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Convert RandomZoom to backend-agnostic and improve affine_transform #574

Merged
merged 18 commits into from
Jul 26, 2023
Merged

Convert RandomZoom to backend-agnostic and improve affine_transform #574

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0ff51ba
Convert RandomZoom
james77777778 Jul 21, 2023
aa6f95b
Fix docstring
james77777778 Jul 21, 2023
6097ca3
Update docstring
james77777778 Jul 21, 2023
a7c2266
Update docstring
james77777778 Jul 21, 2023
e85b5d2
Address comments
james77777778 Jul 24, 2023
7f9f54a
Update `affine_transform`
james77777778 Jul 24, 2023
d649de3
Merge branch 'main' of github.com:james77777778/keras-core into updat…
james77777778 Jul 24, 2023
d94fc97
Update
james77777778 Jul 24, 2023
94b950a
Fix fill_mode in torch
james77777778 Jul 24, 2023
ab1bb5f
Merge branch 'keras-team:main' into update-randomzoom
james77777778 Jul 25, 2023
b55c52d
Update `affine_transform`
james77777778 Jul 25, 2023
06d06c7
Revert RandomTranslation tests
james77777778 Jul 25, 2023
e06d1b6
Fix typo
james77777778 Jul 25, 2023
11567f7
Remove docstring
james77777778 Jul 25, 2023
678675a
Merge branch 'keras-team:main' into update-randomzoom
james77777778 Jul 26, 2023
34ea188
Redirect `fill_mode=reflect` to `fill_mode=mirror` using torch
james77777778 Jul 26, 2023
9f23afd
Update docstring
james77777778 Jul 26, 2023
df4492d
Update tests because using torch `fill_mode=reflect` is actually `fil…
james77777778 Jul 26, 2023
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Update tests because using torch fill_mode=reflect is actually `fil… 
…l_mode=mirror`
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@james77777778
james77777778 committed Jul 26, 2023
commit df4492d326716351d1ab115835e6cdc32a6fe3db
129 changes: 90 additions & 39 deletions keras_core/layers/preprocessing/random_translation_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,15 +58,27 @@ def test_random_translation_with_inference_mode(self):
@parameterized.parameters(["channels_first", "channels_last"])
def test_random_translation_up_numeric_reflect(self, data_format):
input_image = np.arange(0, 25)
expected_output = np.asarray(
[
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[20, 21, 22, 23, 24],
]
)
if backend.backend() == "torch":
# redirect fill_mode=reflect to fill_mode=mirror
expected_output = np.asarray(
[
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[15, 16, 17, 18, 19],
]
)
else:
expected_output = np.asarray(
[
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[20, 21, 22, 23, 24],
]
)
if data_format == "channels_last":
input_image = np.reshape(input_image, (1, 5, 5, 1))
expected_output = backend.convert_to_tensor(
Expand Down Expand Up @@ -133,15 +145,27 @@ def test_random_translation_up_numeric_constant(self, data_format):
def test_random_translation_down_numeric_reflect(self, data_format):
input_image = np.arange(0, 25)
# Shifting by .2 * 5 = 1 pixel.
expected_output = np.asarray(
[
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
]
)
if backend.backend() == "torch":
# redirect fill_mode=reflect to fill_mode=mirror
expected_output = np.asarray(
[
[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
]
)
else:
expected_output = np.asarray(
[
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
]
)
if data_format == "channels_last":
input_image = np.reshape(input_image, (1, 5, 5, 1))
expected_output = backend.convert_to_tensor(
Expand Down Expand Up @@ -172,18 +196,33 @@ def test_random_translation_asymmetric_size_numeric_reflect(
):
input_image = np.arange(0, 16)
# Shifting by .2 * 5 = 1 pixel.
expected_output = np.asarray(
[
[6, 7],
[4, 5],
[2, 3],
[0, 1],
[0, 1],
[2, 3],
[4, 5],
[6, 7],
]
)
if backend.backend() == "torch":
# redirect fill_mode=reflect to fill_mode=mirror
expected_output = np.asarray(
[
[8, 9],
[6, 7],
[4, 5],
[2, 3],
[0, 1],
[2, 3],
[4, 5],
[6, 7],
]
)
else:
expected_output = np.asarray(
[
[6, 7],
[4, 5],
[2, 3],
[0, 1],
[0, 1],
[2, 3],
[4, 5],
[6, 7],
]
)
if data_format == "channels_last":
input_image = np.reshape(input_image, (1, 8, 2, 1))
expected_output = backend.convert_to_tensor(
Expand Down Expand Up @@ -251,15 +290,27 @@ def test_random_translation_down_numeric_constant(self, data_format):
def test_random_translation_left_numeric_reflect(self, data_format):
input_image = np.arange(0, 25)
# Shifting by .2 * 5 = 1 pixel.
expected_output = np.asarray(
[
[1, 2, 3, 4, 4],
[6, 7, 8, 9, 9],
[11, 12, 13, 14, 14],
[16, 17, 18, 19, 19],
[21, 22, 23, 24, 24],
]
)
if backend.backend() == "torch":
# redirect fill_mode=reflect to fill_mode=mirror
expected_output = np.asarray(
[
[1, 2, 3, 4, 3],
[6, 7, 8, 9, 8],
[11, 12, 13, 14, 13],
[16, 17, 18, 19, 18],
[21, 22, 23, 24, 23],
]
)
else:
expected_output = np.asarray(
[
[1, 2, 3, 4, 4],
[6, 7, 8, 9, 9],
[11, 12, 13, 14, 14],
[16, 17, 18, 19, 19],
[21, 22, 23, 24, 24],
]
)
if data_format == "channels_last":
input_image = np.reshape(input_image, (1, 5, 5, 1))
expected_output = backend.convert_to_tensor(
Expand Down