# 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 from collections import OrderedDict from paddle import fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.initializer import Normal, Xavier from paddle.fluid.regularizer import L2Decay from paddle.fluid.initializer import MSRA __all__ = ['BBoxHead', 'TwoFCHead'] class TwoFCHead(object): """ RCNN head with two Fully Connected layers Args: mlp_dim (int): num of filters for the fc layers """ def __init__(self, mlp_dim=1024): super(TwoFCHead, self).__init__() self.mlp_dim = mlp_dim def __call__(self, roi_feat): fan = roi_feat.shape[1] * roi_feat.shape[2] * roi_feat.shape[3] fc6 = fluid.layers.fc( input=roi_feat, size=self.mlp_dim, act='relu', name='fc6', param_attr=ParamAttr( name='fc6_w', initializer=Xavier(fan_out=fan)), bias_attr=ParamAttr( name='fc6_b', learning_rate=2., regularizer=L2Decay(0.))) head_feat = fluid.layers.fc( input=fc6, size=self.mlp_dim, act='relu', name='fc7', param_attr=ParamAttr(name='fc7_w', initializer=Xavier()), bias_attr=ParamAttr( name='fc7_b', learning_rate=2., regularizer=L2Decay(0.))) return head_feat class BBoxHead(object): def __init__( self, head, #box_coder prior_box_var=[0.1, 0.1, 0.2, 0.2], code_type='decode_center_size', box_normalized=False, axis=1, #MultiClassNMS score_threshold=.05, nms_top_k=-1, keep_top_k=100, nms_threshold=.5, normalized=False, nms_eta=1.0, background_label=0, #bbox_loss sigma=1.0, num_classes=81, bbox_loss_type='SmoothL1Loss'): super(BBoxHead, self).__init__() self.head = head self.prior_box_var = prior_box_var self.code_type = code_type self.box_normalized = box_normalized self.axis = axis self.score_threshold = score_threshold self.nms_top_k = nms_top_k self.keep_top_k = keep_top_k self.nms_threshold = nms_threshold self.normalized = normalized self.nms_eta = nms_eta self.background_label = background_label self.sigma = sigma self.num_classes = num_classes self.head_feat = None self.bbox_loss_type = bbox_loss_type def get_head_feat(self, input=None): """ Get the bbox head feature map. """ if input is not None: feat = self.head(input) if isinstance(feat, OrderedDict): feat = list(feat.values())[0] self.head_feat = feat return self.head_feat def _get_output(self, roi_feat): """ Get bbox head output. Args: roi_feat (Variable): RoI feature from RoIExtractor. Returns: cls_score(Variable): Output of rpn head with shape of [N, num_anchors, H, W]. bbox_pred(Variable): Output of rpn head with shape of [N, num_anchors * 4, H, W]. """ head_feat = self.get_head_feat(roi_feat) # when ResNetC5 output a single feature map if not isinstance(self.head, TwoFCHead): head_feat = fluid.layers.pool2d( head_feat, pool_type='avg', global_pooling=True) cls_score = fluid.layers.fc( input=head_feat, size=self.num_classes, act=None, name='cls_score', param_attr=ParamAttr( name='cls_score_w', initializer=Normal(loc=0.0, scale=0.01)), bias_attr=ParamAttr( name='cls_score_b', learning_rate=2., regularizer=L2Decay(0.))) bbox_pred = fluid.layers.fc( input=head_feat, size=4 * self.num_classes, act=None, name='bbox_pred', param_attr=ParamAttr( name='bbox_pred_w', initializer=Normal(loc=0.0, scale=0.001)), bias_attr=ParamAttr( name='bbox_pred_b', learning_rate=2., regularizer=L2Decay(0.))) return cls_score, bbox_pred def get_loss(self, roi_feat, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights): """ Get bbox_head loss. Args: roi_feat (Variable): RoI feature from RoIExtractor. labels_int32(Variable): Class label of a RoI with shape [P, 1]. P is the number of