# Copyright (c) 2019 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 ppdet.modeling.ops import MultiClassNMS from ppdet.core.workspace import register, serializable __all__ = ['BBoxHead', 'TwoFCHead'] @register @serializable class BoxCoder(object): __op__ = fluid.layers.box_coder __append_doc__ = True def __init__(self, prior_box_var=[0.1, 0.1, 0.2, 0.2], code_type='decode_center_size', box_normalized=False, axis=1): super(BoxCoder, self).__init__() self.prior_box_var = prior_box_var self.code_type = code_type self.box_normalized = box_normalized self.axis = axis @register class TwoFCHead(object): """ RCNN head with two Fully Connected layers Args: num_chan (int): num of filters for the fc layers """ def __init__(self, num_chan=1024): super(TwoFCHead, self).__init__() self.num_chan = num_chan 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.num_chan, 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.num_chan, 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 @register class BBoxHead(object): """ RCNN bbox head Args: head (object): the head module instance, e.g., `ResNetC5`, `TwoFCHead` box_coder (object): `BoxCoder` instance nms (object): `MultiClassNMS` instance num_classes: number of output classes """ __inject__ = ['head', 'box_coder', 'nms'] __shared__ = ['num_classes'] def __init__(self, head, box_coder=BoxCoder().__dict__, nms=MultiClassNMS().__dict__, num_classes=81): super(BBoxHead, self).__init__() self.head = head self.num_classes = num_classes self.box_coder = box_coder self.nms = nms if isinstance(box_coder, dict): self.box_coder = BoxCoder(**box_coder) if isinstance(nms, dict): self.nms = MultiClassNMS(**nms) self.head_feat = None 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) loss_bbox = fluid.layers.smooth_l1( x=bbox_pred, y=bbox_targets, inside_weight=bbox_inside_weights, outside_weight=bbox_outside_weights, sigma=1.0) 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): """ Get prediction bounding box in test stage. Args: 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. cls_score (Variable), bbox_pred(Variable): Output of get_output. 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 = self.box_coder(prior_box=boxes, target_box=bbox_pred) cliped_box = fluid.layers.box_clip(input=decoded_box, im_info=im_shape) pred_result = self.nms(bboxes=cliped_box, scores=cls_prob) return {'bbox': pred_result}