update

configs/mmpose/pose-detection_simcc_onnxruntime_dynamic.py

@@ -14,3 +14,7 @@
             0: 'batch'
         }
     })
+
+codebase_config = dict(
+    export_postprocess=False  # do not export get_simcc_maximum
+)

mmdeploy/codebase/mmpose/codecs/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+from .post_processing import get_simcc_maximum
+
+__all__ = ['get_simcc_maximum']

mmdeploy/codebase/mmpose/codecs/post_processing.py

@@ -0,0 +1,33 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def get_simcc_maximum(simcc_x: torch.Tensor,
+                      simcc_y: torch.Tensor) -> torch.Tensor:
+    """Get maximum response location and value from simcc representations.
+
+    rewrite to support `torch.Tensor` input type.
+
+    Args:
+        simcc_x (torch.Tensor): x-axis SimCC in shape (N, K, Wx)
+        simcc_y (torch.Tensor): y-axis SimCC in shape (N, K, Wy)
+
+    Returns:
+        tuple:
+        - locs (torch.Tensor): locations of maximum heatmap responses in shape
+            (N, K, 2)
+        - vals (torch.Tensor): values of maximum heatmap responses in shape
+            (N, K)
+    """
+    N, K, _ = simcc_x.shape
+    simcc_x = simcc_x.flatten(0, 1)
+    simcc_y = simcc_y.flatten(0, 1)
+    x_locs = simcc_x.argmax(dim=1, keepdim=True)
+    y_locs = simcc_y.argmax(dim=1, keepdim=True)
+    locs = torch.cat((x_locs, y_locs), dim=1).to(torch.float32)
+    max_val_x, _ = simcc_x.max(dim=1, keepdim=True)
+    max_val_y, _ = simcc_y.max(dim=1, keepdim=True)
+    vals, _ = torch.cat([max_val_x, max_val_y], dim=1).min(dim=1)
+    locs = locs.reshape(N, K, 2)
+    vals = vals.reshape(N, K)
+    return locs, vals

mmdeploy/codebase/mmpose/deploy/pose_detection.py

@@ -13,7 +13,8 @@
 from mmengine.registry import Registry
 
 from mmdeploy.codebase.base import CODEBASE, BaseTask, MMCodebase
-from mmdeploy.utils import Codebase, Task, get_input_shape, get_root_logger
+from mmdeploy.utils import (Codebase, Task, get_codebase_config,
+                            get_input_shape, get_root_logger)
 
 
 def process_model_config(
@@ -362,6 +363,9 @@ def get_postprocess(self, *args, **kwargs) -> Dict:
                 params['post_process'] = 'megvii'
                 params['modulate_kernel'] = self.model_cfg.kernel_sizes[-1]
             elif codec.type == 'SimCCLabel':
+                export_postprocess = get_codebase_config(self.deploy_cfg).get(
+                    'export_postprocess', False)
+                params['export_postprocess'] = export_postprocess
                 component = 'SimCCLabelDecode'
             elif codec.type == 'RegressionLabel':
                 component = 'DeepposeRegressionHeadDecode'

mmdeploy/codebase/mmpose/deploy/pose_detection_model.py

@@ -101,12 +101,20 @@ def forward(self,
         if self.model_cfg.model.type == 'YOLODetector':
             return self.pack_yolox_pose_result(batch_outputs, data_samples)
 
+        codebase_cfg = get_codebase_config(self.deploy_cfg)
         codec = self.model_cfg.codec
         if isinstance(codec, (list, tuple)):
             codec = codec[-1]
         if codec.type == 'SimCCLabel':
-            batch_pred_x, batch_pred_y = batch_outputs
-            preds = self.head.decode((batch_pred_x, batch_pred_y))
+            export_postprocess = codebase_cfg.get('export_postprocess', False)
+            if export_postprocess:
+                keypoints, scores = [_.cpu().numpy() for _ in batch_outputs]
+                preds = [
+                    InstanceData(keypoints=keypoints, keypoint_scores=scores)
+                ]
+            else:
+                batch_pred_x, batch_pred_y = batch_outputs
+                preds = self.head.decode((batch_pred_x, batch_pred_y))
         elif codec.type in ['RegressionLabel', 'IntegralRegressionLabel']:
             preds = self.head.decode(batch_outputs)
         else:

mmdeploy/codebase/mmpose/models/heads/__init__.py

@@ -1,4 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from . import mspn_head, yolox_pose_head  # noqa: F401,F403
+from . import mspn_head, simcc_head, yolox_pose_head  # noqa: F401,F403
 
-__all__ = ['mspn_head', 'yolox_pose_head']
+__all__ = ['mspn_head', 'yolox_pose_head', 'simcc_head']

mmdeploy/codebase/mmpose/models/heads/simcc_head.py

@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdeploy.codebase.mmpose.codecs import get_simcc_maximum
+from mmdeploy.core import FUNCTION_REWRITER
+from mmdeploy.utils import get_codebase_config
+
+
+@FUNCTION_REWRITER.register_rewriter('mmpose.models.heads.RTMCCHead.forward')
+@FUNCTION_REWRITER.register_rewriter('mmpose.models.heads.SimCCHead.forward')
+def simcc_head__forward(self, feats):
+    """Rewrite `forward` of SimCCHead for default backend.
+
+    Args:
+        feats (tuple[Tensor]): Input features.
+    Returns:
+        key-points (torch.Tensor): Output keypoints in
+            shape of (N, K, 3)
+    """
+    ctx = FUNCTION_REWRITER.get_context()
+    simcc_x, simcc_y = ctx.origin_func(self, feats)
+    codebase_cfg = get_codebase_config(ctx.cfg)
+    export_postprocess = codebase_cfg.get('export_postprocess', False)
+    if not export_postprocess:
+        return simcc_x, simcc_y
+    assert self.decoder.use_dark is False, \
+        'Do not support SimCCLabel with use_dark=True'
+    pts, scores = get_simcc_maximum(simcc_x, simcc_y)
+    pts /= self.decoder.simcc_split_ratio
+    return pts, scores