depthwise
GitOrigin-RevId: 950d2f4889
master
@@ -543,11 +543,11 @@ class RegionRestrictedConvolutionForward : public ConvolutionBase<param::Convolu | |||
public: | |||
/** | |||
* \param[in] src (n, ic, ih, iw) | |||
* \param[in] filter (oc, ic, fh, fw) | |||
* \param[in] src (n, ic, ih, iw) or (n, g*icpg, ih, iw) | |||
* \param[in] filter (oc, ic, fh, fw) or (g, ocpg, icpg, fh, fw) | |||
* \param[in] rin (n, ih, iw) | |||
* \param[in] rout (n, oh, ow) | |||
* \param[out] dst (n, oc, oh, ow) | |||
* \param[out] dst (n, oc, oh, ow) or (n, g*ocpg, oh, ow) | |||
*/ | |||
virtual void exec( | |||
_megdnn_tensor_in src, _megdnn_tensor_in filter, _megdnn_tensor_in rin, | |||
@@ -592,11 +592,11 @@ class RegionRestrictedConvolutionBackwardData | |||
public: | |||
/** | |||
* \param[in] filter (oc, ic, fh, fw) | |||
* \param[in] diff (n, oc, oh, ow) | |||
* \param[in] filter (oc, ic, fh, fw) or (g, ocpg, icpg, fh, fw) | |||
* \param[in] diff (n, oc, oh, ow) or (n, g*ocpg, oh, ow) | |||
* \param[in] rin (n, ih, iw) | |||
* \param[in] rout (n, oh, ow) | |||
* \param[out] grad (n, ic, ih, iw) | |||
* \param[out] grad (n, ic, ih, iw) or (n, g*icpg, ih, iw) | |||
*/ | |||
virtual void exec( | |||
_megdnn_tensor_in filter, _megdnn_tensor_in diff, _megdnn_tensor_in rin, | |||
@@ -635,11 +635,11 @@ class RegionRestrictedConvolutionBackwardFilter | |||
public: | |||
/** | |||
* \param[in] src (n, ic, ih, iw) | |||
* \param[in] diff (n, oc, oh, ow) | |||
* \param[in] src (n, ic, ih, iw) or (n, g*icpg, ih, iw) | |||
* \param[in] diff (n, oc, oh, ow) or (n, g*ocpg, oh, ow) | |||
* \param[in] rin (n, ih, iw) | |||
* \param[in] rout (n, oh, ow) | |||
* \param[out] grad (oc, ic, fh, fw) | |||
* \param[out] grad (oc, ic, fh, fw) or (g, ocpg, icpg, fh, fw) | |||
*/ | |||
virtual void exec( | |||
_megdnn_tensor_in src, _megdnn_tensor_in diff, _megdnn_tensor_in rin, | |||
@@ -20,7 +20,7 @@ void RegionRestrictedConvolutionForwardImpl::exec( | |||
src.layout, dst.layout, fm, | |||
param().compute_mode == Param::ComputeMode::DEFAULT); | |||
megdnn_assert( | |||
fm.group > 1 && src.layout.dtype.category() == DTypeCategory::FLOAT && | |||
src.layout.dtype.category() == DTypeCategory::FLOAT && | |||
param().compute_mode == Param::ComputeMode::DEFAULT && | |||
fm.spatial_ndim == 2 && fm.icpg == 1 && fm.ocpg == 1 && | |||
fm.dilation[0] == 1 && fm.dilation[1] == 1 && !fm.should_flip && | |||
@@ -76,7 +76,7 @@ void RegionRestrictedConvolutionBackwardDataImpl::exec( | |||
diff.layout, grad.layout, fm, | |||
param().compute_mode == Param::ComputeMode::DEFAULT); | |||
megdnn_assert( | |||
fm.group > 1 && diff.layout.dtype.category() == DTypeCategory::FLOAT && | |||
diff.layout.dtype.category() == DTypeCategory::FLOAT && | |||
param().compute_mode == Param::ComputeMode::DEFAULT && | |||