RoI. bbox_targets(Variable): Box label of a RoI with shape [P, 4 * class_nums]. bbox_inside_weights(Variable): Indicates whether a box should contribute to loss. Same shape as bbox_targets. bbox_outside_weights(Variable): Indicates whether a box should contribute to loss. Same shape as bbox_targets. Return: Type: Dict loss_cls(Variable): bbox_head loss. loss_bbox(Variable): bbox_head loss. """ cls_score, bbox_pred = self._get_output(roi_feat) labels_int64 = fluid.layers.cast(x=labels_int32, dtype='int64') labels_int64.stop_gradient = True loss_cls = fluid.layers.softmax_with_cross_entropy( logits=cls_score, label=labels_int64, numeric_stable_mode=True) loss_cls = fluid.layers.reduce_mean(loss_cls) if self.bbox_loss_type == 'CiouLoss': from .loss.diou_loss import DiouLoss loss_obj = DiouLoss(loss_weight=10., is_cls_agnostic=False, num_classes=self.num_classes, use_complete_iou_loss=True) elif self.bbox_loss_type == 'DiouLoss': from .loss.diou_loss import DiouLoss loss_obj = DiouLoss(loss_weight=12., is_cls_agnostic=False, num_classes=self.num_classes, use_complete_iou_loss=False) elif self.bbox_loss_type == 'GiouLoss': from .loss.giou_loss import GiouLoss loss_obj = GiouLoss(loss_weight=10., is_cls_agnostic=False, num_classes=self.num_classes) else: from .loss.smoothl1_loss import SmoothL1Loss loss_obj = SmoothL1Loss(self.sigma) loss_bbox = loss_obj( x=bbox_pred, y=bbox_targets, inside_weight=bbox_inside_weights, outside_weight=bbox_outside_weights) loss_bbox = fluid.layers.reduce_mean(loss_bbox) return {'loss_cls': loss_cls, 'loss_bbox': loss_bbox} def get_prediction(self, roi_feat, rois, im_info, im_shape, return_box_score=False): """ Get prediction bounding box in test stage. Args: roi_feat (Variable): RoI feature from RoIExtractor. rois (Variable): Output of generate_proposals in rpn head. im_info (Variable): A 2-D LoDTensor with shape [B, 3]. B is the number of input images, each element consists of im_height, im_width, im_scale. im_shape (Variable): Actual shape of original image with shape [B, 3]. B is the number of images, each element consists of original_height, original_width, 1 Returns: pred_result(Variable): Prediction result with shape [N, 6]. Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]. N is the total number of prediction. """ cls_score, bbox_pred = self._get_output(roi_feat) im_scale = fluid.layers.slice(im_info, [1], starts=[2], ends=[3]) im_scale = fluid.layers.sequence_expand(im_scale, rois) boxes = rois / im_scale cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False) bbox_pred = fluid.layers.reshape(bbox_pred, (-1, self.num_classes, 4)) decoded_box = fluid.layers.box_coder( prior_box=boxes, target_box=bbox_pred, prior_box_var=self.prior_box_var, code_type=self.code_type, box_normalized=self.box_normalized, axis=self.axis) cliped_box = fluid.layers.box_clip(input=decoded_box, im_info=im_shape) if return_box_score: return {'bbox': cliped_box, 'score': cls_prob} if self.bbox_loss_type == 'CiouLoss': from .nms import MultiClassDiouNMS nms_obj = MultiClassDiouNMS(score_threshold=self.score_threshold, nms_threshold=self.nms_threshold, keep_top_k=self.keep_top_k) pred_result = nms_obj(bboxes=cliped_box, scores=cls_prob) else: pred_result = fluid.layers.multiclass_nms( bboxes=cliped_box, scores=cls_prob, score_threshold=self.score_threshold, nms_top_k=self.nms_top_k, keep_top_k=self.keep_top_k, nms_threshold=self.nms_threshold, normalized=self.normalized, nms_eta=self.nms_eta, background_label=self.background_label) return {'bbox': pred_result}