Change op domain (open-mmlab#262)

* change domain to mmdeploy

* update tests

* resolve comments

csrc/backend_ops/onnxruntime/onnxruntime_register.cpp

@@ -3,13 +3,13 @@
 
 #include "ort_utils.h"
 
-const char *c_MMCVOpDomain = "mmcv";
+const char *c_MMDeployOpDomain = "mmdeploy";
 
 OrtStatus *ORT_API_CALL RegisterCustomOps(OrtSessionOptions *options, const OrtApiBase *api) {
   OrtCustomOpDomain *domain = nullptr;
   const OrtApi *kOrtApi = api->GetApi(ORT_API_VERSION);
 
-  if (auto status = kOrtApi->CreateCustomOpDomain(c_MMCVOpDomain, &domain)) {
+  if (auto status = kOrtApi->CreateCustomOpDomain(c_MMDeployOpDomain, &domain)) {
     return status;
   }
 

docs/build.md

@@ -22,6 +22,7 @@
       # go back to third_party directory and git clone pybind11
       cd ..
       git clone [email protected]:pybind/pybind11.git pybind11
+      cd pybind11
       git checkout 70a58c5
       ```
 

mmdeploy/codebase/mmdet/models/roi_heads/single_level_roi_extractor.py

@@ -28,7 +28,7 @@ def symbolic(g, *args):
         inputs = args[:len(featmap_strides)]
         rois = args[len(featmap_strides)]
         return g.op(
-            'mmcv::MMCVMultiLevelRoiAlign',
+            'mmdeploy::MMCVMultiLevelRoiAlign',
             rois,
             *inputs,
             output_height_i=output_size[1],

mmdeploy/core/optimizers/function_marker.py

@@ -40,9 +40,9 @@ class Mark(torch.autograd.Function):
 
     @staticmethod
     def symbolic(g, x, dtype, shape, func, func_id, type, name, id, attrs):
-        """Symbolic function for mmcv::Mark op."""
+        """Symbolic function for mmdeploy::Mark op."""
         n = g.op(
-            'mmcv::Mark',
+            'mmdeploy::Mark',
             x,
             dtype_i=TORCH_DTYPE_TO_ONNX[dtype],
             shape_i=shape,

mmdeploy/mmcv/ops/nms.py

@@ -132,10 +132,10 @@ def nms_dynamic(ctx, g, boxes: Tensor, scores: Tensor,
 
 
 class TRTBatchedNMSop(torch.autograd.Function):
-    """Create mmcv::TRTBatchedNMS op for TensorRT backend.
+    """Create mmdeploy::TRTBatchedNMS op for TensorRT backend.
 
     NMS in ONNX supports dynamic outputs. This class helps replace
-    onnx::NonMaxSuppression with mmcv::TRTBatchedNMS.
+    onnx::NonMaxSuppression with mmdeploy::TRTBatchedNMS.
     """
 
     @staticmethod
@@ -190,9 +190,9 @@ def symbolic(g,
                  iou_threshold: float,
                  score_threshold: float,
                  background_label_id: int = -1):
-        """Symbolic function for mmcv::TRTBatchedNMS."""
+        """Symbolic function for mmdeploy::TRTBatchedNMS."""
         return g.op(
-            'mmcv::TRTBatchedNMS',
+            'mmdeploy::TRTBatchedNMS',
             boxes,
             scores,
             num_classes_i=num_classes,

mmdeploy/mmcv/ops/roi_align.py

@@ -16,7 +16,7 @@ def roi_align_default(ctx, g, input: Tensor, rois: Tensor,
                       sampling_ratio: int, pool_mode: str, aligned: bool):
     """Rewrite symbolic function for default backend.
 
-    Replace onnx::RoiAlign with mmcv::MMCVRoiAlign.
+    Replace onnx::RoiAlign with mmdeploy::MMCVRoiAlign.
 
