[Fix] fix triu (#792)

* fix triu

* triu -> triu_default

mmdeploy/backend/ncnn/init_plugins.py

@@ -31,6 +31,7 @@ def get_onnx2ncnn_path() -> str:
 
     if onnx2ncnn_path is None or not os.path.exists(onnx2ncnn_path):
         onnx2ncnn_path = shutil.which('mmdeploy_onnx2ncnn')
+        onnx2ncnn_path = '' if onnx2ncnn_path is None else onnx2ncnn_path
 
     return onnx2ncnn_path
 

mmdeploy/pytorch/functions/__init__.py

@@ -12,13 +12,13 @@
 from .size import tensor__size__ncnn
 from .tensor_setitem import tensor__setitem__default
 from .topk import topk__dynamic, topk__tensorrt
-from .triu import triu
+from .triu import triu__default
 
 __all__ = [
     'tensor__getattribute__ncnn', 'group_norm__ncnn', 'interpolate__ncnn',
     'interpolate__tensorrt', 'linear__ncnn', 'tensor__repeat__tensorrt',
     'tensor__size__ncnn', 'topk__dynamic', 'topk__tensorrt', 'chunk__ncnn',
-    'triu', 'atan2__default', 'normalize__ncnn', 'expand__ncnn',
+    'triu__default', 'atan2__default', 'normalize__ncnn', 'expand__ncnn',
     'chunk__torchscript', 'masked_fill__onnxruntime',
     'tensor__setitem__default'
 ]

mmdeploy/pytorch/functions/triu.py

@@ -5,15 +5,15 @@
 
 
 @FUNCTION_REWRITER.register_rewriter(func_name='torch.triu')
-def triu(ctx,
-         input: torch.Tensor,
-         diagonal: int = 0,
-         *args,
-         **kwargs) -> torch.Tensor:
+def triu__default(ctx,
+                  input: torch.Tensor,
+                  diagonal: int = 0,
+                  *args,
+                  **kwargs) -> torch.Tensor:
     """Rewrite `triu` for exporting model to ONNX."""
     assert len(input.shape) >= 2
     height, width = input.shape[-2:]
     arange0 = torch.arange(width, device=input.device).unsqueeze(0)
     arange1 = torch.arange(height, device=input.device).unsqueeze(-1)
-    mask = arange0 >= arange1
+    mask = arange0 >= torch.add(arange1, diagonal)
     return input * mask

tests/test_pytorch/test_pytorch_functions.py

@@ -239,28 +239,28 @@ def model_func(input):
 
 @backend_checker(Backend.TENSORRT)
 @pytest.mark.parametrize('shape', [[2, 2], [4, 2], [2, 4], [2, 4, 2]])
-def test_triu_trt(shape):
+@pytest.mark.parametrize('diagonal', [0, 1, -1])
+def test_triu_trt(shape, diagonal):
 
     input = torch.rand(shape)
+    model_output = torch.triu(input=input, diagonal=diagonal)
 
     def triu_caller(*arg, **kwargs):
         return torch.triu(*arg, **kwargs)
 
-    wrapped_func = WrapFunction(triu_caller, diagonal=1)
-    import tempfile
-
-    import onnx
+    wrapped_func = WrapFunction(triu_caller, diagonal=diagonal)
+    rewrite_outputs, is_backend_output = get_rewrite_outputs(
+        wrapped_func,
+        model_inputs={'input': input},
+        deploy_cfg=get_trt_config(['output'], shape=shape),
+        run_with_backend=True)
+    if is_backend_output:
+        rewrite_outputs = rewrite_outputs[0].detach().cpu()
 
-    from mmdeploy.core import RewriterContext
-    onnx_file = tempfile.NamedTemporaryFile(suffix='onnx').name
-    with RewriterContext(
-            cfg=get_trt_config('output', shape),
-            backend=Backend.TENSORRT.value,
-            opset=11), torch.no_grad():
-        torch.onnx.export(wrapped_func, input, onnx_file, opset_version=11)
-    onnx_model = onnx.load(onnx_file)
-    nodes = onnx_model.graph.node
-    assert nodes is not None
+        assert np.allclose(
+            model_output, rewrite_outputs, rtol=1e-03, atol=1e-05)
+    else:
+        assert rewrite_outputs is not None
 
 
 @backend_checker(Backend.NCNN)