transpose dynamic test case using set_inputs

tests/st/ops/dynamic_shape/test_transpose_dyn.py

@@ -22,75 +22,74 @@ import mindspore.ops as ops
 from mindspore import Tensor
 
 
-class TransposeDynNet(nn.Cell):
-    def __init__(self, axis=0):
-        super(TransposeDynNet, self).__init__()
-        self.unique = ops.Unique()
-        self.gather = ops.Gather()
+class Net(nn.Cell):
+    def __init__(self, perm_in):
+        super(Net, self).__init__()
         self.transpose = ops.Transpose()
-        self.axis = axis
+        self.perm = perm_in
 
-    def construct(self, x, perm, indices):
-        unique_indices, _ = self.unique(indices)
-        input_x = self.gather(x, unique_indices, self.axis)
-        return self.transpose(input_x, perm)
+    def construct(self, input_):
+        x = self.transpose(input_, self.perm)
+        return x
 
 
 def dyn_case():
     perm = (1, 0, 2)
-    x = np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(np.float32)
-    indices = np.array([0, 1, 0], dtype=np.int32)
-    expect = np.array([[[[0, 1, 2, 3],
-                         [8, 9, 10, 11]],
-                        [[4, 5, 6, 7],
-                         [12, 13, 14, 15]]]]).astype(np.float32)
+    in_shape = (2, 4, 8)
+    np_value = np.random.uniform(0, 20, size=in_shape).astype(np.float16)
+    real_input = Tensor(np_value)
 
-    net = TransposeDynNet()
-    output = net(Tensor(x), perm, Tensor(indices))
-    assert (output.asnumpy() == expect).all()
+    # dynamic transpose
+    dyn_transpose = Net(perm)
+    dyn_input = Tensor(shape=[None for _ in real_input.shape], dtype=real_input.dtype)
+    dyn_transpose.set_inputs(dyn_input)
+    dyn_out = dyn_transpose(real_input)
+
+    # static transpose
+    static_transpose = Net(perm)
+    static_out = static_transpose(real_input)
+
+    np.allclose(dyn_out.asnumpy(), static_out.asnumpy(), 1e-6, 1e-6)
 
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
-def test_transpose_dyn_cpu():
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_transpose_dyn_cpu(mode):
     """
     Feature: test Transpose dynamic shape on CPU.
     Description: inputs is dynamic shape.
     Expectation: the result match with numpy result
     """
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
-    dyn_case()
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    context.set_context(mode=mode, device_target="CPU")
     dyn_case()
 
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
-def test_transpose_dyn_gpu():
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_transpose_dyn_gpu(mode):
     """
     Feature: test Transpose dynamic shape on GPU.
     Description: inputs is dynamic shape.
     Expectation: the result match with numpy result
     """
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
-    dyn_case()
-    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    context.set_context(mode=mode, device_target="GPU")
     dyn_case()
 
 
-@pytest.mark.level1
+@pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
-def test_transpose_dyn_ascend():
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_transpose_dyn_ascend(mode):
     """
     Feature: test Transpose dynamic shape on Ascend.
     Description: inputs is dynamic shape.
     Expectation: the result match with numpy result
     """
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
-    dyn_case()
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    context.set_context(mode=mode, device_target="Ascend")
     dyn_case()