support torch.t to ncnn (#4940)

tools/pnnx/src/CMakeLists.txt

@@ -537,6 +537,7 @@ set(pnnx_pass_ncnn_SRCS
     pass_ncnn/torch_prod.cpp
     pass_ncnn/torch_squeeze.cpp
     pass_ncnn/torch_sum.cpp
+    pass_ncnn/torch_t.cpp
     pass_ncnn/torch_transpose.cpp
     pass_ncnn/torch_unsqueeze.cpp
     pass_ncnn/torchvision_DeformConv2d.cpp

tools/pnnx/src/pass_ncnn/torch_t.cpp

@@ -0,0 +1,54 @@
+// 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_ncnn.h"
+
+namespace pnnx {
+
+namespace ncnn {
+
+class torch_t : public GraphRewriterPass
+{
+public:
+    const char* match_pattern_graph() const
+    {
+        return R"PNNXIR(7767517
+3 2
+pnnx.Input              input       0 1 input
+torch.t                 op_0        1 1 input out
+pnnx.Output             output      1 0 out
+)PNNXIR";
+    }
+
+    const char* type_str() const
+    {
+        return "Permute";
+    }
+
+    const char* name_str() const
+    {
+        return "t";
+    }
+
+    void write(Operator* op, const std::map<std::string, Parameter>& captured_params) const
+    {
+        op->params["0"] = 1;
+    }
+};
+
+REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(torch_t, 20)
+
+} // namespace ncnn
+
+} // namespace pnnx

tools/pnnx/tests/ncnn/CMakeLists.txt

@@ -153,6 +153,7 @@ pnnx_ncnn_add_test(torch_norm)
 pnnx_ncnn_add_test(torch_permute)
 pnnx_ncnn_add_test(torch_prod)
 pnnx_ncnn_add_test(torch_sum)
+pnnx_ncnn_add_test(torch_t)
 pnnx_ncnn_add_test(torch_squeeze)
 pnnx_ncnn_add_test(torch_stack)
 pnnx_ncnn_add_test(torch_tensor_split)

tools/pnnx/tests/ncnn/test_torch_t.py

@@ -0,0 +1,61 @@
+# 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, y):
+        x = torch.t(x)
+        y = torch.t(y)
+        x = F.relu(x)
+        y = F.relu(y)
+        return x, y
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(3)
+    y = torch.rand(5, 9)
+
+    a = net(x, y)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y))
+    mod.save("test_torch_t.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../../src/pnnx test_torch_t.pt inputshape=[3],[5,9]")
+
+    # ncnn inference
+    import test_torch_t_ncnn
+    b = test_torch_t_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)