     Args:
         ctx (ContextCaller): The context with additional information.
@@ -38,7 +38,7 @@ def roi_align_default(ctx, g, input: Tensor, rois: Tensor,
     """
 
     return g.op(
-        'mmcv::MMCVRoiAlign',
+        'mmdeploy::MMCVRoiAlign',
         input,
         rois,
         output_height_i=output_size[0],

mmdeploy/pytorch/ops/grid_sampler.py

@@ -15,10 +15,10 @@ def grid_sampler(g,
 
     PyTorch does not support export grid_sampler to ONNX by default. We add the
     support here. `grid_sampler` will be exported as ONNX node
-    'mmcv::grid_sampler'
+    'mmdeploy::grid_sampler'
     """
     return g.op(
-        'mmcv::grid_sampler',
+        'mmdeploy::grid_sampler',
         input,
         grid,
         interpolation_mode_i=interpolation_mode,

mmdeploy/pytorch/ops/instance_norm.py

@@ -39,7 +39,7 @@ def instance_norm(g, input, num_groups, weight, bias, eps, cudnn_enabled):
                                     'Tensor'))
 
     norm_reshaped = g.op(
-        'mmcv::TRTInstanceNormalization',
+        'mmdeploy::TRTInstanceNormalization',
         input_reshaped,
         weight_,
         bias_,

tests/test_core/test_mark.py

@@ -35,7 +35,7 @@ def forward(self, x, y):
 
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'Mark'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
     assert attribute_to_dict(nodes[0].attribute) == dict(
         dtype=1,
         func='add',
@@ -46,7 +46,7 @@ def forward(self, x, y):
         shape=[2, 3, 4])
 
     assert nodes[1].op_type == 'Mark'
-    assert nodes[1].domain == 'mmcv'
+    assert nodes[1].domain == 'mmdeploy'
     assert attribute_to_dict(nodes[1].attribute) == dict(
         dtype=1,
         func='add',
@@ -59,7 +59,7 @@ def forward(self, x, y):
     assert nodes[2].op_type == 'Add'
 
     assert nodes[3].op_type == 'Mark'
-    assert nodes[3].domain == 'mmcv'
+    assert nodes[3].domain == 'mmdeploy'
     assert attribute_to_dict(nodes[3].attribute) == dict(
         dtype=1,
         func='add',

tests/test_core/test_symbolic_register.py

@@ -20,7 +20,7 @@ class TestFunc(Function):
 
         @staticmethod
         def symbolic(g, x, val):
-            return g.op('mmcv::symbolic_old', x, val_i=val)
+            return g.op('mmdeploy::symbolic_old', x, val_i=val)
 
         @staticmethod
         def forward(ctx, x, val):
@@ -42,17 +42,17 @@ def test_symbolic_rewriter():
     @SYMBOLIC_REWRITER.register_symbolic('mmdeploy.TestFunc')
     def symbolic_testfunc_default(symbolic_wrapper, g, x, val):
         assert hasattr(symbolic_wrapper, 'cfg')
-        return g.op('mmcv::symbolic_testfunc_default', x, val_i=val)
+        return g.op('mmdeploy::symbolic_testfunc_default', x, val_i=val)
 
     @SYMBOLIC_REWRITER.register_symbolic(
         'mmdeploy.TestFunc', backend='tensorrt')
     def symbolic_testfunc_tensorrt(symbolic_wrapper, g, x, val):
-        return g.op('mmcv::symbolic_testfunc_tensorrt', x, val_i=val)
+        return g.op('mmdeploy::symbolic_testfunc_tensorrt', x, val_i=val)
 
     @SYMBOLIC_REWRITER.register_symbolic(
         'cummax', is_pytorch=True, arg_descriptors=['v', 'i'])
     def symbolic_cummax(symbolic_wrapper, g, input, dim):
-        return g.op('mmcv::cummax_default', input, dim_i=dim, outputs=2)
+        return g.op('mmdeploy::cummax_default', input, dim_i=dim, outputs=2)
 
     class TestModel(torch.nn.Module):
 
@@ -74,42 +74,42 @@ def forward(self, x):
     onnx_model = onnx.load(output_file)
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'symbolic_testfunc_default'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
     assert nodes[1].op_type == 'cummax_default'
-    assert nodes[1].domain == 'mmcv'
+    assert nodes[1].domain == 'mmdeploy'
 
     # ncnn
     with RewriterContext(cfg=cfg, backend='ncnn', opset=11):
         torch.onnx.export(model, x, output_file, opset_version=11)
     onnx_model = onnx.load(output_file)
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'symbolic_testfunc_default'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
     assert nodes[1].op_type == 'cummax_default'
-    assert nodes[1].domain == 'mmcv'
+    assert nodes[1].domain == 'mmdeploy'
 
