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support conv3d on cpu for TF #19641

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Apr 30, 2024
Merged

support conv3d on cpu for TF #19641

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6 changes: 6 additions & 0 deletions keras/src/backend/tensorflow/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -252,6 +252,12 @@ def _conv_xla():
# If kernel's in_channel does not match input's channels, it indicates
# convolution is broken down into groups.
return _conv_xla()
if data_format == "channels_first" and len(inputs.shape) == 5:
inputs = convert_to_tensor(inputs)
if inputs.device.split(":")[-2] == "CPU":
inputs = tf.transpose(inputs, perm=(0, 2, 3, 4, 1))
data_format = "channels_last"
return tf.transpose(_conv(), perm=(0, 4, 1, 2, 3))
return _conv()


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16 changes: 11 additions & 5 deletions keras/src/ops/nn_test.py
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Expand Up @@ -1445,23 +1445,29 @@ def test_conv_2d_group_2(self, strides, dilation_rate):
)
self.assertAllClose(outputs, expected)

@parameterized.product(strides=(1, (1, 1, 1), 2), padding=("valid", "same"))
def test_conv_3d(self, strides, padding):
if backend.config.image_data_format() == "channels_last":
@parameterized.product(
strides=(1, (1, 1, 1), 2),
padding=("valid", "same"),
data_format=("channels_first", "channels_last"),
)
def test_conv_3d(self, strides, padding, data_format):
if data_format == "channels_last":
input_shape = (2, 8, 8, 8, 3)
else:
input_shape = (2, 3, 8, 8, 8)
inputs_3d = np.arange(3072, dtype=float).reshape(input_shape)
kernel = np.arange(162, dtype=float).reshape([3, 3, 3, 3, 2])

outputs = knn.conv(inputs_3d, kernel, strides, padding=padding)
outputs = knn.conv(
inputs_3d, kernel, strides, padding=padding, data_format=data_format
)
expected = np_conv3d(
inputs_3d,
kernel,
bias_weights=np.zeros((2,)),
strides=strides,
padding=padding,
data_format=backend.config.image_data_format(),
data_format=data_format,
dilation_rate=1,
groups=1,
)
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