first commit

2025-07-02 08:57:16 +03:00
commit 56532cc9a9
1901 changed files with 457695 additions and 0 deletions
--- a/ppocr/modeling/architectures/init.py
+++ b/ppocr/modeling/architectures/init.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2020 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.
+
+import copy
+import importlib
+
+from paddle.jit import to_static
+from paddle.static import InputSpec
+
+from .base_model import BaseModel
+from .distillation_model import DistillationModel
+
+__all__ = ["build_model", "apply_to_static"]
+
+
+def build_model(config):
+    config = copy.deepcopy(config)
+    if not "name" in config:
+        arch = BaseModel(config)
+    else:
+        name = config.pop("name")
+        mod = importlib.import_module(__name__)
+        arch = getattr(mod, name)(config)
+    return arch
+
+
+def apply_to_static(model, config, logger):
+    if config["Global"].get("to_static", False) is not True:
+        return model
+    assert (
+        "d2s_train_image_shape" in config["Global"]
+    ), "d2s_train_image_shape must be assigned for static training mode..."
+    supported_list = [
+        "DB",
+        "SVTR_LCNet",
+        "TableMaster",
+        "LayoutXLM",
+        "SLANet",
+        "SVTR",
+        "SVTR_HGNet",
+        "LaTeXOCR",
+        "UniMERNet",
+        "PP-FormulaNet-S",
+        "PP-FormulaNet-L",
+    ]
+    if config["Architecture"]["algorithm"] in ["Distillation"]:
+        algo = list(config["Architecture"]["Models"].values())[0]["algorithm"]
+    else:
+        algo = config["Architecture"]["algorithm"]
+    assert (
+        algo in supported_list
+    ), f"algorithms that supports static training must in in {supported_list} but got {algo}"
+
+    specs = [
+        InputSpec([None] + config["Global"]["d2s_train_image_shape"], dtype="float32")
+    ]
+
+    if algo == "SVTR_LCNet":
+        specs.append(
+            [
+                InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
+                InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
+                InputSpec([None], dtype="int64"),
+                InputSpec([None], dtype="float64"),
+            ]
+        )
+    elif algo == "TableMaster":
+        specs.append(
+            [
+                InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
+                InputSpec(
+                    [None, config["Global"]["max_text_length"], 4], dtype="float32"
+                ),
+                InputSpec(
+                    [None, config["Global"]["max_text_length"], 1], dtype="float32"
+                ),
+                InputSpec([None, 6], dtype="float32"),
+            ]
+        )
+    elif algo == "LayoutXLM":
+        specs = [
+            [
+                InputSpec(shape=[None, 512], dtype="int64"),  # input_ids
+                InputSpec(shape=[None, 512, 4], dtype="int64"),  # bbox
+                InputSpec(shape=[None, 512], dtype="int64"),  # attention_mask
+                InputSpec(shape=[None, 512], dtype="int64"),  # token_type_ids
+                InputSpec(shape=[None, 3, 224, 224], dtype="float32"),  # image
+                InputSpec(shape=[None, 512], dtype="int64"),  # label
+            ]
+        ]
+    elif algo == "SLANet":
+        specs.append(
+            [
+                InputSpec(
+                    [None, config["Global"]["max_text_length"] + 2], dtype="int64"
+                ),
+                InputSpec(
+                    [None, config["Global"]["max_text_length"] + 2, 4], dtype="float32"
+                ),
+                InputSpec(
+                    [None, config["Global"]["max_text_length"] + 2, 1], dtype="float32"
+                ),
+                InputSpec([None], dtype="int64"),
+                InputSpec([None, 6], dtype="float64"),
+            ]
+        )
+    elif algo == "SVTR":
+        specs.append(
+            [
+                InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
+                InputSpec([None], dtype="int64"),
+            ]
+        )
+    elif algo == "LaTeXOCR":
+        specs = [
+            [
+                InputSpec(shape=[None, 1, None, None], dtype="float32"),
+                InputSpec(shape=[None, None], dtype="float32"),
+                InputSpec(shape=[None, None], dtype="float32"),
+            ]
+        ]
+    elif algo in ["UniMERNet", "PP-FormulaNet-S", "PP-FormulaNet-L"]:
+        specs = [
+            [
+                InputSpec(
+                    [None] + config["Global"]["d2s_train_image_shape"], dtype="float32"
+                ),
+                InputSpec(shape=[None, None], dtype="float32"),
+                InputSpec(shape=[None, None], dtype="float32"),
+            ]
+        ]
+    model = to_static(model, input_spec=specs)
+    logger.info("Successfully to apply @to_static with specs: {}".format(specs))
+    return model