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@@ -44,6 +44,10 @@ class NetWorkSlicePositive(Cell): |
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return ret0, ret1, ret2, ret3 |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_slice_positive(): |
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net = NetWorkSlicePositive() |
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input_np = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32) |
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@@ -143,7 +147,12 @@ class TensorGetItemByThreeTensors(Cell): |
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return ret0, ret1, ret2 |
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def test_getitem_by_tensors(): |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def Xtest_getitem_by_tensors(): |
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"""This testcase may encounter a sync stream error occassionally""" |
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net = TensorGetItemByThreeTensors() |
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input_x = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32) |
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index_0 = np.random.randint(6, size=(3, 4, 5)).astype(np.int32) |
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@@ -179,6 +188,10 @@ class TensorGetItemByMixedTensorsBasicCase(Cell): |
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return ret0, ret1, ret2, ret3, ret4, ret5 |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_getitem_by_mixed_tensors(): |
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const0 = np.ones((3, 4, 5, 3), np.float32) |
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const1 = np.ones((3, 3, 4, 5, 5), np.float32) |
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@@ -217,6 +230,10 @@ class TensorSetItemByMixedTensors_0(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_mixed_tensors_0(): |
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value = 88.0 |
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net = TensorSetItemByMixedTensors_0(value) |
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@@ -247,6 +264,10 @@ class TensorSetItemByMixedTensors_1(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_mixed_tensors_1(): |
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value = 88.0 |
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net = TensorSetItemByMixedTensors_1(value) |
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@@ -277,6 +298,10 @@ class TensorSetItemByMixedTensors_2(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_mixed_tensors_2(): |
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value = 88.0 |
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net = TensorSetItemByMixedTensors_2(value) |
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@@ -324,6 +349,10 @@ class TensorSetItemByOneTensorWithNumber(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_one_tensor_with_number(): |
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value = 0.0 |
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net = TensorSetItemByOneTensorWithNumber(value) |
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@@ -348,6 +377,10 @@ class TensorSetItemByOneTensorWithTensor(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_one_tensor_with_tensor(): |
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net = TensorSetItemByOneTensorWithTensor() |
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index_np = np.random.randint(4, size=(5, 4)) |
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@@ -374,6 +407,10 @@ class TensorSetItemByOneTensorWithTupleOfNumber(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_one_tensor_with_tuple_number(): |
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value = (0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7) |
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net = TensorSetItemByOneTensorWithTupleOfNumber(value) |
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@@ -398,6 +435,10 @@ class TensorSetItemByOneTensorWithTupleOfTensor(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_one_tensor_with_tuple_tensors(): |
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net = TensorSetItemByOneTensorWithTupleOfTensor() |
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input_np = np.random.randint(6, size=(5, 4)).astype(np.int32) |
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@@ -428,6 +469,10 @@ class TensorSetItemByTensorsWithNumber(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_tensors_with_number(): |
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value = 0.0 |
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net = TensorSetItemByTensorsWithNumber(value) |
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@@ -456,6 +501,10 @@ class TensorSetItemByTensorsWithTensor(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_tensors_with_tensor(): |
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net = TensorSetItemByTensorsWithTensor() |
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index_0 = np.random.randint(6, size=(3, 4, 5)) |
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@@ -485,6 +534,10 @@ class TensorSetItemByTensorsWithTensorNumberError(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_tensors_with_tensor_error(): |
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index_0 = Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32) |
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index_1 = Tensor(np.random.randint(7, size=(4, 5)), mstype.int32) |
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@@ -509,6 +562,10 @@ class TensorSetItemByTensorsWithTupleOfNumber(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_tensors_with_tuple_of_number(): |
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value = (0.0, 1.1, 2.2, 3.3, 4.4) |
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net = TensorSetItemByTensorsWithTupleOfNumber(value) |
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@@ -537,6 +594,10 @@ class TensorSetItemByTensorsWithTupleOfTensor(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_tensors_with_tuple_of_tensor(): |
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value_0 = np.zeros((4, 5)) |
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value_1 = np.ones((4, 5)) |
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@@ -570,6 +631,10 @@ class TensorSetItemByTensorsWithTupleOfTensorNumberError(Cell): |
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return ret |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_setitem_by_tensor_with_tuple_of_tensor_error(): |
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net = TensorSetItemByTensorsWithTupleOfTensorNumberError() |
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index_0_ms = Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32) |
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@@ -661,6 +726,10 @@ class TensorAssignWithSlice(Cell): |
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return z |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_tensor_assign_slice_value_1(): |
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net = TensorAssignWithSlice() |
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a = np.arange(60).reshape(3, 4, 5) |
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@@ -682,6 +751,10 @@ def test_tensor_assign_slice_value_1(): |
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assert np.all(z == out.asnumpy()) |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_tensor_assign_slice_value_2(): |
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net2 = TensorAssignWithSlice2() |
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a = np.array([1, 2, 3, 4, 5, 6, 7, 8]) |
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@@ -701,6 +774,10 @@ def test_tensor_assign_slice_value_2(): |
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assert np.all(z == out.asnumpy()) |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_tensor_assign_exception(): |
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net = TensorAssignWithSlice() |
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net2 = TensorAssignWithSlice2() |
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@@ -886,6 +963,10 @@ class TensorAssignWithBoolTensorIndex2Error(Cell): |
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return a |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_tensor_assign_bool_index_0(): |
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a = np.arange(60).reshape(3, 4, 5) |
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b = a > 5 |
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@@ -903,6 +984,10 @@ def test_tensor_assign_bool_index_0(): |
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assert np.all(out.asnumpy() == res) |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_tensor_assign_bool_index_1(): |
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a = np.arange(60).reshape(3, 4, 5) |
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Ta = Tensor(a, dtype=mstype.float32) |
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@@ -992,6 +1077,10 @@ def Xtest_tensor_slice_reduce_out_of_bounds_positive(): |
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assert "For 'StridedSlice' the `begin[0]` should be an int and must less than 6, but got `6`" in str(ex.value) |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_tensor_range(): |
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a = np.arange(4*5*6).reshape(4, 5, 6).astype(np.float32) |
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ta = Tensor(a, mstype.float32) |
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