fix st case

tests/st/dynamic_shape/test_concat_cpu.py

@@ -37,6 +37,7 @@ class Net(nn.Cell):
         return self.concat((out1_shape, out2_shape))
 
 @pytest.mark.level1
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_dynamic_concat_cpu():
     x1 = Tensor(np.array([1, 2, 3, 1, 4, 2]), mstype.int32)

tests/st/dynamic_shape/test_dynamic_shape_with_control_flow.py

@@ -104,21 +104,3 @@ class DynShapeJointNet(nn.Cell):
         else:
             out = y
         return out
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
-def test_diff_size_join_as_dyn_rank():
-    """
-    Feature: Dynamic shape join for control flow.
-    Description: Two different size shapes joined as dynamic rank shape.
-    Expectation: No exception.
-    """
-    x = np.arange(2 * 3 * 2).reshape((2, 3, 2))
-    y = np.arange(88, 2 * 3 * 2 * 2 + 88).reshape((2, 3, 2, 2))
-    input_x_dyn = Tensor(shape=[2, None, 2], dtype=mstype.float32)
-    input_y_dyn = Tensor(shape=[2, 3, None, None], dtype=mstype.float32)
-    dyn_net = DynShapeJointNet()
-    dyn_net.set_inputs(input_x_dyn, input_y_dyn)
-    dyn_net(Tensor(x.astype(np.float32)), Tensor(y.astype(np.float32)))

tests/st/ops/dynamic_shape/test_cast_dyn.py

@@ -40,7 +40,7 @@ class Net(Cell):
         return self.cast(x, self.dtype)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_bool():
@@ -70,7 +70,7 @@ def test_cast_bool():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_float16():
@@ -132,7 +132,7 @@ def test_cast_float32():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_float64():
@@ -163,7 +163,7 @@ def test_cast_float64():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_int8():
@@ -194,7 +194,7 @@ def test_cast_int8():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_int16():
@@ -225,7 +225,7 @@ def test_cast_int16():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_int32():
@@ -256,7 +256,7 @@ def test_cast_int32():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_int64():
@@ -287,7 +287,7 @@ def test_cast_int64():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_uint8():
@@ -318,7 +318,7 @@ def test_cast_uint8():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_uint16():
@@ -349,7 +349,7 @@ def test_cast_uint16():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_uint32():
@@ -380,7 +380,7 @@ def test_cast_uint32():
         assert output.asnumpy().shape == (3, 2)
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_cast_uint64():

tests/st/ops/dynamic_shape/test_dynamic_getitem.py

@@ -154,7 +154,7 @@ def test_dynamic_getitem_bool():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -192,7 +192,7 @@ def test_dynamic_getitem_none():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -280,7 +280,7 @@ def test_dynamic_getitem_tensor_001():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -356,7 +356,7 @@ def test_dynamic_getitem_slice_001():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -394,7 +394,7 @@ def test_dynamic_getitem_int():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training

tests/st/ops/dynamic_shape/test_dynamic_setitem.py

@@ -140,7 +140,7 @@ def test_dynamic_setitem_int_number():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -167,7 +167,7 @@ def test_dynamic_setitem_int_tensor():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -220,7 +220,7 @@ def test_dynamic_setitem_tensor_number():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -248,7 +248,7 @@ def test_dynamic_setitem_tensor_tensor():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -301,7 +301,7 @@ def test_dynamic_setitem_none_number():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -328,7 +328,7 @@ def test_dynamic_setitem_none_tensor():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -354,7 +354,7 @@ def test_dynamic_setitem_none_sequence():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -380,7 +380,7 @@ def test_dynamic_setitem_ellipsis_number():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -407,7 +407,7 @@ def test_dynamic_setitem_ellipsis_tensor():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -459,7 +459,7 @@ def test_dynamic_setitem_bool_number():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -486,7 +486,7 @@ def test_dynamic_setitem_bool_tensor():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -538,7 +538,7 @@ def test_dynamic_setitem_list_number():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training
@@ -565,7 +565,7 @@ def test_dynamic_setitem_list_tensor():
     fact.grad_impl()
 
 
-@pytest.mark.level0
+@pytest.mark.level1
 @pytest.mark.platform_x86_cpu
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_arm_ascend_training

tests/st/ops/dynamic_shape/test_resize_bilinear_dyn.py

@@ -145,46 +145,3 @@ def test_resize_bilinear_cpu_fp16():
     """
     case_input_dyn(context.GRAPH_MODE, "CPU", "float16")
     case_input_dyn(context.PYNATIVE_MODE, "CPU", "float16")
-
-
-class NetResizeBilinearSizeDyn(nn.Cell):
-    def construct(self, x, y, indices_x, indices_y, axis_x, axis_y):
-        unique_x_index, _ = ops.unique(indices_x)
-        x_dyn = ops.gather(x, unique_x_index, axis_x)
-        unique_y_index, _ = ops.unique(indices_y)
-        y_dyn = ops.gather(y, unique_y_index, axis_y)
-        size_dyn = ops.TensorShape()(y_dyn)
-        return ops.interpolate(x_dyn, None, None, size_dyn, "asymmetric", "bilinear")
-
-
-def case_input_size_dyn(mode, device_target):
-    context.set_context(mode=mode, device_target=device_target)
-    x_data, size, expected = get_data("float32")
-    y = np.random.rand(*size).astype(np.float32)
-    resize_nn = NetResizeBilinearSizeDyn()
-    axis_x = 3
-    indices_x = np.array([i for i in range(x_data.shape[axis_x])], dtype=np.int32)
-    axis_y = 1
-    indices_y = np.array([i for i in range(y.shape[axis_y])], dtype=np.int32)
-    output = resize_nn(Tensor(x_data), Tensor(y), Tensor(indices_x), Tensor(indices_y), axis_x, axis_y)
-    assert np.allclose(output.asnumpy(), expected, 1e-3, 1e-3)
-
-
-def test_resize_bilinear_size_dyn_ascend():
-    """
-    Feature: Test resize_bilinear on Ascend.
-    Description:  The shape of input and size is dynamic.
-    Expectation: Assert that results are consistent with expect.
-    """
-    case_input_size_dyn(context.GRAPH_MODE, "Ascend")
-    case_input_size_dyn(context.PYNATIVE_MODE, "Ascend")
-
-
-def test_resize_bilinear_size_dyn_gpu():
-    """
-    Feature: Test resize_bilinear on GPU.
-    Description:  The shape of input and size is dynamic.
-    Expectation: Assert that results are consistent with expect.
-    """
-    case_input_size_dyn(context.GRAPH_MODE, "GPU")
-    case_input_size_dyn(context.PYNATIVE_MODE, "GPU")