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batchnorm2d use_batch_statistics false test case

This commit is contained in:
jonwe 2020-11-19 16:23:23 -05:00
parent e3b1814401
commit 036e28bb96
1 changed files with 55 additions and 6 deletions
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61
tests/st/ops/gpu/test_batchnorm_op.py
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@ -24,10 +24,11 @@ from mindspore.ops import composite as C
class Batchnorm_Net(Cell): class Batchnorm_Net(Cell):
def __init__(self, c, weight, bias, moving_mean, moving_var_init): def __init__(self, c, weight, bias, moving_mean, moving_var_init, use_batch_statistics=None):
super(Batchnorm_Net, self).__init__() super(Batchnorm_Net, self).__init__()
self.bn = BatchNorm2d(c, eps=0.00001, momentum=0.1, beta_init=bias, gamma_init=weight, self.bn = BatchNorm2d(c, eps=0.00001, momentum=0.1, beta_init=bias, gamma_init=weight,
moving_mean_init=moving_mean, moving_var_init=moving_var_init) moving_mean_init=moving_mean, moving_var_init=moving_var_init,
use_batch_statistics=use_batch_statistics)
def construct(self, input_data): def construct(self, input_data):
x = self.bn(input_data) x = self.bn(input_data)
@ -69,7 +70,8 @@ def test_train_forward():
error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4 error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init)) bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias),
Tensor(moving_mean), Tensor(moving_var_init))
bn_net.set_train() bn_net.set_train()
output = bn_net(Tensor(x)) output = bn_net(Tensor(x))
diff = output.asnumpy() - expect_output diff = output.asnumpy() - expect_output
@ -77,7 +79,8 @@ def test_train_forward():
assert np.all(-diff < error) assert np.all(-diff < error)
context.set_context(mode=context.GRAPH_MODE, device_target="GPU") context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init)) bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias),
Tensor(moving_mean), Tensor(moving_var_init))
bn_net.set_train() bn_net.set_train()
output = bn_net(Tensor(x)) output = bn_net(Tensor(x))
diff = output.asnumpy() - expect_output diff = output.asnumpy() - expect_output
@ -85,12 +88,14 @@ def test_train_forward():
assert np.all(-diff < error) assert np.all(-diff < error)
context.set_context(mode=context.GRAPH_MODE, device_target="GPU") context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init)) bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias),
Tensor(moving_mean), Tensor(moving_var_init))
bn_net.set_train(False) bn_net.set_train(False)
output = bn_net(Tensor(x)) output = bn_net(Tensor(x))
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init)) bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias),
Tensor(moving_mean), Tensor(moving_var_init))
bn_net.set_train(False) bn_net.set_train(False)
output = bn_net(Tensor(x)) output = bn_net(Tensor(x))
@ -129,3 +134,47 @@ def test_train_backward():
diff = output[0].asnumpy() - expect_output diff = output[0].asnumpy() - expect_output
assert np.all(diff < error) assert np.all(diff < error)
assert np.all(-diff < error) assert np.all(-diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_train_stats_false_forward():
x = np.array([[
[[1, 3, 3, 5], [2, 4, 6, 8], [3, 6, 7, 7], [4, 3, 8, 2]],
[[5, 7, 6, 3], [3, 5, 6, 7], [9, 4, 2, 5], [7, 5, 8, 1]]]]).astype(np.float32)
expect_output = np.array([[[[3.707105, 5.121315, 5.121315, 6.535525],
[4.41421, 5.8284197, 7.24263, 8.656839],
[5.121315, 7.24263, 7.9497347, 7.9497347],
[5.8284197, 5.121315, 8.656839, 4.41421]],
[[6.535525, 7.9497347, 7.24263, 5.121315],
[5.121315, 6.535525, 7.24263, 7.9497347],
[9.363945, 5.8284197, 4.41421, 6.535525],
[7.9497347, 6.535525, 8.656839, 3.707105]]]]).astype(np.float32)
weight = np.ones(2).astype(np.float32)
bias = np.ones(2).astype(np.float32) * 3
moving_mean = np.zeros(2).astype(np.float32)
moving_var_init = np.ones(2).astype(np.float32) * 2
error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
use_batch_statistics = False
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean),
Tensor(moving_var_init), use_batch_statistics)
bn_net.set_train()
output = bn_net(Tensor(x))
diff = output.asnumpy() - expect_output
assert np.all(diff < error)
assert np.all(-diff < error)
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean),
Tensor(moving_var_init), use_batch_statistics)
bn_net.set_train()
output = bn_net(Tensor(x))
diff = output.asnumpy() - expect_output
assert np.all(diff < error)
assert np.all(-diff < error)