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!35863 Modify the channel from 8 to 64 in deformable offset grad test 

Merge pull request !35863 from guoqi/deformable_offsets_grad
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i-robot 2022-06-14 02:47:07 +00:00 committed by Gitee
parent e057782637 e67d508414
commit 067212500a
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1 changed files with 14 additions and 40 deletions
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54
tests/st/ops/ascend/test_tbe_ops/test_deformable_offsets_grad.py
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@ -59,7 +59,7 @@ class Grad(nn.Cell):
return self.grad(self.network)(x, w, offset, output_grad)
@pytest.mark.level1
@pytest.mark.level0
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.env_onecard
@ -74,10 +74,10 @@ def test_deformable_conv2d_grad():
stride = 1
pad = 0
dilation = 1
# x shape [1, 8, 2, 2]
x = Tensor(np.ones([1, 8, 2, 2]).astype(np.float32) * 0.1)
# weight shape [1, 8, 2, 2]
weight = Tensor(np.ones([1, 8, 2, 2]).astype(np.float32) * 0.1)
# x shape [1, 64, 2, 2]
x = Tensor(np.ones([1, 64, 2, 2]).astype(np.float32) * 0.1)
# weight shape [1, 64, 2, 2]
weight = Tensor(np.ones([1, 64, 2, 2]).astype(np.float32) * 0.1)
# offsets shape [1, 12, 1, 1]
offsets = Tensor(np.ones([1, 12, 1, 1]).astype(np.float32) * 0.1)
# out_channel, kernel_size, pad, stride, dilation
@ -85,39 +85,13 @@ def test_deformable_conv2d_grad():
out = dfm_conv2d_net(x, weight, offsets)
grad_net = Grad(dfm_conv2d_net)
grad_output = grad_net(x, weight, offsets, out)
expected_out = np.array([[[[0.02888089]]]]).astype(np.float32)
expect_grad_x = np.array([[[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]],
[[0.00023391, 0.0002599],
[0.0002599, 0.00028877]]]]).astype(np.float32)
expect_grad_offset = np.array([[[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]],
[[0.00028891, 0.00026004],
[0.00026004, 0.00023404]]]]).astype(np.float32)
assert np.allclose(out.asnumpy(), expected_out, 0.0001, 0.0001)
expect_out = np.array([[[[0.2310471]]]]).astype(np.float32)
expect_grad_x = np.array([[[[0.00187125, 0.00207916], [0.00207916, 0.00231018]]] * 64]).astype(np.float32)
expect_grad_weight = np.array([[[[0.00231128, 0.00208033], [0.00208033, 0.0018723]]] * 64]).astype((np.float32))
expect_grad_offset = np.array([[[0]], [[-0.01478]], [[0]], [[-0.01331]],
[[0]], [[0]], [[-0.01478]], [[-0.01331]],
[[0.14785]], [[0.13307]], [[0.13307]], [[0.11976]]]).astype((np.float32))
assert np.allclose(out.asnumpy(), expect_out, 0.0001, 0.0001)
assert np.allclose(grad_output[0].asnumpy(), expect_grad_x, 0.0001, 0.0001)
assert np.allclose(grad_output[1].asnumpy(), expect_grad_offset, 0.0001, 0.0001)
assert np.allclose(grad_output[1].asnumpy(), expect_grad_weight, 0.0001, 0.0001)
assert np.allclose(grad_output[2].asnumpy(), expect_grad_offset, 0.0001, 0.0001)