Unify the rank of prelu alpha to 4, corresponding to [N, C, H, W], except for the all mode

python/paddle/fluid/layers/nn.py

@@ -8693,7 +8693,7 @@ def prelu(x, mode, param_attr=None, name=None):
     if mode == 'channel':
         alpha_shape = [1, x.shape[1], 1, 1]
     elif mode == 'element':
-        alpha_shape = x.shape[1:]
+        alpha_shape = [1, x.shape[1], x.shape[2], x.shape[3]]
     dtype = helper.input_dtype(input_param_name='x')
     alpha = helper.create_parameter(
         attr=helper.param_attr,

python/paddle/fluid/tests/unittests/test_layers.py

@@ -712,7 +712,7 @@ class TestLayer(LayerTest):
             self.assertTrue(
                 np.array_equal(btp1.bias.numpy(), btp2.bias.numpy()))
 
-    def test_prelu(self):
+    def prelu_test(self, mode):
         inp_np = np.ones([5, 200, 100, 100]).astype('float32')
         with self.static_graph():
             data_t = layers.data(
@@ -720,7 +720,6 @@ class TestLayer(LayerTest):
                 shape=[5, 200, 100, 100],
                 dtype="float32",
                 append_batch_size=False)
-            mode = 'channel'
             out = layers.prelu(
                 data_t, mode, param_attr=ParamAttr(initializer=Constant(1.0)))
             static_rlt = self.get_static_graph_result(
@@ -732,7 +731,6 @@ class TestLayer(LayerTest):
                 shape=[5, 200, 100, 100],
                 dtype="float32",
                 append_batch_size=False)
-            mode = 'channel'
             prelu = nn.PRelu(
                 'prelu',
                 mode=mode,
@@ -742,7 +740,6 @@ class TestLayer(LayerTest):
                 feed={"input": inp_np}, fetch_list=[out])[0]
 
         with self.dynamic_graph():
-            mode = 'channel'
             prelu = nn.PRelu(
                 'prelu',
                 mode=mode,
@@ -756,7 +753,6 @@ class TestLayer(LayerTest):
         with self.dynamic_graph():
             inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
             inp = base.to_variable(inp_np)
-            mode = 'channel'
             prelu1 = nn.PRelu(
                 'prelu1',
                 mode=mode,
@@ -779,6 +775,11 @@ class TestLayer(LayerTest):
             self.assertTrue(
                 np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy()))
 
+    def test_prelu(self):
+        self.prelu_test("channel")
+        self.prelu_test("element")
+        self.prelu_test("all")
+
     def test_embeding(self):
         inp_word = np.array([[[1]]]).astype('int64')
         dict_size = 20