[Feature] support MMRotate model with le135 (#788)

* support MMRotate model with le135

* cse before fuse select assign

* remove unused import

csrc/mmdeploy/backend_ops/torchscript/optimizer/passes/onnx/fuse_select_assign.cpp

@@ -3,6 +3,7 @@
 #include <torch/csrc/jit/passes/dead_code_elimination.h>
 
 #include "../../ir/subgraph_matcher.h"
+#include "common_subgraph_elimination.h"
 #include "torch/csrc/jit/ir/irparser.h"
 
 namespace mmdeploy {
@@ -126,14 +127,16 @@ void FuseSelectAssign(Block* block, std::unordered_map<std::string, Tensor>& par
 
 void FuseSelectAssign(std::shared_ptr<Graph>& graph,
                       std::unordered_map<std::string, Tensor>& params) {
+  // cse before search
+  CommonSubgraphElimination(graph, params);
+
   std::string pattern_str = R"IR(
-      graph(%y, %z, %cmp_1, %cmp_2, %start, %axes):
+      graph(%y, %z, %cmp_1, %cmp_2, %start, %axes, %shape_2):
         %nz_1 = onnx::NonZero(%cmp_1)
         %trans_1 = onnx::Transpose(%nz_1)
         %gather_1 = onnx::GatherND(%z, %trans_1)
         %reshape_1_shape = onnx::Constant()
         %reshape_1 = onnx::Reshape(%gather_1, %reshape_1_shape)
-        %shape_2 = onnx::Shape(%y)
         %expand_2 = onnx::Expand(%cmp_2, %shape_2)
         %nz_2 = onnx::NonZero(%expand_2)
         %trans_2 = onnx::Transpose(%nz_2)

mmdeploy/codebase/mmrotate/core/bbox/transforms.py

@@ -38,3 +38,65 @@ def poly2obb_le90__tensorrt(ctx, polys: torch.Tensor) -> torch.Tensor:
     width, _ = torch.max(edges, 1)
     height, _ = torch.min(edges, 1)
     return torch.stack([x_ctr, y_ctr, width, height, angles], 1)
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='mmrotate.core.bbox.transforms.poly2obb_le135')
+def poly2obb_le135__default(ctx, polys):
+    """This is a rewrite for poly2obb to remove NonZero ops.
+
+    Args:
+        polys (torch.Tensor): [x0,y0,x1,y1,x2,y2,x3,y3]
+
+    Returns:
+        obbs (torch.Tensor): [x_ctr,y_ctr,w,h,angle]
+    """
+    polys = torch.reshape(polys, [-1, 8])
+    pt1, pt2, pt3, pt4 = polys[..., :8].chunk(4, 1)
+    edge1 = torch.sqrt(
+        torch.pow(pt1[..., 0] - pt2[..., 0], 2) +
+        torch.pow(pt1[..., 1] - pt2[..., 1], 2))
+    edge2 = torch.sqrt(
+        torch.pow(pt2[..., 0] - pt3[..., 0], 2) +
+        torch.pow(pt2[..., 1] - pt3[..., 1], 2))
+    angles1 = torch.atan2((pt2[..., 1] - pt1[..., 1]),
+                          (pt2[..., 0] - pt1[..., 0]))
+    angles2 = torch.atan2((pt4[..., 1] - pt1[..., 1]),
+                          (pt4[..., 0] - pt1[..., 0]))
+    angles = torch.where(edge1 > edge2, angles1, angles2)
+    angles = norm_angle(angles, 'le135')
+    x_ctr = (pt1[..., 0] + pt3[..., 0]) / 2.0
+    y_ctr = (pt1[..., 1] + pt3[..., 1]) / 2.0
+    edges = torch.stack([edge1, edge2], dim=1)
+    width, _ = torch.max(edges, 1)
+    height, _ = torch.min(edges, 1)
+    return torch.stack([x_ctr, y_ctr, width, height, angles], 1)
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='mmrotate.core.bbox.transforms.obb2poly_le135')
+def obb2poly_le135__default(ctx, rboxes):
+    """Support batched input.
+
+    Args:
+        ctx : context of rewriter
+        obbs (torch.Tensor): [x_ctr,y_ctr,w,h,angle]
+
+    Returns:
+        polys (torch.Tensor): [x0,y0,x1,y1,x2,y2,x3,y3]
+    """
+    B, N = rboxes.shape[:2]
+    x_ctr, y_ctr, width, height, angle = rboxes[..., 0], rboxes[
+        ..., 1], rboxes[..., 2], rboxes[..., 3], rboxes[..., 4]
+    tl_x, tl_y, br_x, br_y = \
+        -width * 0.5, -height * 0.5, \
+        width * 0.5, height * 0.5
+    rects = torch.stack([tl_x, br_x, br_x, tl_x, tl_y, tl_y, br_y, br_y],
+                        dim=-1).reshape(B, N, 2, 4)
+    sin, cos = torch.sin(angle), torch.cos(angle)
+    M = torch.stack([cos, -sin, sin, cos], dim=-1).reshape(B, N, 2, 2)
+    polys = M.matmul(rects).permute(0, 1, 3, 2)
+    xy_ctr = torch.stack([x_ctr, y_ctr], dim=-1).unsqueeze(-2)
+    polys += xy_ctr
+    polys = polys.reshape(B, N, 8)
+    return polys.contiguous()

