forked from mindspore-Ecosystem/mindspore
fix cpu conv2d padding
This commit is contained in:
parent
ea5ad3c298
commit
ff5fb9d93c
|
|
@ -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) {
|
||||
for (size_t i = 0; i < weight_height.size(); ++i) {
|
||||
auto wh = weight_height[i];
|
||||
int rad = kernel_size[i] / 2;
|
||||
int need_pad = kernel_size[i] - 1;
|
||||
int re = (wh - 1) % stride;
|
||||
int pad = std::max(rad - (re / 2), 0);
|
||||
padding_r->emplace_back(pad);
|
||||
pad = std::max(need_pad - pad - re, 0);
|
||||
padding_l->emplace_back(pad);
|
||||
int re = wh % stride;
|
||||
if (re == 0) {
|
||||
re = stride;
|
||||
}
|
||||
int pad = kernel_size[i] - re;
|
||||
padding_l->emplace_back(pad / 2);
|
||||
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) {
|
||||
MS_LOG(INFO) << "pad valid";
|
||||
|
|
|
|||
|
|
@ -55,13 +55,13 @@ def test_conv2d():
|
|||
conv2d = NetConv2d()
|
||||
output = conv2d()
|
||||
print("================================")
|
||||
# expect output:
|
||||
# [[[[ 45. 48. 51.]
|
||||
# [ 54. 57. 60.]
|
||||
# [ 63. 66. 69.]]
|
||||
# [[126. 138. 150.]
|
||||
# [162. 174. 186.]
|
||||
# [198. 210. 222.]]]]
|
||||
# expect output:
|
||||
# [[[[ 45. 48. 51.]
|
||||
# [ 54. 57. 60.]
|
||||
# [ 63. 66. 69.]]
|
||||
# [[126. 138. 150.]
|
||||
# [162. 174. 186.]
|
||||
# [198. 210. 222.]]]]
|
||||
expect = np.array([[[[45, 48, 51],
|
||||
[54, 57, 60],
|
||||
[63, 66, 69]],
|
||||
|
|
@ -70,3 +70,105 @@ def test_conv2d():
|
|||
[198, 210, 222]]]]).astype(np.float32)
|
||||
print(output)
|
||||
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()
|
||||
|
|
|
|||
Loading…
Reference in New Issue