Add tests for LogSigmoid and LogSoftmax in pnnx

tools/pnnx/tests/ncnn/CMakeLists.txt

@@ -100,6 +100,8 @@ pnnx_ncnn_add_test(nn_LayerNorm)
 pnnx_ncnn_add_test(nn_LeakyReLU)
 pnnx_ncnn_add_test(nn_Linear)
 pnnx_ncnn_add_test(nn_LocalResponseNorm)
+pnnx_ncnn_add_test(nn_LogSigmoid)
+pnnx_ncnn_add_test(nn_LogSoftmax)
 pnnx_ncnn_add_test(nn_LSTM)
 pnnx_ncnn_add_test(nn_MaxPool1d)
 pnnx_ncnn_add_test(nn_MaxPool2d)

tools/pnnx/tests/ncnn/test_nn_LogSigmoid.py

@@ -0,0 +1,65 @@
+# Tencent is pleased to support the open source community by making ncnn available.
+#
+# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# 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 torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+        self.act_0 = nn.LogSigmoid()
+
+    def forward(self, x, y, z, w):
+        x = self.act_0(x)
+        y = self.act_0(y)
+        z = self.act_0(z)
+        w = self.act_0(w)
+        return x, y, z, w
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    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, w)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y, z, w))
+    mod.save("test_nn_Sigmoid.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../../src/pnnx test_nn_Sigmoid.pt inputshape=[12],[12,64],[12,24,64],[12,24,32,64]")
+
+    # ncnn inference
+    import test_nn_Sigmoid_ncnn
+    b = test_nn_Sigmoid_ncnn.test_inference()
+
+    for a0, b0 in zip(a, b):
+        if not torch.allclose(a0, b0, 1e-4, 1e-4):
+            return False
+    return True
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)

tools/pnnx/tests/ncnn/test_nn_LogSoftmax.py

@@ -0,0 +1,67 @@
+# Tencent is pleased to support the open source community by making ncnn available.
+#
+# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# 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 torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+        self.act_0 = nn.LogSoftmax(dim=0)
+        self.act_1 = nn.LogSoftmax(dim=1)
+        self.act_2 = nn.LogSoftmax(dim=2)
+        self.act_3 = nn.LogSoftmax(dim=-1)
+
+    def forward(self, x, y, z):
+        x = self.act_0(x)
+        y = self.act_1(y)
+        z = self.act_2(z)
+        z2 = self.act_3(z)
+        return x, y, z, z2
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(12)
+    y = torch.rand(12, 64)
+    z = torch.rand(12, 24, 64)
+
+    a = net(x, y, z)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y, z))
+    mod.save("test_nn_Softmax.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../../src/pnnx test_nn_Softmax.pt inputshape=[12],[12,64],[12,24,64]")
+
+    # ncnn inference
+    import test_nn_Softmax_ncnn
+    b = test_nn_Softmax_ncnn.test_inference()
+
+    for a0, b0 in zip(a, b):
+        if not torch.allclose(a0, b0, 1e-4, 1e-4):
+            return False
+    return True
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)