add tesecases of Tensor

tests/st/fallback/test_graph_fallback.py

@@ -160,3 +160,19 @@ def test_np_fallback_func_tensor_index():
     output = np_fallback_func_tensor_index(x)
     output_expect = Tensor(6, mstype.float32)
     assert output == output_expect
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_np_calculate():
+    """
+    Feature: Fallback feature.
+    Description: Support numpy calculation.
+    Expectation: No exception.
+    """
+    @ms_function
+    def np_calculate():
+        x = np.array([3, 1, 2, 4, 5])
+        y = x % 2
+        z = Tensor(y)
+        return z
+    assert np.all(np_calculate().asnumpy() == np.array([1, 1, 0, 0, 1]))

tests/ut/python/fallback/test_graph_fallback.py

@@ -54,21 +54,6 @@ def test_use_monad():
     print(use_monad(x, y))
 
 
-@ms_function
-def use_tensor_with_mstype():
-    me_x = Tensor(1, mstype.int32)
-    return me_x
-
-
-def test_tensor_with_mstype():
-    """
-    Feature: JIT Fallback
-    Description: Test tensor with mstype in graph mode.
-    Expectation: No exception.
-    """
-    print(use_tensor_with_mstype())
-
-
 @ms_function
 def use_tuple_of_tensor():
     me_x = (Tensor(1), Tensor(1))
@@ -244,11 +229,7 @@ def test_np_tensor_add():
     print("tensor_list:", tensor_list)
     assert tensor_list[-1] == 11
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
+
 def test_binop_new_tensor():
     """
     Feature: Fallback feature
@@ -268,11 +249,6 @@ def test_binop_new_tensor():
     print(net())
 
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
 def test_fallback_tensor_compare():
     """
     Feature: Fallback feature
@@ -293,11 +269,6 @@ def test_fallback_tensor_compare():
     print(compare_net())
 
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
 def test_fallback_tensor_not():
     """
     Feature: Fallback feature

tests/ut/python/fallback/test_graph_fallback_tensor.py

@@ -0,0 +1,215 @@
+import pytest
+import numpy as np
+from mindspore import Tensor, ms_function, context
+import mindspore.common.dtype as mstype
+from mindspore.common.initializer import One
+
+
+context.set_context(mode=context.GRAPH_MODE)
+
+def test_tensor():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor(1)
+        return me_x
+    print(foo())
+
+
+def test_tensor_bool():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor(bool) in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor([True, True, False])
+        return me_x
+    print(foo())
+
+
+def test_tensor_array():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor(array) in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor([1])
+        return me_x
+    print(foo())
+
+
+def test_tensor_with_mstype():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor() with mstype in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor(1, mstype.int32)
+        return me_x
+    print(foo())
+
+
+def test_tensor_array_with_mstype():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor(array) with mstype in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor([1], mstype.int32)
+        return me_x
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_array_astype():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor(array) with astype() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor([1.1, -2.1]).astype("float32")
+        return me_x
+    print(foo())
+
+
+def test_tensor_with_numpy():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor() with numpy in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor(np.zeros([1, 2, 3]), mstype.float32)
+        return me_x
+    print(foo())
+
+
+def test_tensor_with_init():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor() with init in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor(shape=(1, 3), dtype=mstype.float32, init=One())
+        return me_x
+    print(foo())
+
+
+def test_tensor_reshape():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor() with reshape() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        me_x = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32"))
+        return me_x
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_abs():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor.abs() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        a = Tensor([1.1, -2.1]).astype("float32")
+        out = a.abs()
+        return out
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_all():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor.all() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        a = Tensor([True, True, False])
+        out = a.all()
+        return out
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_any():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor.any() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        a = Tensor([True, True, False])
+        out = a.any()
+        return out
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_argmax():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor.argmax() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        a = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32"))
+        out = a.argmax()
+        return out
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_argmin():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor.argmin() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        a = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32"))
+        out = a.argmin()
+        return out
+    print(foo())
+
+
+@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
+def test_tensor_astype():
+    """
+    Feature: JIT Fallback
+    Description: Test Tensor.astype() in graph mode.
+    Expectation: No exception.
+    """
+    @ms_function
+    def foo():
+        a = Tensor(np.ones((1, 2, 2, 1), dtype=np.float32))
+        out = a.astype("int32")
+        return out
+    print(foo())