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ppocr/modeling/architectures/base_model.py

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+# Copyright (c) 2021 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import nn
+from ppocr.modeling.transforms import build_transform
+from ppocr.modeling.backbones import build_backbone
+from ppocr.modeling.necks import build_neck
+from ppocr.modeling.heads import build_head
+
+__all__ = ["BaseModel"]
+
+
+class BaseModel(nn.Layer):
+    def __init__(self, config):
+        """
+        the module for OCR.
+        args:
+            config (dict): the super parameters for module.
+        """
+        super(BaseModel, self).__init__()
+        in_channels = config.get("in_channels", 3)
+        model_type = config["model_type"]
+        # build transform,
+        # for rec, transform can be TPS,None
+        # for det and cls, transform should to be None,
+        # if you make model differently, you can use transform in det and cls
+        if "Transform" not in config or config["Transform"] is None:
+            self.use_transform = False
+        else:
+            self.use_transform = True
+            config["Transform"]["in_channels"] = in_channels
+            self.transform = build_transform(config["Transform"])
+            in_channels = self.transform.out_channels
+
+        # build backbone, backbone is need for del, rec and cls
+        if "Backbone" not in config or config["Backbone"] is None:
+            self.use_backbone = False
+        else:
+            self.use_backbone = True
+            config["Backbone"]["in_channels"] = in_channels
+            self.backbone = build_backbone(config["Backbone"], model_type)
+            in_channels = self.backbone.out_channels
+
+        # build neck
+        # for rec, neck can be cnn,rnn or reshape(None)
+        # for det, neck can be FPN, BIFPN and so on.
+        # for cls, neck should be none
+        if "Neck" not in config or config["Neck"] is None:
+            self.use_neck = False
+        else:
+            self.use_neck = True
+            config["Neck"]["in_channels"] = in_channels
+            self.neck = build_neck(config["Neck"])
+            in_channels = self.neck.out_channels
+
+        # # build head, head is need for det, rec and cls
+        if "Head" not in config or config["Head"] is None:
+            self.use_head = False
+        else:
+            self.use_head = True
+            config["Head"]["in_channels"] = in_channels
+            self.head = build_head(config["Head"])
+
+        self.return_all_feats = config.get("return_all_feats", False)
+
+    def forward(self, x, data=None):
+        y = dict()
+        if self.use_transform:
+            x = self.transform(x)
+        if self.use_backbone:
+            x = self.backbone(x)
+        if isinstance(x, dict):
+            y.update(x)
+        else:
+            y["backbone_out"] = x
+        final_name = "backbone_out"
+        if self.use_neck:
+            x = self.neck(x)
+            if isinstance(x, dict):
+                y.update(x)
+            else:
+                y["neck_out"] = x
+            final_name = "neck_out"
+        if self.use_head:
+            x = self.head(x, targets=data)
+            # for multi head, save ctc neck out for udml
+            if isinstance(x, dict) and "ctc_neck" in x.keys():
+                y["neck_out"] = x["ctc_neck"]
+                y["head_out"] = x
+            elif isinstance(x, dict):
+                y.update(x)
+            else:
+                y["head_out"] = x
+            final_name = "head_out"
+        if self.return_all_feats:
+            if self.training:
+                return y
+            elif isinstance(x, dict):
+                return x
+            else:
+                return {final_name: x}
+        else:
+            return x