[Feat]: Add UVDoc dynamic graph model

paddlex/inference/models/common/transformers/transformers/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,4 +13,5 @@
 # limitations under the License.
 
 from .configuration_utils import PretrainedConfig
+from .hf_state_dict_utils import BatchNormHFStateDictMixin
 from .model_utils import PretrainedModel

paddlex/inference/models/common/transformers/transformers/hf_state_dict_utils.py

@@ -0,0 +1,67 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+class BatchNormHFStateDictMixin:
+    def _get_forward_key_rules(self):
+        return [
+            ("_mean", "_mean", "running_mean"),
+            ("_variance", "_variance", "running_var"),
+        ]
+
+    def _get_reverse_key_rules(self):
+        return [
+            ("running_mean", "running_mean", "_mean"),
+            ("running_var", "running_var", "_variance"),
+        ]
+
+    def get_hf_state_dict(self, *args, **kwargs):
+
+        try:
+            super().get_hf_state_dict(*args, **kwargs)
+        except NotImplementedError:
+            pass
+
+        model_state_dict = self.state_dict(*args, **kwargs)
+        hf_state_dict = {}
+        rules = self._get_forward_key_rules()
+
+        for old_key, value in model_state_dict.items():
+            new_key = old_key
+            for match_key, old_sub, new_sub in rules:
+                if match_key in old_key:
+                    new_key = old_key.replace(old_sub, new_sub)
+                    break
+            hf_state_dict[new_key] = value
+        return hf_state_dict
+
+    def set_hf_state_dict(self, state_dict, *args, **kwargs):
+
+        try:
+            super().set_hf_state_dict(state_dict, *args, **kwargs)
+        except NotImplementedError:
+            pass
+
+        key_mapping = {}
+        rules = self._get_reverse_key_rules()
+
+        for old_key in list(state_dict.keys()):
+            for match_key, old_sub, new_sub in rules:
+                if match_key in old_key:
+                    key_mapping[old_key] = old_key.replace(old_sub, new_sub)
+                    break
+
+        for old_key, new_key in key_mapping.items():
+            state_dict[new_key] = state_dict.pop(old_key)
+        return self.set_state_dict(state_dict, *args, **kwargs)

paddlex/inference/models/common/transformers/transformers/model_utils.py

@@ -203,7 +203,11 @@ def _transpose_hf_weight(key, weight):
                     else:
                         weight = tp_fn(py_safe_slice_)
                 else:
-                    weight = py_safe_slice_[:]
+                    # HACK
+                    if len(py_safe_slice_.get_shape()) == 0:
+                        logging.debug("Ignore empty shape this moment")
+                    else:
+                        weight = py_safe_slice_[:]
 
                 if not return_numpy and device == "expected":
                     weight = weight._copy_to(
@@ -1841,13 +1845,12 @@ def from_pretrained(
             ):
                 raise NotImplementedError
             else:
-                try:
-                    transpose_weight_keys = model.get_transpose_weight_keys()
-                except NotImplementedError:
-                    if convert_from_hf:
-                        raise ValueError("`convert_from_hf=True` is not supported")
-                    else:
-                        transpose_weight_keys = None
+                transpose_weight_keys = None
+                if convert_from_hf:
+                    try:
+                        transpose_weight_keys = model.get_transpose_weight_keys()
+                    except NotImplementedError:
+                        pass
                 state_dict = load_state_dict(
                     resolved_archive_file,
                     convert_from_hf=convert_from_hf,

paddlex/inference/models/image_unwarping/modeling/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .uvdoc import UVDocNet