elu4d

src/layer/elu.cpp

@@ -35,8 +35,9 @@ int ELU::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
 {
     int w = bottom_top_blob.w;
     int h = bottom_top_blob.h;
+    int d = bottom_top_blob.d;
     int channels = bottom_top_blob.c;
-    int size = w * h;
+    int size = w * h * d;
 
     #pragma omp parallel for num_threads(opt.num_threads)
     for (int q = 0; q < channels; q++)

src/layer/x86/elu_x86.cpp

@@ -29,9 +29,10 @@ int ELU_x86::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
 {
     int w = bottom_top_blob.w;
     int h = bottom_top_blob.h;
+    int d = bottom_top_blob.d;
     int channels = bottom_top_blob.c;
     int elempack = bottom_top_blob.elempack;
-    int size = w * h * elempack;
+    int size = w * h * d * elempack;
 
     #pragma omp parallel for num_threads(opt.num_threads)
     for (int q = 0; q < channels; q++)

tools/pnnx/tests/ncnn/test_F_elu.py

@@ -20,30 +20,36 @@ class Model(nn.Module):
     def __init__(self):
         super(Model, self).__init__()
 
-    def forward(self, x, y, z):
+    def forward(self, x, y, z, w):
+        x = x * 2 - 1
+        y = y * 2 - 1
+        z = z * 2 - 1
+        w = w * 2 - 1
         x = F.elu(x)
         y = F.elu(y, 1.2)
         z = F.elu(z, -0.6)
-        return x, y, z
+        w = F.elu(w, 0.1)
+        return x, y, z, w
 
 def test():
     net = Model()
     net.eval()
 
     torch.manual_seed(0)
-    x = torch.rand(1, 16)
-    y = torch.rand(1, 2, 16)
-    z = torch.rand(1, 3, 12, 16)
+    x = torch.rand(16)
+    y = torch.rand(2, 16)
+    z = torch.rand(3, 12, 16)
+    w = torch.rand(5, 7, 9, 11)
 
-    a = net(x, y, z)
+    a = net(x, y, z, w)
 
     # export torchscript
-    mod = torch.jit.trace(net, (x, y, z))
+    mod = torch.jit.trace(net, (x, y, z, w))
     mod.save("test_F_elu.pt")
 
     # torchscript to pnnx
     import os
-    os.system("../../src/pnnx test_F_elu.pt inputshape=[1,16],[1,2,16],[1,3,12,16]")
+    os.system("../../src/pnnx test_F_elu.pt inputshape=[16],[2,16],[3,12,16],[5,7,9,11]")
 
     # ncnn inference
     import test_F_elu_ncnn

tools/pnnx/tests/ncnn/test_nn_ELU.py

@@ -23,30 +23,36 @@ def __init__(self):
         self.act_0 = nn.ELU()
         self.act_1 = nn.ELU(alpha=1.3)
 
-    def forward(self, x, y, z):
+    def forward(self, x, y, z, w):
+        x = x * 2 - 1
+        y = y * 2 - 1
+        z = z * 2 - 1
+        w = w * 2 - 1
         x = self.act_0(x)
         y = self.act_0(y)
         z = self.act_1(z)
-        return x, y, z
+        w = self.act_1(w)
+        return x, y, z, w
 
 def test():
     net = Model()
     net.eval()
 
     torch.manual_seed(0)
-    x = torch.rand(1, 12)
-    y = torch.rand(1, 12, 64)
-    z = torch.rand(1, 12, 24, 64)
+    x = torch.rand(12)
+    y = torch.rand(12, 64)
+    z = torch.rand(12, 24, 64)
+    w = torch.rand(12, 24, 32, 64)
 
-    a = net(x, y, z)
+    a = net(x, y, z, w)
 
     # export torchscript
-    mod = torch.jit.trace(net, (x, y, z))
+    mod = torch.jit.trace(net, (x, y, z, w))
     mod.save("test_nn_ELU.pt")
 
     # torchscript to pnnx
     import os
-    os.system("../../src/pnnx test_nn_ELU.pt inputshape=[1,12],[1,12,64],[1,12,24,64]")
+    os.system("../../src/pnnx test_nn_ELU.pt inputshape=[12],[12,64],[12,24,64],[12,24,32,64]")
 
     # ncnn inference
     import test_nn_ELU_ncnn

tools/pnnx/tests/test_F_elu.py

@@ -21,6 +21,10 @@ def __init__(self):
         super(Model, self).__init__()
 
     def forward(self, x, y, z, w):
+        x = x * 2 - 1
+        y = y * 2 - 1
+        z = z * 2 - 1
+        w = w * 2 - 1
         x = F.elu(x)
         y = F.elu(y, 1.2)
         z = F.elu(z, -0.6)

tools/pnnx/tests/test_nn_ELU.py

@@ -24,6 +24,10 @@ def __init__(self):
         self.act_1 = nn.ELU(alpha=1.3)
 
     def forward(self, x, y, z, w):
+        x = x * 2 - 1
+        y = y * 2 - 1
+        z = z * 2 - 1
+        w = w * 2 - 1
         x = self.act_0(x)
         y = self.act_0(y)
         z = self.act_1(z)