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fix cpu conv2d padding

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pull/7276/head
baihuawei 4 years ago
parent ea5ad3c298
commit ff5fb9d93c
2 changed files with 120 additions and 14 deletions
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18
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.cc
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@ -37,13 +37,17 @@ void MKLCPUKernel::GetPadding(const CNodePtr &kernel_node, const std::string &pa
if (pad_mode == PAD_MODE_LOWER_SAME || pad_mode == PAD_MODE_UPPER_SAME) { if (pad_mode == PAD_MODE_LOWER_SAME || pad_mode == PAD_MODE_UPPER_SAME) {
for (size_t i = 0; i < weight_height.size(); ++i) { for (size_t i = 0; i < weight_height.size(); ++i) {
auto wh = weight_height[i]; auto wh = weight_height[i];
int rad = kernel_size[i] / 2; int re = wh % stride;
int need_pad = kernel_size[i] - 1; if (re == 0) {
int re = (wh - 1) % stride; re = stride;
int pad = std::max(rad - (re / 2), 0); }
padding_r->emplace_back(pad); int pad = kernel_size[i] - re;
pad = std::max(need_pad - pad - re, 0); padding_l->emplace_back(pad / 2);
padding_l->emplace_back(pad); if (pad % 2 == 0) {
padding_r->emplace_back(pad / 2);
} else {
padding_r->emplace_back(pad / 2 + 1);
}
} }
} else if (pad_mode == PAD_MODE_LOWER_VALID || pad_mode == PAD_MODE_UPPER_VALID) { } else if (pad_mode == PAD_MODE_LOWER_VALID || pad_mode == PAD_MODE_UPPER_VALID) {
MS_LOG(INFO) << "pad valid"; MS_LOG(INFO) << "pad valid";

116
tests/st/ops/cpu/test_conv2d_op.py
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@ -55,13 +55,13 @@ def test_conv2d():
conv2d = NetConv2d() conv2d = NetConv2d()
output = conv2d() output = conv2d()
print("================================") print("================================")
# expect output: # expect output:
# [[[[ 45. 48. 51.] # [[[[ 45. 48. 51.]
# [ 54. 57. 60.] # [ 54. 57. 60.]
# [ 63. 66. 69.]] # [ 63. 66. 69.]]
# [[126. 138. 150.] # [[126. 138. 150.]
# [162. 174. 186.] # [162. 174. 186.]
# [198. 210. 222.]]]] # [198. 210. 222.]]]]
expect = np.array([[[[45, 48, 51], expect = np.array([[[[45, 48, 51],
[54, 57, 60], [54, 57, 60],
[63, 66, 69]], [63, 66, 69]],
@ -70,3 +70,105 @@ def test_conv2d():
[198, 210, 222]]]]).astype(np.float32) [198, 210, 222]]]]).astype(np.float32)
print(output) print(output)
assert (output.asnumpy() == expect).all() assert (output.asnumpy() == expect).all()
class NetConv(nn.Cell):
def __init__(self, weight, x):
super(NetConv, self).__init__()
self.conv = nn.Conv2d(in_channels=3,
out_channels=3,
kernel_size=(5, 3),
stride=2,
pad_mode='same',
padding=(0, 0, 0, 0),
dilation=(1, 1),
group=1,
has_bias=False,
weight_init=Tensor(weight)
)
self.x = Parameter(initializer(Tensor(x), [1, 3, 4, 2]), name="x")
def construct(self):
return self.conv(self.x)
def test_conv():
weight = np.array([[[[0.38968208, 0.14398979, 0.7962463],
[-2.1836321, -0.63823014, -0.50588065],
[0.6660469, 0.64673275, -0.13160042],
[1.3683757, 1.4005762, -0.37235805],
[-0.22638111, 0.45427424, -0.10293389]],
[[1.4985064, -0.29318333, -0.92694616],
[1.539068, 0.8937254, -1.2598171],
[0.9658142, -0.63945454, -0.23185322],
[1.363089, -0.41694695, -2.2750475],
[-0.4865508, -1.6938025, 0.609849]],
[[1.1844803, 0.99874926, -1.9475793],
[0.4987858, 0.5307887, -0.04226681],
[0.4529779, -1.1960793, 0.9456575],
[3.133675, 0.2309789, -0.29201075],
[-0.59632736, -0.0789804, -0.69486314]]],
[[[-0.5606142, 0.6420862, 0.2478745],
[0.02717604, 1.5483379, -0.9373383],
[-1.1017276, -0.259478, 1.0311872],
[1.8387799, 0.16468556, 0.33392152],
[-1.8781787, 1.0158662, 1.6527579]],
[[0.45696944, -0.5652523, -1.5618048],
[-0.30304828, 0.1331878, -0.36955845],
[0.91655576, 0.66612357, 0.3068175],
[-0.45732066, 0.8923335, 1.0542952],
[-0.73519516, 1.0518405, -1.0273266]],
[[-0.79712886, -0.26814285, 0.12779616],
[1.0367643, -1.6180774, 0.42999932],
[-0.81818223, -0.81502074, 0.882194],
[0.53640485, 0.4178927, 1.6037121],
[0.9256354, -1.1006796, 0.16614541]]],
[[[-1.5216796, -1.2473261, 0.6549515],
[0.63627815, 0.7221449, 0.02977821],
[-0.61331123, -0.49451825, 0.33852202],
[1.4510741, -1.3818305, -0.791747],
[0.6989747, 0.49558765, 1.0813237]],
[[-0.03969796, 0.71586496, 0.8326594],
[-0.15443641, 1.0389746, -0.59301984],
[0.7197836, 0.03257621, 1.8398637],
[0.6111736, -0.16166899, -2.4869773],
[1.3066711, -1.8003578, 0.17412892]],
[[-0.31470737, -0.5938182, -1.1311078],
[-0.99081016, 0.4005125, 0.44154453],
[1.0876914, -2.5958562, -0.5914863],
[1.3759689, -0.7741513, 0.19928917],
[1.6792973, 2.2744863, -0.04308867]]]]).astype(np.float32)
x = np.array([[[[-1.4311737, 1.015344],
[0.04431088, -2.2886624],
[1.4832113, 1.240908],
[0.67040104, 0.15266363]],
[[0.44226435, 1.1461105],
[1.194218, 1.5547837],
[0.23152256, 1.5911953],
[0.11206784, 0.17978816]],
[[-0.57803905, 0.8039611],
[0.0823025, -0.6134477],
[-1.4171146, 1.6269946],
[0.48878875, 0.9117505]]]]).astype(np.float32)
conv2d = NetConv(weight, x)
output = conv2d()
expected = np.array([[[[2.3498724],
[-1.9199573]],
[[5.376562],
[-5.425745]],
[[5.9105043],
[7.469034]]]]).astype(np.float32)
loss = np.abs(expected - output.asnumpy())
error = 1e-4 * np.ones(loss.shape)
assert (loss < error).all()
test_conv2d()
test_conv()

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