更换文档检测模型

paddle_detection/ppdet/modeling/architectures/blazeface.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 ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+import paddle
+import paddle.nn.functional as F
+
+__all__ = ['BlazeFace']
+
+
+@register
+class BlazeFace(BaseArch):
+    """
+    BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,
+               see https://arxiv.org/abs/1907.05047
+
+    Args:
+        backbone (nn.Layer): backbone instance
+        neck (nn.Layer): neck instance
+        blaze_head (nn.Layer): `blazeHead` instance
+        post_process (object): `BBoxPostProcess` instance
+    """
+
+    __category__ = 'architecture'
+    __inject__ = ['post_process']
+
+    def __init__(self, backbone, blaze_head, neck, post_process):
+        super(BlazeFace, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.blaze_head = blaze_head
+        self.post_process = post_process
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        # backbone
+        backbone = create(cfg['backbone'])
+        # fpn
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
+        # head
+        kwargs = {'input_shape': neck.out_shape}
+        blaze_head = create(cfg['blaze_head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            'blaze_head': blaze_head,
+        }
+
+    def _forward(self):
+        # Backbone
+        body_feats = self.backbone(self.inputs)
+        # neck
+        neck_feats = self.neck(body_feats)
+        # blaze Head
+        if self.training:
+            return self.blaze_head(neck_feats, self.inputs['image'],
+                                   self.inputs['gt_bbox'],
+                                   self.inputs['gt_class'])
+        else:
+            preds, anchors = self.blaze_head(neck_feats, self.inputs['image'])
+            bbox, bbox_num, nms_keep_idx = self.post_process(
+                preds, anchors, self.inputs['im_shape'],
+                self.inputs['scale_factor'])
+            if self.use_extra_data:
+                extra_data = {}  # record the bbox output before nms, such like scores and nms_keep_idx
+                """extra_data:{
+                            'scores': predict scores,
+                            'nms_keep_idx': bbox index before nms,
+                           }
+                           """
+                preds_logits = preds[1]  # [[1xNumBBoxNumClass]]
+                extra_data['scores'] = F.softmax(paddle.concat(
+                    preds_logits, axis=1)).transpose([0, 2, 1])
+                extra_data['logits'] = paddle.concat(
+                    preds_logits, axis=1).transpose([0, 2, 1])
+                extra_data['nms_keep_idx'] = nms_keep_idx  # bbox index before nms
+                return bbox, bbox_num, extra_data
+            else:
+                return bbox, bbox_num
+
+    def get_loss(self, ):
+        return {"loss": self._forward()}
+
+    def get_pred(self):
+        if self.use_extra_data:
+            bbox_pred, bbox_num, extra_data = self._forward()
+            output = {
+                "bbox": bbox_pred,
+                "bbox_num": bbox_num,
+                "extra_data": extra_data
+            }
+        else:
+            bbox_pred, bbox_num = self._forward()
+            output = {
+                "bbox": bbox_pred,
+                "bbox_num": bbox_num,
+            }
+
+        return output