     # tensorrt
     with RewriterContext(cfg=cfg, backend='tensorrt', opset=11):
         torch.onnx.export(model, x, output_file, opset_version=11)
     onnx_model = onnx.load(output_file)
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'symbolic_testfunc_tensorrt'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
     assert nodes[1].op_type == 'cummax_default'
-    assert nodes[1].domain == 'mmcv'
+    assert nodes[1].domain == 'mmdeploy'
 
 
 def test_unregister():
     test_func = mmdeploy.TestFunc.apply
 
     @SYMBOLIC_REWRITER.register_symbolic('mmdeploy.TestFunc')
     def symbolic_testfunc_default(symbolic_wrapper, g, x, val):
-        return g.op('mmcv::symbolic_testfunc_default', x, val_i=val)
+        return g.op('mmdeploy::symbolic_testfunc_default', x, val_i=val)
 
     @SYMBOLIC_REWRITER.register_symbolic(
         'cummax', is_pytorch=True, arg_descriptors=['v', 'i'])
     def symbolic_cummax(symbolic_wrapper, g, input, dim):
-        return g.op('mmcv::cummax_default', input, dim_i=dim, outputs=2)
+        return g.op('mmdeploy::cummax_default', input, dim_i=dim, outputs=2)
 
     class TestModel(torch.nn.Module):
 
@@ -135,7 +135,7 @@ def forward(self, x):
     onnx_model = onnx.load(output_file)
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'cummax_default'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
 
     with pytest.raises(RuntimeError):
         torch.onnx.export(model, x, output_file, opset_version=11)
@@ -146,21 +146,21 @@ def forward(self, x):
     onnx_model = onnx.load(output_file)
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'symbolic_testfunc_default'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
 
     torch.onnx.export(model, x, output_file, opset_version=11)
     onnx_model = onnx.load(output_file)
     nodes = onnx_model.graph.node
     assert nodes[0].op_type == 'symbolic_old'
-    assert nodes[0].domain == 'mmcv'
+    assert nodes[0].domain == 'mmdeploy'
 
 
 def test_register_empty_symbolic():
     symbolic_rewriter = SymbolicRewriter()
 
     @symbolic_rewriter.register_symbolic('mmdeploy.EmptyFunction')
     def symbolic_testfunc_default(symbolic_wrapper, g, x, val):
-        return g.op('mmcv::symbolic_testfunc_default', x, val_i=val)
+        return g.op('mmdeploy::symbolic_testfunc_default', x, val_i=val)
 
     symbolic_rewriter.enter()
     assert len(symbolic_rewriter._extra_symbolic) == 0

tests/test_ops/test_ops.py

@@ -384,7 +384,7 @@ def test_multi_level_roi_align(backend,
         input_name, ['bbox_feats'],
         'MMCVMultiLevelRoiAlign_0',
         None,
-        'mmlab',
+        'mmdeploy',
         aligned=aligned,
         featmap_strides=featmap_strides,
         finest_scale=finest_scale,
@@ -397,7 +397,7 @@ def test_multi_level_roi_align(backend,
         graph, producer_name='pytorch', producer_version='1.8')
     onnx_model.opset_import[0].version = 11
     onnx_model.opset_import.append(
-        onnx.onnx_ml_pb2.OperatorSetIdProto(domain='mmlab', version=1))
+        onnx.onnx_ml_pb2.OperatorSetIdProto(domain='mmdeploy', version=1))
 
     backend.run_and_validate(
         onnx_model, [rois, *input],

tests/test_pytorch/test_pytorch_ops.py

@@ -85,7 +85,7 @@ def test_grid_sampler():
     model = OpModel(torch.grid_sampler, flow, 0, 0, False).eval()
     nodes = get_model_onnx_nodes(model, x)
     assert nodes[1].op_type == 'grid_sampler'
-    assert nodes[1].domain == 'mmcv'
+    assert nodes[1].domain == 'mmdeploy'
 
 
 def test_instance_norm():
@@ -94,7 +94,7 @@ def test_instance_norm():
                     1e-05).eval()
     nodes = get_model_onnx_nodes(model, x)
     assert nodes[4].op_type == 'TRTInstanceNormalization'
-    assert nodes[4].domain == 'mmcv'
+    assert nodes[4].domain == 'mmdeploy'
 
 
 class TestSqueeze: