,
int
])
->
None
:
"""Test convolve3 with varying filter sizes."""
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
dtype
.
f32
)
filter
=
wrapper
.
randu
(
filter_size
,
dtype
.
f32
)
result
=
convolutions
.
convolve3
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
wrapper
.
get_dims
(
signal
)[
2
],
)
==
expected_output
,
f"Failed for filter_size:
{
filter_size
}
"
@
pytest
.
mark
.
parametrize
(
"conv_mode"
, [
0
,
1
])
# 0: Default, 1: Expand
def
test_convolve3_conv_mode
(
conv_mode
:
int
)
->
None
:
"""Test convolve3 with varying convolution modes."""
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
dtype
.
f32
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
dtype
.
f32
)
result
=
convolutions
.
convolve3
(
signal
,
filter
,
ConvMode
(
conv_mode
),
ConvDomain
(
0
))
if
conv_mode
==
0
:
expected_output
=
(
10
,
10
,
10
)
else
:
expected_output
=
(
10
+
3
-
1
,
10
+
3
-
1
,
10
+
3
-
1
)
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
],
)
==
expected_output
,
f"Failed for conv_mode:
{
conv_mode
}
"
@
pytest
.
mark
.
parametrize
(
"dtypes"
,
get_all_types
())
def
test_convolve3_valid_dtype
(
dtypes
:
dtype
.
Dtype
)
->
None
:
"""Test convolve3 with valid data types."""
check_type_supported
(
dtypes
)
if
dtypes
==
dtype
.
f16
:
pytest
.
skip
()
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
dtypes
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
dtypes
)
result
=
convolutions
.
convolve3
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
wrapper
.
get_dims
(
signal
)[
2
],
)
==
expected_output
,
f"Failed for dtype:
{
dtypes
}
"
@
pytest
.
mark
.
parametrize
(
"conv_domain"
, [
0
,
1
,
2
])
def
test_convolve3_conv_domain
(
conv_domain
:
int
)
->
None
:
"""Test convolve3 with varying convolution domains."""
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
dtype
.
f32
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
dtype
.
f32
)
result
=
convolutions
.
convolve3
(
signal
,
filter
,
ConvMode
(
0
),
ConvDomain
(
conv_domain
))
expected_output
=
(
10
,
10
,
10
)
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
],
)
==
expected_output
,
f"Failed for conv_mode:
{
conv_domain
}
"
@
pytest
.
mark
.
parametrize
(
"input_size"
, [(
8
,
8
,
8
), (
12
,
12
,
12
), (
10
,
10
,
10
,
10
)])
def
test_fft_convolve3_input_size
(
input_size
:
tuple
[
int
,
int
,
int
])
->
None
:
"""Test fft_convolve3 with varying input sizes."""
signal
=
wrapper
.
randu
(
input_size
,
dtype
.
f32
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
dtype
.
f32
)
result
=
wrapper
.
fft_convolve3
(
signal
,
filter
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
wrapper
.
get_dims
(
signal
)[
2
],
)
==
expected_output
,
f"Failed for input_size:
{
input_size
}
"
@
pytest
.
mark
.
parametrize
(
"filter_size"
, [(
3
,
3
,
3
), (
5
,
5
,
5
), (
2
,
2
,
2
,
2
)])
def
test_fft_convolve3_filter_size
(
filter_size
:
tuple
[
int
,
int
,
int
])
->
None
:
"""Test fft_convolve3 with varying filter sizes."""
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
dtype
.
f32
)
filter
=
wrapper
.
randu
(
filter_size
,
dtype
.
f32
)
result
=
wrapper
.
fft_convolve3
(
signal
,
filter
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
wrapper
.
get_dims
(
signal
)[
2
],
)
==
expected_output
,
f"Failed for filter_size:
{
filter_size
}
"
@
pytest
.
mark
.
parametrize
(
"conv_mode"
, [
0
,
1
])
# Assuming 0: Default, 1: Expand, for example purposes
def
test_fft_convolve3_conv_mode
(
conv_mode
:
int
)
->
None
:
"""Test fft_convolve3 with varying convolution modes."""
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
dtype
.
f32
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
dtype
.
f32
)
result
=
wrapper
.
fft_convolve3
(
signal
,
filter
,
ConvMode
(
conv_mode
))
if
conv_mode
==
0
:
expected_output
=
(
10
,
10
,
10
)
else
:
expected_output
=
(
10
+
3
-
1
,
10
+
3
-
1
,
10
+
3
-
1
)
assert
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
],
)
==
expected_output
,
f"Failed for conv_mode:
{
conv_mode
}
"
@
pytest
.
mark
.
parametrize
(
"valid_dtype"
,
get_all_types
())
def
test_fft_convolve3_valid_dtype
(
valid_dtype
:
dtype
.
Dtype
)
->
None
:
"""Test fft_convolve3 with valid data types."""
check_type_supported
(
valid_dtype
)
if
valid_dtype
==
dtype
.
f16
:
pytest
.
skip
()
signal
=
wrapper
.
randu
((
10
,
10
,
10
),
valid_dtype
)
filter
=
wrapper
.
randu
((
3
,
3
,
3
),
valid_dtype
)
result
=
wrapper
.
fft_convolve3
(
signal
,
filter
,
ConvMode
(
0
))
expected_output
=
(
wrapper
.
get_dims
(
result
)[
0
],
wrapper
.
get_dims
(
result
)[
1
],
wrapper
.
get_dims
(
result
)[
2
])
assert
(
wrapper
.
get_dims
(
signal
)[
0
],
wrapper
.
get_dims
(
signal
)[
1
],
wrapper
.
get_dims
(
signal
)[
2
],
)
==
expected_output
,
f"Failed for dtype:
{
valid_dtype
}
"
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