pnnx convert torch narrow (#4918)

tools/pnnx/src/CMakeLists.txt

@@ -237,6 +237,7 @@ set(pnnx_pass_level2_SRCS
     pass_level2/torch_mean.cpp
     pass_level2/torch_min.cpp
     pass_level2/torch_mm.cpp
+    pass_level2/torch_narrow.cpp
     pass_level2/torch_ne.cpp
     pass_level2/torch_norm.cpp
     pass_level2/torch_normal.cpp

tools/pnnx/src/pass_level2/torch_narrow.cpp

@@ -0,0 +1,43 @@
+// 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_narrow : public GraphRewriterPass
+{
+public:
+    const char* match_pattern_graph() const
+    {
+        return R"PNNXIR(7767517
+6 5
+pnnx.Input              input_0     0 1 input
+pnnx.Input              input_1     0 1 dim
+pnnx.Input              input_2     0 1 start
+pnnx.Input              input_3     0 1 length
+aten::narrow            op_0        4 1 input dim start length out
+pnnx.Output             output      1 0 out
+)PNNXIR";
+    }
+
+    const char* type_str() const
+    {
+        return "torch.narrow";
+    }
+};
+
+REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(torch_narrow, 20)
+
+} // namespace pnnx

tools/pnnx/tests/CMakeLists.txt

@@ -212,6 +212,7 @@ pnnx_add_test(torch_max)
 pnnx_add_test(torch_mean)
 pnnx_add_test(torch_min)
 pnnx_add_test(torch_mm)
+pnnx_add_test(torch_narrow)
 pnnx_add_test(torch_ne)
 pnnx_add_test(torch_norm)
 pnnx_add_test(torch_ones)

tools/pnnx/tests/test_torch_narrow.py

@@ -0,0 +1,63 @@
+# 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.
+
+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, y, z):
+        out0 = torch.narrow(x, 0, 0, 2)
+        out1 = torch.narrow(x, 1, 1, 2)
+        out2 = torch.narrow(y, 0, 0, 2)
+        out3 = torch.narrow(y, 1, 1, 2)
+        out4 = torch.narrow(z, 0, 0, 2)
+        out5 = torch.narrow(z, 1, 1, 2)
+        return out0, out1, out2, out3, out4, out5
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(3, 3)
+    y = torch.rand(5, 3)
+    z = torch.rand(3, 5)
+    a = net(x, y, z)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y, z))
+    mod.save("test_torch_narrow.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../src/pnnx test_torch_narrow.pt inputshape=[3,3],[5,3],[3,5]")
+
+    # pnnx inference
+    import test_torch_narrow_pnnx
+    b = test_torch_narrow_pnnx.test_inference()
+
+    for a0, b0 in zip(a, b):
+        if not torch.equal(a0, b0):
+            return False
+    return True
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)