更换文档检测模型

paddle_detection/ppdet/modeling/architectures/fcos.py

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+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ['FCOS', 'ARSL_FCOS']
+
+
+@register
+class FCOS(BaseArch):
+    """
+    FCOS network, see https://arxiv.org/abs/1904.01355
+
+    Args:
+        backbone (object): backbone instance
+        neck (object): 'FPN' instance
+        fcos_head (object): 'FCOSHead' instance
+        ssod_loss (object): 'SSODFCOSLoss' instance, only used for semi-det(ssod) by DenseTeacher
+    """
+
+    __category__ = 'architecture'
+    __inject__ = ['ssod_loss']
+
+    def __init__(self,
+                 backbone='ResNet',
+                 neck='FPN',
+                 fcos_head='FCOSHead',
+                 ssod_loss='SSODFCOSLoss'):
+        super(FCOS, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.fcos_head = fcos_head
+
+        # for ssod, semi-det
+        self.is_teacher = False
+        self.ssod_loss = ssod_loss
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
+
+        kwargs = {'input_shape': neck.out_shape}
+        fcos_head = create(cfg['fcos_head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            "fcos_head": fcos_head,
+        }
+
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        fpn_feats = self.neck(body_feats)
+
+        self.is_teacher = self.inputs.get('is_teacher', False)
+        if self.training or self.is_teacher:
+            losses = self.fcos_head(fpn_feats, self.inputs)
+            return losses
+        else:
+            fcos_head_outs = self.fcos_head(fpn_feats)
+            bbox_pred, bbox_num = self.fcos_head.post_process(
+                fcos_head_outs, self.inputs['scale_factor'])
+            return {'bbox': bbox_pred, 'bbox_num': bbox_num}
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        return self._forward()
+
+    def get_loss_keys(self):
+        return ['loss_cls', 'loss_box', 'loss_quality']
+
+    def get_ssod_loss(self, student_head_outs, teacher_head_outs, train_cfg):
+        ssod_losses = self.ssod_loss(student_head_outs, teacher_head_outs,
+                                     train_cfg)
+        return ssod_losses
+
+
+@register
+class ARSL_FCOS(BaseArch):
+    """
+    FCOS ARSL network, see https://arxiv.org/abs/
+
+    Args:
+        backbone (object): backbone instance
+        neck (object): 'FPN' instance
+        fcos_head (object): 'FCOSHead_ARSL' instance
+        fcos_cr_loss (object): 'FCOSLossCR' instance, only used for semi-det(ssod) by ARSL
+    """
+
+    __category__ = 'architecture'
+    __inject__ = ['fcos_cr_loss']
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 fcos_head='FCOSHead_ARSL',
+                 fcos_cr_loss='FCOSLossCR'):
+        super(ARSL_FCOS, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.fcos_head = fcos_head
+        self.fcos_cr_loss = fcos_cr_loss
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
+
+        kwargs = {'input_shape': neck.out_shape}
+        fcos_head = create(cfg['fcos_head'], **kwargs)
+
+        # consistency regularization loss
+        fcos_cr_loss = create(cfg['fcos_cr_loss'])
+
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            'fcos_head': fcos_head,
+            'fcos_cr_loss': fcos_cr_loss,
+        }
+
+    def forward(self, inputs, branch="supervised", teacher_prediction=None):
+        assert branch in ['supervised', 'semi_supervised'], \
+            print('In ARSL, type must be supervised or semi_supervised.')
+
+        if self.data_format == 'NHWC':
+            image = inputs['image']
+            inputs['image'] = paddle.transpose(image, [0, 2, 3, 1])
+        self.inputs = inputs
+
+        if self.training:
+            if branch == "supervised":
+                out = self.get_loss()
+            else:
+                out = self.get_pseudo_loss(teacher_prediction)
+        else:
+            # norm test
+            if branch == "supervised":
+                out = self.get_pred()
+                # predict pseudo labels
+            else:
+                out = self.get_pseudo_pred()
+        return out
+
+    # model forward 
+    def model_forward(self):
+        body_feats = self.backbone(self.inputs)
+        fpn_feats = self.neck(body_feats)
+        fcos_head_outs = self.fcos_head(fpn_feats)
+        return fcos_head_outs
+
+    # supervised loss for labeled data
+    def get_loss(self):
+        loss = {}
+        tag_labels, tag_bboxes, tag_centerness = [], [], []
+        for i in range(len(self.fcos_head.fpn_stride)):
+            # labels, reg_target, centerness
+            k_lbl = 'labels{}'.format(i)
+            if k_lbl in self.inputs:
+                tag_labels.append(self.inputs[k_lbl])
+            k_box = 'reg_target{}'.format(i)
+            if k_box in self.inputs:
+                tag_bboxes.append(self.inputs[k_box])
+            k_ctn = 'centerness{}'.format(i)
+            if k_ctn in self.inputs:
+                tag_centerness.append(self.inputs[k_ctn])
+        fcos_head_outs = self.model_forward()
+        loss_fcos = self.fcos_head.get_loss(fcos_head_outs, tag_labels,
+                                            tag_bboxes, tag_centerness)
+        loss.update(loss_fcos)
+        return loss
+
+    # unsupervised loss for unlabeled data
+    def get_pseudo_loss(self, teacher_prediction):
+        loss = {}
+        fcos_head_outs = self.model_forward()
+        unsup_loss = self.fcos_cr_loss(fcos_head_outs, teacher_prediction)
+        for k in unsup_loss.keys():
+            loss[k + '_pseudo'] = unsup_loss[k]
+        return loss
+
+    # get detection results for test, decode and rescale the results to original size
+    def get_pred(self):
+        fcos_head_outs = self.model_forward()
+        scale_factor = self.inputs['scale_factor']
+        bbox_pred, bbox_num = self.fcos_head.post_process(fcos_head_outs,
+                                                          scale_factor)
+        output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
+        return output
+
+    # generate pseudo labels to guide student
+    def get_pseudo_pred(self):
+        fcos_head_outs = self.model_forward()
+        pred_cls, pred_loc, pred_iou = fcos_head_outs[1:]  # 0 is locations
+        for lvl, _ in enumerate(pred_loc):
+            pred_loc[lvl] = pred_loc[lvl] / self.fcos_head.fpn_stride[lvl]
+
+        return [pred_cls, pred_loc, pred_iou, self.fcos_head.fpn_stride]