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convert padding to lower case (#18553)

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Ramesh Sampath 2023-10-06 03:45:15 -05:00 committed by GitHub
parent e82c68ecdb
commit 1fb14e178c
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 84 additions and 44 deletions
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18
keras/ops/nn.py
View File

@ -592,7 +592,7 @@ class MaxPool(Operation):
super().__init__()
self.pool_size = pool_size
self.strides = strides
self.padding = padding
self.padding = padding.lower()
self.data_format = data_format
def call(self, inputs):
@ -656,6 +656,7 @@ def max_pool(
A tensor of rank N+2, the result of the max pooling operation.
"""
data_format = standardize_data_format(data_format)
padding = padding.lower()
if any_symbolic_tensors((inputs,)):
return MaxPool(
pool_size,
@ -677,7 +678,7 @@ class AveragePool(Operation):
super().__init__()
self.pool_size = pool_size
self.strides = strides
self.padding = padding
self.padding = padding.lower()
self.data_format = data_format
def call(self, inputs):
@ -746,6 +747,7 @@ def average_pool(
A tensor of rank N+2, the result of the average pooling operation.
"""
data_format = standardize_data_format(data_format)
padding = padding.lower()
if any_symbolic_tensors((inputs,)):
return AveragePool(
pool_size,
@ -768,7 +770,7 @@ class Conv(Operation):
):
super().__init__()
self.strides = strides
self.padding = padding
self.padding = padding.lower()
self.data_format = data_format
self.dilation_rate = dilation_rate
@ -841,6 +843,7 @@ def conv(
A tensor of rank N+2, the result of the conv operation.
"""
data_format = standardize_data_format(data_format)
padding = padding.lower()
if any_symbolic_tensors((inputs,)):
return Conv(strides, padding, data_format, dilation_rate).symbolic_call(
inputs, kernel
@ -860,7 +863,7 @@ class DepthwiseConv(Operation):
):
super().__init__()
self.strides = strides
self.padding = padding
self.padding = padding.lower()
self.data_format = data_format
self.dilation_rate = dilation_rate
@ -938,6 +941,7 @@ def depthwise_conv(
A tensor of rank N+2, the result of the depthwise conv operation.
"""
data_format = standardize_data_format(data_format)
padding = padding.lower()
if any_symbolic_tensors((inputs,)):
return DepthwiseConv(
strides, padding, data_format, dilation_rate
@ -962,7 +966,7 @@ class SeparableConv(Operation):
):
super().__init__()
self.strides = strides
self.padding = padding
self.padding = padding.lower()
self.data_format = data_format
self.dilation_rate = dilation_rate
@ -1051,6 +1055,7 @@ def separable_conv(
A tensor of rank N+2, the result of the depthwise conv operation.
"""
data_format = standardize_data_format(data_format)
padding = padding.lower()
if any_symbolic_tensors((inputs,)):
return SeparableConv(
strides,
@ -1081,7 +1086,7 @@ class ConvTranspose(Operation):
super().__init__()
self.strides = strides
self.output_padding = output_padding
self.padding = padding
self.padding = padding.lower()
self.data_format = data_format
self.dilation_rate = dilation_rate
@ -1175,6 +1180,7 @@ def conv_transpose(
A tensor of rank N+2, the result of the conv operation.
"""
data_format = standardize_data_format(data_format)
padding = padding.lower()
if any_symbolic_tensors((inputs,)):
return ConvTranspose(
strides, padding, output_padding, data_format, dilation_rate

