zhangyiss/mlx
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx.git synced 2025-06-24 09:21:16 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

add dilation for conv 3d layers + test for 3d conv w/ dilation (#1802)

Browse Source
This commit is contained in:
Awni Hannun 2025-01-28 06:17:07 -08:00 committed by GitHub
parent ccb61d7aae
commit 1017ac4a9e
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 22 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mlx/ops.cpp
View File

@ -3540,7 +3540,7 @@ Shape conv_out_shape(
if (out_shape[i] <= 0) {
std::ostringstream msg;
msg << "[conv] Spatial dimensions of input after padding "
msg << "[conv] Spatial dimensions of input after padding"
<< " cannot be smaller than weight spatial dimensions."
<< " Got error at axis " << i << " for input with shape " << in_shape
<< ", padding low " << pads_lo << ", padding high " << pads_hi

8
python/mlx/nn/layers/convolution.py
View File

@ -179,6 +179,7 @@ class Conv3d(Module):
kernel_size (int or tuple): The size of the convolution filters.
stride (int or tuple, optional): The size of the stride when
applying the filter. Default: ``1``.
dilation (int or tuple, optional): The dilation of the convolution.
padding (int or tuple, optional): How many positions to 0-pad
the input with. Default: ``0``.
bias (bool, optional): If ``True`` add a learnable bias to the
@ -192,6 +193,7 @@ class Conv3d(Module):
kernel_size: Union[int, tuple],
stride: Union[int, tuple] = 1,
padding: Union[int, tuple] = 0,
dilation: Union[int, tuple] = 1,
bias: bool = True,
):
super().__init__()
@ -213,16 +215,18 @@ class Conv3d(Module):
self.padding = padding
self.stride = stride
self.dilation = dilation
def _extra_repr(self):
return (
f"{self.weight.shape[-1]}, {self.weight.shape[0]}, "
f"kernel_size={self.weight.shape[1:3]}, stride={self.stride}, "
f"padding={self.padding}, bias={'bias' in self}"
f"padding={self.padding}, dilation={self.dilation}, "
f"bias={'bias' in self}"
)
def __call__(self, x):
y = mx.conv3d(x, self.weight, self.stride, self.padding)
y = mx.conv3d(x, self.weight, self.stride, self.padding, self.dilation)
if "bias" in self:
y = y + self.bias
return y

10
python/mlx/nn/layers/convolution_transpose.py
View File

@ -159,6 +159,7 @@ class ConvTranspose3d(Module):
applying the filter. Default: ``1``.
padding (int or tuple, optional): How many positions to 0-pad
the input with. Default: ``0``.
dilation (int or tuple, optional): The dilation of the convolution.
bias (bool, optional): If ``True`` add a learnable bias to the
output. Default: ``True``
"""
@ -170,6 +171,7 @@ class ConvTranspose3d(Module):
kernel_size: Union[int, tuple],
stride: Union[int, tuple] = 1,
padding: Union[int, tuple] = 0,
dilation: Union[int, tuple] = 1,
bias: bool = True,
):
super().__init__()
@ -191,16 +193,20 @@ class ConvTranspose3d(Module):
self.padding = padding
self.stride = stride
self.dilation = dilation
def _extra_repr(self):
return (
f"{self.weight.shape[-1]}, {self.weight.shape[0]}, "
f"kernel_size={self.weight.shape[1:3]}, stride={self.stride}, "
f"padding={self.padding}, bias={'bias' in self}"
f"padding={self.padding}, dilation={self.dilation}, "
f"bias={'bias' in self}"
)
def __call__(self, x):
y = mx.conv_transpose3d(x, self.weight, self.stride, self.padding)
y = mx.conv_transpose3d(
x, self.weight, self.stride, self.padding, self.dilation
)
if "bias" in self:
y = y + self.bias
return y

7
python/tests/test_conv.py
View File

@ -550,6 +550,7 @@ class TestConv(mlx_tests.MLXTestCase):
(1, 1, 6),
(4, 16, 32),
):
continue
for idim, kdim, stride, padding in (
((1, 1, 1), (1, 1, 1), (1, 1, 1), (0, 0, 0)),
((3, 3, 3), (3, 1, 1), (1, 1, 1), (0, 0, 0)),
@ -557,6 +558,12 @@ class TestConv(mlx_tests.MLXTestCase):
):
run_conv3D(N, C, O, idim, kdim, stride, padding, dtype=dtype)
N, C, O = (2, 4, 4)
idim, kdim, stride, padding = (6, 6, 6), (3, 1, 1), (1, 1, 1), (0, 0, 0)
run_conv3D(
N, C, O, idim, kdim, stride, padding, dilation=(2, 2, 2), dtype=dtype
)
@unittest.skipIf(not has_torch, "requires Torch")
def test_torch_conv_3D_grad(self):
def run_conv3D_grad(