!46810 fix prelu when weight is negative

Merge pull request !46810 from 冯一航/fix_prelu

mindspore/python/mindspore/nn/layer/activation.py

@@ -1124,15 +1124,9 @@ class PReLU(Cell):
                             f"but got {type(w).__name__}.")
         self.w = Parameter(w, name='a')
         self.prelu = P.PReLU()
-        self.relu = P.ReLU()
-        self.assign = P.Assign()
 
     def construct(self, x):
-        u = self.relu(self.w)
-        v = self.prelu(x, F.cast(u, x.dtype))
-        if self.training:
-            self.assign(self.w, u)
-        return v
+        return self.prelu(x, F.cast(self.w, x.dtype))
 
 
 class HSwish(Cell):
@@ -1525,6 +1519,7 @@ class GLU(Cell):
         x2 = self.sigmoid(x2)
         return x1 * x2
 
+
 _activation = {
     'softmin': Softmin,
     'softmax': Softmax,

tests/st/nn/test_prelu.py

@@ -0,0 +1,66 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore as ms
+import mindspore.nn as nn
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.pool = nn.PReLU(channel=2, w=-0.25)
+
+    def construct(self, x):
+        out = self.pool(x)
+        return out
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_prelu_normal(mode):
+    """
+    Feature: PReLU
+    Description: Verify the result of PReLU
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    x = ms.Tensor([[[0.9192, -0.1487],
+                    [-0.3999, -0.6840]],
+
+                   [[0.4745, -0.6271],
+                    [-0.6547, -0.5856]],
+
+                   [[-0.2572, -0.8412],
+                    [0.1918, -0.6117]]])
+    net = Net()
+    out = net(x)
+    expect_out = np.array([[[0.9192, 0.037175],
+                            [0.099975, 0.171]],
+
+                           [[0.4745, 0.156775],
+                            [0.163675, 0.1464]],
+
+                           [[0.0643, 0.2103],
+                            [0.1918, 0.152925]]])
+    assert np.allclose(out.asnumpy().astype(np.float16), expect_out.astype(np.float16))