tests/test_codebase/test_mmrotate/test_mmrotate_core.py

@@ -312,6 +312,76 @@ def poly2obb_le90(*args, **kwargs):
     assert rewrite_outputs is not None
 
 
+@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
+def test_poly2obb_le135(backend_type: Backend):
+    check_backend(backend_type)
+    polys = torch.rand(1, 10, 8)
+    deploy_cfg = mmcv.Config(
+        dict(
+            onnx_config=dict(output_names=None, input_shape=None),
+            backend_config=dict(
+                type=backend_type.value,
+                model_inputs=[
+                    dict(
+                        input_shapes=dict(
+                            polys=dict(
+                                min_shape=polys.shape,
+                                opt_shape=polys.shape,
+                                max_shape=polys.shape)))
+                ]),
+            codebase_config=dict(type='mmrotate', task='RotatedDetection')))
+
+    # wrap function to enable rewrite
+    def poly2obb_le135(*args, **kwargs):
+        import mmrotate
+        return mmrotate.core.bbox.transforms.poly2obb_le135(*args, **kwargs)
+
+    # wrap function to nn.Module, enable torch.onnx.export
+    wrapped_func = WrapFunction(poly2obb_le135)
+    rewrite_outputs, is_backend_output = get_rewrite_outputs(
+        wrapped_func,
+        model_inputs={'polys': polys},
+        deploy_cfg=deploy_cfg,
+        run_with_backend=False)
+
+    assert rewrite_outputs is not None
+
+
+@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
+def test_obb2poly_le135(backend_type: Backend):
+    check_backend(backend_type)
+    rboxes = torch.rand(1, 10, 5)
+    deploy_cfg = mmcv.Config(
+        dict(
+            onnx_config=dict(output_names=None, input_shape=None),
+            backend_config=dict(
+                type=backend_type.value,
+                model_inputs=[
+                    dict(
+                        input_shapes=dict(
+                            rboxes=dict(
+                                min_shape=rboxes.shape,
+                                opt_shape=rboxes.shape,
+                                max_shape=rboxes.shape)))
+                ]),
+            codebase_config=dict(type='mmrotate', task='RotatedDetection')))
+
+    # wrap function to enable rewrite
+    def obb2poly_le135(*args, **kwargs):
+        import mmrotate
+        return mmrotate.core.bbox.transforms.obb2poly_le135(*args, **kwargs)
+
+    # wrap function to nn.Module, enable torch.onnx.export
+    wrapped_func = WrapFunction(obb2poly_le135)
+    rewrite_outputs, is_backend_output = get_rewrite_outputs(
+        wrapped_func,
+        model_inputs={'rboxes': rboxes},
+        deploy_cfg=deploy_cfg,
+        run_with_backend=False)
+
+    assert rewrite_outputs is not None
+
+
 @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
 def test_gvfixcoder__decode(backend_type: Backend):
     check_backend(backend_type)