[v1.7] Backport Gluon Activations Fix #18370

python/mxnet/gluon/nn/activations.py

@@ -139,7 +139,8 @@ def __init__(self, alpha_initializer=initializer.Constant(0.25),
                                          init=alpha_initializer)
 
     def hybrid_forward(self, F, x, alpha):
-        return F.LeakyReLU(x, gamma=alpha, act_type='prelu', name='fwd')
+        leaky_relu = F.npx.leaky_relu if is_np_array() else F.LeakyReLU
+        return leaky_relu(x, gamma=alpha, act_type='prelu', name='fwd')
 
 
 class ELU(HybridBlock):
@@ -167,7 +168,8 @@ def __init__(self, alpha=1.0, **kwargs):
         self._alpha = alpha
 
     def hybrid_forward(self, F, x):
-        return F.LeakyReLU(x, act_type='elu', slope=self._alpha)
+        leaky_relu = F.npx.leaky_relu if is_np_array() else F.LeakyReLU
+        return leaky_relu(x, act_type='elu', slope=self._alpha)
 
 
 class SELU(HybridBlock):
@@ -187,7 +189,9 @@ def __init__(self, **kwargs):
         super(SELU, self).__init__(**kwargs)
 
     def hybrid_forward(self, F, x):
-        return F.LeakyReLU(x, act_type='selu', name='fwd')
+        leaky_relu = F.npx.leaky_relu if is_np_array() else F.LeakyReLU
+        return leaky_relu(x, act_type='selu', name='fwd')
+
 
 class GELU(HybridBlock):
     r"""
@@ -206,7 +210,8 @@ def __init__(self, **kwargs):
         super(GELU, self).__init__(**kwargs)
 
     def hybrid_forward(self, F, x):
-        return F.LeakyReLU(x, act_type='gelu', name='fwd')
+        leaky_relu = F.npx.leaky_relu if is_np_array() else F.LeakyReLU
+        return leaky_relu(x, act_type='gelu', name='fwd')
 
 
 class Swish(HybridBlock):
@@ -232,4 +237,7 @@ def __init__(self, beta=1.0, **kwargs):
         self._beta = beta
 
     def hybrid_forward(self, F, x):
-        return x * F.sigmoid(self._beta * x, name='fwd')
+        if is_np_array():
+            return x * F.npx.sigmoid(self._beta * x)
+        else:
+            return x * F.sigmoid(self._beta * x, name='fwd')

tests/python/unittest/test_numpy_gluon.py

@@ -25,6 +25,7 @@
 import mxnet as mx
 from mxnet import gluon, autograd, np
 from mxnet.test_utils import use_np, assert_almost_equal, check_gluon_hybridize_consistency
+from mxnet.gluon import nn
 from common import with_seed
 import random
 
@@ -422,6 +423,55 @@ def hybrid_forward(self, F, valid_length):
     assert mx.test_utils.same(out1.asnumpy(), out2.asnumpy())
 
 
+@with_seed()
+@use_np
+def test_activations_leakyrelu():
+    # Currently, all the activation tests, we will just test for runnable.
+    act_layer = nn.LeakyReLU(0.1)
+    out = act_layer(mx.np.random.uniform(size=(10,)))
+    out.asnumpy()
+
+
+@with_seed()
+@use_np
+def test_activations_prelu():
+    act_layer = nn.PReLU()
+    act_layer.initialize()
+    out = act_layer(mx.np.random.uniform(size=(10,)))
+    out.asnumpy()
+
+
+@with_seed()
+@use_np
+def test_activations_elu():
+    act_layer = nn.ELU(1.0)
+    out = act_layer(mx.np.random.uniform(size=(10,)))
+    out.asnumpy()
+
+
+@with_seed()
+@use_np
+def test_activations_selu():
+    act_layer = nn.SELU()
+    out = act_layer(mx.np.random.uniform(size=(10,)))
+    out.asnumpy()
+
+
+@with_seed()
+@use_np
+def test_activations_gelu():
+    act_layer = nn.GELU()
+    out = act_layer(mx.np.random.uniform(size=(10,)))
+    out.asnumpy()
+
+
+@with_seed()
+@use_np
+def test_activations_swish():
+    act_layer = nn.Swish()
+    out = act_layer(mx.np.random.uniform(size=(10,)))
+    out.asnumpy()
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()