fm.spatial_ndim == 2 && fm.icpg == 1 && fm.ocpg == 1 && | |||
fm.dilation[0] == 1 && fm.dilation[1] == 1 && !fm.should_flip && | |||
@@ -120,7 +120,7 @@ void RegionRestrictedConvolutionBackwardFilterImpl::exec( | |||
workspace.size); | |||
megdnn_assert( | |||
fm.group > 1 && src.layout.dtype.category() == DTypeCategory::FLOAT && | |||
src.layout.dtype.category() == DTypeCategory::FLOAT && | |||
param().compute_mode == Param::ComputeMode::DEFAULT && | |||
fm.spatial_ndim == 2 && fm.icpg == 1 && fm.ocpg == 1 && | |||
fm.dilation[0] == 1 && fm.dilation[1] == 1 && !fm.should_flip && | |||
@@ -53,6 +53,7 @@ TEST_F(CUDA, REGION_RESTRICTED_CONV_FORWARD_LARGE_FILTER) { | |||
run(4, 8, 32, 5, 5 / 2, 1); | |||
run(4, 8, 32, 7, 7 / 2, 1); | |||
run(1, 2, 32, 9, 9 / 2, 1); | |||
run(4, 1, 32, 9, 9 / 2, 1); | |||
run(4, 8, 32, 11, 11 / 2, 1); | |||
run(4, 8, 32, 13, 13 / 2, 1); | |||
run(4, 8, 32, 15, 15 / 2, 1); | |||
@@ -723,6 +724,7 @@ TEST_F(CUDA, REGION_RESTRICTED_CONV_BWD_DATA_FP32) { | |||
run(4, 8, 32, 25, 25 / 2, 1); | |||
run(4, 8, 32, 27, 27 / 2, 1); | |||
run(4, 8, 32, 29, 29 / 2, 1); | |||
run(4, 1, 32, 29, 29 / 2, 1); | |||
run(4, 8, 32, 31, 31 / 2, 1); | |||
} | |||
} | |||
@@ -779,6 +781,7 @@ TEST_F(CUDA, REGION_RESTRICTED_CONV_BWD_DATA_FP32_RIN_EQ_ROUT) { | |||
run(4, 8, 32, 21, 21 / 2, 1); | |||
run(4, 8, 32, 23, 23 / 2, 1); | |||
run(4, 8, 32, 25, 25 / 2, 1); | |||
run(4, 1, 32, 25, 25 / 2, 1); | |||
run(4, 8, 32, 27, 27 / 2, 1); | |||
run(4, 8, 32, 29, 29 / 2, 1); | |||
run(4, 8, 32, 31, 31 / 2, 1); | |||
@@ -841,6 +844,7 @@ TEST_F(CUDA, REGION_RESTRICTED_CONV_BWD_FILTER_FP32) { | |||
run(4, 8, 32, 23, 23 / 2, 1); | |||
run(4, 8, 32, 25, 25 / 2, 1); | |||
run(4, 8, 32, 27, 27 / 2, 1); | |||
run(4, 1, 32, 27, 27 / 2, 1); | |||
run(4, 8, 32, 29, 29 / 2, 1); | |||
run(4, 8, 32, 31, 31 / 2, 1); | |||
} | |||
@@ -899,6 +903,7 @@ TEST_F(CUDA, REGION_RESTRICTED_CONV_BWD_FILTER_FP32_RIN_EQ_ROUT) { | |||
run(4, 8, 32, 17, 17 / 2, 1); | |||
run(4, 8, 32, 19, 19 / 2, 1); | |||
run(4, 8, 32, 21, 21 / 2, 1); | |||
run(4, 1, 32, 21, 21 / 2, 1); | |||
run(4, 8, 32, 23, 23 / 2, 1); | |||
run(4, 8, 32, 25, 25 / 2, 1); | |||
run(4, 8, 32, 27, 27 / 2, 1); | |||
@@ -2016,7 +2016,12 @@ def region_restricted_conv( | |||
stride_h, stride_w = expand_hw(stride) | |||
dilate_h, dilate_w = expand_hw(dilation) | |||
sparse_type = "dense" if groups == 1 else "group" | |||
sparse_type = "group" | |||
assert groups > 0, ( | |||
"RegionRestrictedConv expected grouped conv mode, \ | |||
which requires groups > 0, but got groups=%d" | |||
% (groups) | |||
) | |||
op = builtin.RegionRestrictedConvolution( | |||
stride_h=stride_h, | |||
stride_w=stride_w, | |||
@@ -1050,8 +1050,8 @@ class RegionRestrictedConv(_ConvNd): | |||
