From dd962a47008bb8a79fd6c8dc46d6f62064a6cf22 Mon Sep 17 00:00:00 2001
From: Megvii Engine Team <megengine@megvii.com>
Date: Thu, 23 Apr 2020 20:47:10 +0800
Subject: [PATCH] fix(mge/module): add negative slope attribute for LeakyReLU
 module

GitOrigin-RevId: 67e1967b56e63d227c0fe080619d89195d0df35b
---
 python_module/megengine/module/activation.py  |  6 ++++-
 .../test/unit/module/test_activation.py       | 24 +++++++++++++++++++
 2 files changed, 29 insertions(+), 1 deletion(-)
 create mode 100644 python_module/test/unit/module/test_activation.py

diff --git a/python_module/megengine/module/activation.py b/python_module/megengine/module/activation.py
index 8240c488..a6bba573 100644
--- a/python_module/megengine/module/activation.py
+++ b/python_module/megengine/module/activation.py
@@ -223,5 +223,9 @@ class LeakyReLU(Module):
 
     """
 
+    def __init__(self, negative_slope: float = 0.01):
+        super().__init__()
+        self.negative_slope = negative_slope
+
     def forward(self, inputs):
-        return leaky_relu(inputs)
+        return leaky_relu(inputs, self.negative_slope)
diff --git a/python_module/test/unit/module/test_activation.py b/python_module/test/unit/module/test_activation.py
new file mode 100644
index 00000000..afca1fde
--- /dev/null
+++ b/python_module/test/unit/module/test_activation.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import numpy as np
+
+import megengine as mge
+from megengine.module import LeakyReLU
+from megengine.test import assertTensorClose
+
+
+def test_leaky_relu():
+    data = np.array([-8, -12, 6, 10]).astype(np.float32)
+    negative_slope = 0.1
+
+    leaky_relu = LeakyReLU(negative_slope)
+    output = leaky_relu(mge.tensor(data))
+
+    np_output = np.maximum(0, data) + negative_slope * np.minimum(0, data)
+    assertTensorClose(output.numpy(), np_output, max_err=0)
-- 
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