110
keras/ops/nn_test.py
View File

@ -121,12 +121,16 @@ class NNOpsDynamicShapeTest(testing.TestCase, parameterized.TestCase):
# Test 1D conv.
inputs_1d = KerasTensor([None, 20, 3])
kernel = KerasTensor([4, 3, 2])
self.assertEqual(
knn.conv(inputs_1d, kernel, 1, padding="valid").shape, (None, 17, 2)
)
self.assertEqual(
knn.conv(inputs_1d, kernel, 1, padding="same").shape, (None, 20, 2)
)
for padding in ["valid", "VALID"]:
self.assertEqual(
knn.conv(inputs_1d, kernel, 1, padding=padding).shape,
(None, 17, 2),
)
for padding in ["same", "SAME"]:
self.assertEqual(
knn.conv(inputs_1d, kernel, 1, padding=padding).shape,
(None, 20, 2),
)
self.assertEqual(
knn.conv(inputs_1d, kernel, (2,), dilation_rate=2).shape,
(None, 7, 2),
@ -135,30 +139,52 @@ class NNOpsDynamicShapeTest(testing.TestCase, parameterized.TestCase):
# Test 2D conv.
inputs_2d = KerasTensor([None, 10, None, 3])
kernel = KerasTensor([2, 2, 3, 2])
self.assertEqual(
knn.conv(inputs_2d, kernel, 1, padding="valid").shape,
(None, 9, None, 2),
)
self.assertEqual(
knn.conv(inputs_2d, kernel, 1, padding="same").shape,
(None, 10, None, 2),
)
for padding in ["valid", "VALID"]:
self.assertEqual(
knn.conv(inputs_2d, kernel, 1, padding=padding).shape,
(None, 9, None, 2),
)
for padding in ["same", "SAME"]:
self.assertEqual(
knn.conv(inputs_2d, kernel, 1, padding=padding).shape,
(None, 10, None, 2),
)
self.assertEqual(
knn.conv(inputs_2d, kernel, (2, 1), dilation_rate=(2, 1)).shape,
(None, 4, None, 2),
)
# Test 2D conv - H, W specified
inputs_2d = KerasTensor([None, 10, 10, 3])
kernel = KerasTensor([2, 2, 3, 2])
for padding in ["valid", "VALID"]:
self.assertEqual(
knn.conv(inputs_2d, kernel, 1, padding=padding).shape,
(None, 9, 9, 2),
)
for padding in ["same", "SAME"]:
self.assertEqual(
knn.conv(inputs_2d, kernel, 1, padding=padding).shape,
(None, 10, 10, 2),
)
self.assertEqual(
knn.conv(inputs_2d, kernel, (2, 1), dilation_rate=(2, 1)).shape,
(None, 4, 9, 2),
)
# Test 3D conv.
inputs_3d = KerasTensor([None, 8, None, 8, 3])
kernel = KerasTensor([3, 3, 3, 3, 2])
self.assertEqual(
knn.conv(inputs_3d, kernel, 1, padding="valid").shape,
(None, 6, None, 6, 2),
)
self.assertEqual(
knn.conv(inputs_3d, kernel, (2, 1, 2), padding="same").shape,
(None, 4, None, 4, 2),
)
for padding in ["valid", "VALID"]:
self.assertEqual(
knn.conv(inputs_3d, kernel, 1, padding=padding).shape,
(None, 6, None, 6, 2),
)
for padding in ["same", "SAME"]:
self.assertEqual(
knn.conv(inputs_3d, kernel, (2, 1, 2), padding=padding).shape,
(None, 4, None, 4, 2),
)
self.assertEqual(
knn.conv(
inputs_3d, kernel, 1, padding="valid", dilation_rate=(1, 2, 2)
@ -170,14 +196,18 @@ class NNOpsDynamicShapeTest(testing.TestCase, parameterized.TestCase):
# Test 1D depthwise conv.
inputs_1d = KerasTensor([None, 20, 3])
kernel = KerasTensor([4, 3, 1])
self.assertEqual(
knn.depthwise_conv(inputs_1d, kernel, 1, padding="valid").shape,
(None, 17, 3),
)
self.assertEqual(
knn.depthwise_conv(inputs_1d, kernel, (1,), padding="same").shape,
(None, 20, 3),
)
for padding in ["valid", "VALID"]:
self.assertEqual(
knn.depthwise_conv(inputs_1d, kernel, 1, padding=padding).shape,
(None, 17, 3),
)
for padding in ["same", "SAME"]:
self.assertEqual(
knn.depthwise_conv(
inputs_1d, kernel, (1,), padding=padding
).shape,
(None, 20, 3),
)
self.assertEqual(
knn.depthwise_conv(inputs_1d, kernel, 2, dilation_rate=2).shape,
(None, 7, 3),
@ -186,14 +216,18 @@ class NNOpsDynamicShapeTest(testing.TestCase, parameterized.TestCase):
# Test 2D depthwise conv.
inputs_2d = KerasTensor([None, 10, 10, 3])
kernel = KerasTensor([2, 2, 3, 1])
self.assertEqual(
knn.depthwise_conv(inputs_2d, kernel, 1, padding="valid").shape,
(None, 9, 9, 3),
)
self.assertEqual(
knn.depthwise_conv(inputs_2d, kernel, (1, 2), padding="same").shape,
(None, 10, 5, 3),
)
for padding in ["valid", "VALID"]:
self.assertEqual(
knn.depthwise_conv(inputs_2d, kernel, 1, padding=padding).shape,
(None, 9, 9, 3),
)
for padding in ["same", "SAME"]:
self.assertEqual(
knn.depthwise_conv(
inputs_2d, kernel, (1, 2), padding=padding
).shape,
(None, 10, 5, 3),
)
self.assertEqual(
knn.depthwise_conv(inputs_2d, kernel, 2, dilation_rate=2).shape,
(None, 4, 4, 3),