Update testcases for initilizers

tests/st/initializer/test_initializer.py

@@ -15,37 +15,69 @@
 
 import mindspore
 from mindspore.common.initializer import initializer, Identity, Dirac, Sparse, VarianceScaling, Orthogonal
+from mindspore import context
+import mindspore.ops as ops
 import numpy as np
+import pytest
 
 
-def test_sparse():
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_sparse(mode):
     """
     Feature: Test sparse initializer.
     Description: Initialize a 2 dimension sparse matrix to fill the input tensor.
     Expectation: The Tensor is initialized with a 2 dimension sparse matrix.
     """
-    tensor1 = initializer(Sparse(sparsity=0.1, sigma=0.01), [5, 8], mindspore.float32)
-    tensor1.init_data()
+    context.set_context(mode=mode)
+    tensor1 = initializer(Sparse(sparsity=0.2, sigma=0.01), [5, 6], mindspore.float32)
+    output = tensor1.init_data()
+    assert np.array_equal(np.count_nonzero(output.asnumpy(), axis=0), [4, 4, 4, 4, 4, 4])
 
 
-def test_orthogonal():
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_orthogonal(mode):
     """
     Feature: Test orthogonal initializer.
     Description: Initialize a (semi) orthogonal matrix to fill the input tensor.
     Expectation: The Tensor is initialized with values from orthogonal matrix.
     """
-    tensor1 = initializer(Orthogonal(gain=2.), [2, 3, 4], mindspore.float32)
-    tensor2 = initializer('orthogonal', [2, 3, 4], mindspore.float32)
-    tensor1.init_data()
-    tensor2.init_data()
+    context.set_context(mode=mode)
+    identity = np.identity(2)
+    tensor1 = initializer(Orthogonal(gain=1.), [2, 2], mindspore.float32)
+    t1 = tensor1.init_data()
+    transpose = t1.transpose()
+    mul = ops.MatMul()
+    output = mul(t1, transpose)
+    assert np.allclose(output.asnumpy(), identity, atol=1e-6, rtol=1e-7)
 
 
-def test_variancescaling():
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_variancescaling(mode):
     """
     Feature: Test varianceScaling initializer.
     Description: Randomly initialize an array with scaling to fill the input tensor.
     Expectation: The Tensor is initialized successfully.
     """
+    context.set_context(mode=mode)
+    mindspore.set_seed(0)
     tensor1 = initializer('varianceScaling', [2, 3], mindspore.float32)
     tensor2 = initializer(VarianceScaling(scale=1.0, mode='fan_out', distribution='untruncated_normal'), [2, 3],
                           mindspore.float32)
@@ -53,18 +85,38 @@ def test_variancescaling():
                           mindspore.float32)
     tensor4 = initializer(VarianceScaling(scale=3.0, mode='fan_avg', distribution='uniform'), [2, 3],
                           mindspore.float32)
-    tensor1.init_data()
-    tensor2.init_data()
-    tensor3.init_data()
-    tensor4.init_data()
+    t1 = tensor1.init_data()
+    expected_t1 = np.array([[0.49535394, -0.03666719, 0.23151064],
+                            [-0.08424897, 0.39260703, -0.26104233]])
+    t2 = tensor2.init_data()
+    expected_t2 = np.array([[1.2710124e+00, 1.2299923e-03, -1.1589712e+00],
+                            [1.1465757e+00, -2.2482322e-01, 9.2637345e-02]])
+    t3 = tensor3.init_data()
+    expected_t3 = np.array([[1.2023407, -0.9182362, 0.20436235],
+                            [0.8581208, 1.0288558, 1.0927733]])
+    t4 = tensor4.init_data()
+    expected_t4 = np.array([[1.2470493, -1.0861205, -1.1339132],
+                            [-0.07604776, -1.8196303, 0.5115674]])
+    assert np.allclose(t1.asnumpy(), expected_t1)
+    assert np.allclose(t2.asnumpy(), expected_t2)
+    assert np.allclose(t3.asnumpy(), expected_t3)
+    assert np.allclose(t4.asnumpy(), expected_t4)
 
 
-def test_identity():
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_identity(mode):
     """
     Feature: Test identity initializer.
     Description: Initialize an identity matrix to fill a Tensor.
     Expectation: The Tensor is initialized with identity matrix.
     """
+    context.set_context(mode=mode)
     tensor1 = initializer(Identity(), [3, 3], mindspore.float32)
     tensor2 = initializer('identity', [3, 4], mindspore.float32)
     tensor3 = initializer('identity', [4, 3], mindspore.float32)
@@ -76,12 +128,20 @@ def test_identity():
     assert (tensor3.asnumpy() == expect3).all()
 
 
-def test_dirac():
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_dirac(mode):
     """
     Feature: Test dirac initializer.
     Description: Initialize input tensor with the Dirac delta function.
     Expectation: The Tensor is correctly initialized.
     """
+    context.set_context(mode=mode)
     tensor3_1 = initializer(Dirac(groups=1), [6, 2, 3], mindspore.float32)
     tensor3_2 = initializer(Dirac(groups=2), [6, 2, 3], mindspore.float32)
     tensor3_3 = initializer(Dirac(groups=3), [6, 2, 3], mindspore.float32)