convert median to pnnx

tools/pnnx/src/CMakeLists.txt

@@ -235,6 +235,7 @@ set(pnnx_pass_level2_SRCS
     pass_level2/torch_matmul.cpp
     pass_level2/torch_max.cpp
     pass_level2/torch_mean.cpp
+    pass_level2/torch_median.cpp
     pass_level2/torch_min.cpp
     pass_level2/torch_mm.cpp
     pass_level2/torch_ne.cpp

tools/pnnx/src/pass_level2/torch_mm - 副本.cpp

@@ -0,0 +1,41 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2023 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.
+
+#include "pass_level2.h"
+
+namespace pnnx {
+
+class torch_median : public GraphRewriterPass
+{
+public:
+    const char* match_pattern_graph() const
+    {
+        return R"PNNXIR(7767517
+4 3
+pnnx.Input              input_0     0 1 input
+pnnx.Input              input_1     0 1 mat2
+aten::mm                op_0        2 1 input mat2 out
+pnnx.Output             output      1 0 out
+)PNNXIR";
+    }
+
+    const char* type_str() const
+    {
+        return "torch.median";
+    }
+};
+
+REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(torch_median, 20)
+
+} // namespace pnnx

tools/pnnx/tests/test_torch_median.py

@@ -0,0 +1,54 @@
+# 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__()
+
+    def forward(self, x):
+        # ¼ÆËãÖÐλÊý
+        out = torch.median(x, dim=1).values
+        return out
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(3, 5)  
+    a = net(x)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x,))
+    mod.save("test_torch_median.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../src/pnnx test_torch_median.pt inputshape=[3,5]")  
+
+    # pnnx inference
+    import test_torch_median_pnnx
+    b = test_torch_median_pnnx.test_inference()
+
+    return torch.equal(a, b)
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)