enable (PaddlePaddle#2066)

Co-authored-by: Yuqiang Ge <[email protected]>

Author: zrr1999

backends/npu/tests/unittests/test_activation_op.py

@@ -159,90 +159,90 @@ def ref_leaky_relu(x, alpha=0.01):
     return out
 
 
-# class TestLeakyRelu(TestActivation):
-#     def get_alpha(self):
-#         return 0.02
-
-#     def setUp(self):
-#         self.set_npu()
-#         self.op_type = "leaky_relu"
-#         self.init_dtype()
-#         self.init_shape()
-#         alpha = self.get_alpha()
-
-#         np.random.seed(1024)
-#         x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
-#         # The same reason with TestAbs
-#         x[np.abs(x) < 0.005] = 0.05
-#         out = ref_leaky_relu(x, alpha)
-
-#         self.inputs = {"X": x}
-#         self.outputs = {"Out": out}
-#         self.attrs = {"alpha": alpha}
-
-#     def test_check_grad(self):
-#         if self.dtype == np.float16:
-#             return
-#         self.check_grad_with_place(self.place, ["X"], "Out")
-
-
-# class TestLeakyReluAlpha1(TestLeakyRelu):
-#     def get_alpha(self):
-#         return 2
-
-
-# class TestLeakyReluAlpha2(TestLeakyRelu):
-#     def get_alpha(self):
-#         return -0.01
-
-
-# class TestLeakyReluAlpha3(TestLeakyRelu):
-#     def get_alpha(self):
-#         return -2.0
-
-
-# class TestLeakyRelu_ZeroDim(TestLeakyRelu):
-#     def init_shape(self):
-#         self.shape = []
-
-
-# class TestLeakyReluAPI(unittest.TestCase):
-#     # test paddle.nn.LeakyReLU, paddle.nn.functional.leaky_relu,
-#     def setUp(self):
-#         np.random.seed(1024)
-#         self.x_np = np.random.uniform(-1, 1, [10, 12]).astype("float32")
-#         self.place = paddle.CustomPlace("npu", 0)
-
-#     def test_static_api(self):
-#         paddle.enable_static()
-#         with paddle.static.program_guard(paddle.static.Program()):
-#             x = paddle.static.data("X", [10, 12])
-#             out1 = F.leaky_relu(x)
-#             m = paddle.nn.LeakyReLU()
-#             out2 = m(x)
-#             exe = paddle.static.Executor(self.place)
-#             res = exe.run(feed={"X": self.x_np}, fetch_list=[out1, out2])
-#         out_ref = ref_leaky_relu(self.x_np)
-#         for r in res:
-#             np.testing.assert_allclose(out_ref, r, rtol=1e-05)
-
-#     def test_dygraph_api(self):
-#         paddle.disable_static(self.place)
-#         x = paddle.to_tensor(self.x_np)
-#         out1 = F.leaky_relu(x)
-#         m = paddle.nn.LeakyReLU()
-#         out2 = m(x)
-#         out_ref = ref_leaky_relu(self.x_np)
-#         for r in [out1, out2]:
-#             np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
-
-#         out1 = F.leaky_relu(x, 0.6)
-#         m = paddle.nn.LeakyReLU(0.6)
-#         out2 = m(x)
-#         out_ref = ref_leaky_relu(self.x_np, 0.6)
-#         for r in [out1, out2]:
-#             np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
-#         paddle.enable_static()
+class TestLeakyRelu(TestActivation):
+    def get_alpha(self):
+        return 0.02
+
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "leaky_relu"
+        self.init_dtype()
+        self.init_shape()
+        alpha = self.get_alpha()
+
+        np.random.seed(1024)
+        x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        # The same reason with TestAbs
+        x[np.abs(x) < 0.005] = 0.05
+        out = ref_leaky_relu(x, alpha)
+
+        self.inputs = {"X": x}
+        self.outputs = {"Out": out}
+        self.attrs = {"alpha": alpha}
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad_with_place(self.place, ["X"], "Out")
+
+
+class TestLeakyReluAlpha1(TestLeakyRelu):
+    def get_alpha(self):
+        return 2
+
+
+class TestLeakyReluAlpha2(TestLeakyRelu):
+    def get_alpha(self):
+        return -0.01
+
+
+class TestLeakyReluAlpha3(TestLeakyRelu):
+    def get_alpha(self):
+        return -2.0
+
+
+class TestLeakyRelu_ZeroDim(TestLeakyRelu):
+    def init_shape(self):
+        self.shape = []
+
+
+class TestLeakyReluAPI(unittest.TestCase):
+    # test paddle.nn.LeakyReLU, paddle.nn.functional.leaky_relu,
+    def setUp(self):
+        np.random.seed(1024)
+        self.x_np = np.random.uniform(-1, 1, [10, 12]).astype("float32")
+        self.place = paddle.CustomPlace("npu", 0)
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.static.data("X", [10, 12])
+            out1 = F.leaky_relu(x)
+            m = paddle.nn.LeakyReLU()
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={"X": self.x_np}, fetch_list=[out1, out2])
+        out_ref = ref_leaky_relu(self.x_np)
+        for r in res:
+            np.testing.assert_allclose(out_ref, r, rtol=1e-05)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out1 = F.leaky_relu(x)
+        m = paddle.nn.LeakyReLU()
+        out2 = m(x)
+        out_ref = ref_leaky_relu(self.x_np)
+        for r in [out1, out2]:
+            np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
+
+        out1 = F.leaky_relu(x, 0.6)
+        m = paddle.nn.LeakyReLU(0.6)
+        out2 = m(x)
+        out_ref = ref_leaky_relu(self.x_np, 0.6)
+        for r in [out1, out2]:
+            np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
+        paddle.enable_static()
 
 
 def gelu(x, approximate):