Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-09-15 14:13:49 +08:00
commit 7e4ef6f98b
7002 changed files with 1452249 additions and 0 deletions
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+# Copyright (c) OpenMMLab. All rights reserved.
+import base64
+import os
+
+import mmcv
+import torch
+
+from mmpose.apis import (inference_bottom_up_pose_model,
+                         inference_top_down_pose_model, init_pose_model)
+from mmpose.models.detectors import AssociativeEmbedding, TopDown
+
+try:
+    from ts.torch_handler.base_handler import BaseHandler
+except ImportError:
+    raise ImportError('Please install torchserve.')
+
+
+class MMPoseHandler(BaseHandler):
+
+    def initialize(self, context):
+        properties = context.system_properties
+        self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = torch.device(self.map_location + ':' +
+                                   str(properties.get('gpu_id')) if torch.cuda.
+                                   is_available() else self.map_location)
+        self.manifest = context.manifest
+
+        model_dir = properties.get('model_dir')
+        serialized_file = self.manifest['model']['serializedFile']
+        checkpoint = os.path.join(model_dir, serialized_file)
+        self.config_file = os.path.join(model_dir, 'config.py')
+
+        self.model = init_pose_model(self.config_file, checkpoint, self.device)
+        self.initialized = True
+
+    def preprocess(self, data):
+        images = []
+
+        for row in data:
+            image = row.get('data') or row.get('body')
+            if isinstance(image, str):
+                image = base64.b64decode(image)
+            image = mmcv.imfrombytes(image)
+            images.append(image)
+
+        return images
+
+    def inference(self, data, *args, **kwargs):
+        if isinstance(self.model, TopDown):
+            results = self._inference_top_down_pose_model(data)
+        elif isinstance(self.model, (AssociativeEmbedding, )):
+            results = self._inference_bottom_up_pose_model(data)
+        else:
+            raise NotImplementedError(
+                f'Model type {type(self.model)} is not supported.')
+
+        return results
+
+    def _inference_top_down_pose_model(self, data):
+        results = []
+        for image in data:
+            # use dummy person bounding box
+            preds, _ = inference_top_down_pose_model(
+                self.model, image, person_results=None)
+            results.append(preds)
+        return results
+
+    def _inference_bottom_up_pose_model(self, data):
+        results = []
+        for image in data:
+            preds, _ = inference_bottom_up_pose_model(self.model, image)
+            results.append(preds)
+        return results
+
+    def postprocess(self, data):
+        output = [[{
+            'keypoints': pred['keypoints'].tolist()
+        } for pred in preds] for preds in data]
+
+        return output