Refer to :class:`~.module.padding.Pad` for more information. | |||
Note: | |||
* ``weight`` usually has shape ``(out_channels, in_channels, height, width)`` , | |||
if groups is not 1, shape will be ``(groups, out_channels // groups, in_channels // groups, height, width)`` | |||
* weight shape will be ``(groups, out_channels // groups, in_channels // groups, height, width)``, | |||
becasue RegionRestrictedConv support grouped conv only. | |||
Examples: | |||
>>> import numpy as np | |||
@@ -1071,7 +1071,7 @@ class RegionRestrictedConv(_ConvNd): | |||
in_channels: int, | |||
out_channels: int, | |||
kernel_size: Union[int, Tuple[int, int]], | |||
groups: int, | |||
groups: int = 1, | |||
bias: bool = True, | |||
stride: Union[int, Tuple[int, int]] = 1, | |||
padding: Union[int, Tuple[int, int]] = 0, | |||
@@ -1111,9 +1111,6 @@ class RegionRestrictedConv(_ConvNd): | |||
ichl = self.in_channels | |||
ochl = self.out_channels | |||
kh, kw = self.kernel_size | |||
if group == 1: | |||
# Assume format is NCHW | |||
return (ochl, ichl, kh, kw) | |||
assert ( | |||
ichl % group == 0 and ochl % group == 0 | |||
@@ -971,17 +971,16 @@ def test_region_restricted_conv_forward_backward_naive(bias): | |||
@pytest.mark.skipif( | |||
not is_cuda_available(), reason="rrconv cuda kernel requires cuda available" | |||
) | |||
@pytest.mark.parametrize("bias", [True, False]) | |||
def test_region_restricted_conv_forward_backward_cuda(bias): | |||
@pytest.mark.parametrize("bias, groups", [(True, 1), (True, 3), (False, 1), (False, 3)]) | |||
def test_region_restricted_conv_forward_backward_cuda(bias, groups): | |||
import megengine as mge | |||
import megengine.module as M | |||
from megengine.autodiff import GradManager | |||
import megengine.distributed as dist | |||
# params | |||
handle = "gpu0" | |||
N = 1 | |||
GROUP = 3 | |||
GROUP = groups | |||
FH = FW = 2 | |||
IH = IW = 2 | |||
OH = OW = 1 | |||
@@ -1051,8 +1050,8 @@ def test_region_restricted_conv_forward_backward_cuda(bias): | |||
@pytest.mark.skipif( | |||
not is_cuda_available(), reason="rrconv cuda kernel requires cuda available" | |||
) | |||
@pytest.mark.parametrize("bias", [True, False]) | |||
def test_region_restricted_conv_forward_backward_uint8(bias): | |||
@pytest.mark.parametrize("bias, groups", [(True, 1), (True, 3), (False, 1), (False, 3)]) | |||
def test_region_restricted_conv_forward_backward_uint8(bias, groups): | |||
import megengine as mge | |||
import megengine.module as M | |||
from megengine.autodiff import GradManager | |||
@@ -1060,7 +1059,7 @@ def test_region_restricted_conv_forward_backward_uint8(bias): | |||
# params | |||
handle = "gpu0" | |||
N = 1 | |||
GROUP = 2 | |||
GROUP = groups | |||
FH = FW = 1 | |||
IH = IW = 4 | |||
OH = OW = 4 | |||