control_flow case

tests/st/control/test_if_by_if.py

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+import numpy as np
+import pytest
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.common.parameter import Parameter
+from mindspore.nn import Cell
+import mindspore.ops.operations as P
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_if_by_if_basic():
+    class SubNet(Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.TensorAdd()
+            a = np.full((1,), 5, dtype=np.float32)
+            self.a = Parameter(Tensor(a), name='a')
+            b = np.full((1,), 4, dtype=np.float32)
+            self.b = Parameter(Tensor(b), name='b')
+
+        def construct(self, x):
+            if self.a > self.b:
+                x = self.mul(x, 1)
+                while self.b < 6:
+                    x = self.add(x, x)
+                    self.b += 1
+            return x
+
+    class Net(Cell):
+        def __init__(self):
+            super().__init__()
+            context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+            self.subnet = SubNet()
+            self.relu = P.ReLU()
+            self.add = P.TensorAdd()
+            a = np.full((1,), 5, dtype=np.float32)
+            self.a = Parameter(Tensor(a), name='a')
+            b = np.full((1,), 4, dtype=np.float32)
+            self.b = Parameter(Tensor(b), name='b')
+            c = np.full((1,), 7, dtype=np.float32)
+            self.c = Parameter(Tensor(c), name='c')
+
+        def func(self, x):
+            for _ in range(0, 2):
+                x = self.add(x, 0)
+            return x
+
+        def construct(self, x):
+            if self.a > self.b:
+                x = self.subnet(x)
+            else:
+                x = self.relu(x)
+            if self.a < self.c:
+                x = self.func(x)
+            else:
+                x = self.add(x, 2)
+            return x
+
+    input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
+    net = Net()
+    out_ms = net(Tensor(input_np))
+    out_np = input_np * 4
+    assert np.allclose(out_ms.asnumpy(), out_np)