# 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 numbers import Integral from paddle import fluid from ppdet.core.workspace import register, serializable __all__ = [ 'AnchorGenerator', 'RPNTargetAssign', 'GenerateProposals', 'MultiClassNMS', 'BBoxAssigner', 'MaskAssigner', 'RoIAlign', 'RoIPool', 'MultiBoxHead', 'SSDOutputDecoder', 'SSDMetric', 'RetinaTargetAssign', 'RetinaOutputDecoder' ] @register @serializable class AnchorGenerator(object): __op__ = fluid.layers.anchor_generator __append_doc__ = True def __init__(self, stride=[16.0, 16.0], anchor_sizes=[32, 64, 128, 256, 512], aspect_ratios=[0.5, 1., 2.], variance=[1., 1., 1., 1.]): super(AnchorGenerator, self).__init__() self.anchor_sizes = anchor_sizes self.aspect_ratios = aspect_ratios self.variance = variance self.stride = stride @register @serializable class RPNTargetAssign(object): __op__ = fluid.layers.rpn_target_assign __append_doc__ = True def __init__(self, rpn_batch_size_per_im=256, rpn_straddle_thresh=0., rpn_fg_fraction=0.5, rpn_positive_overlap=0.7, rpn_negative_overlap=0.3, use_random=True): super(RPNTargetAssign, self).__init__() self.rpn_batch_size_per_im = rpn_batch_size_per_im self.rpn_straddle_thresh = rpn_straddle_thresh self.rpn_fg_fraction = rpn_fg_fraction self.rpn_positive_overlap = rpn_positive_overlap self.rpn_negative_overlap = rpn_negative_overlap self.use_random = use_random @register @serializable class GenerateProposals(object): __op__ = fluid.layers.generate_proposals __append_doc__ = True def __init__(self, pre_nms_top_n=6000, post_nms_top_n=1000, nms_thresh=.5, min_size=.1, eta=1.): super(GenerateProposals, self).__init__() self.pre_nms_top_n = pre_nms_top_n self.post_nms_top_n = post_nms_top_n self.nms_thresh = nms_thresh self.min_size = min_size self.eta = eta @register class MaskAssigner(object): __op__ = fluid.layers.generate_mask_labels __append_doc__ = True def __init__(self, num_classes=81, resolution=14): super(MaskAssigner, self).__init__() self.num_classes = num_classes self.resolution = resolution @register @serializable class MultiClassNMS(object): __op__ = fluid.layers.multiclass_nms __append_doc__ = True def __init__(self, score_threshold=.05, nms_top_k=-1, keep_top_k=100, nms_threshold=.5, normalized=False, nms_eta=1.0, background_label=0): super(MultiClassNMS, self).__init__() 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 @register class BBoxAssigner(object): __op__ = fluid.layers.generate_proposal_labels __append_doc__ = True def __init__(self, batch_size_per_im=512, fg_fraction=.25, fg_thresh=.5, bg_thresh_hi=.5, bg_thresh_lo=0., bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], num_classes=81, shuffle_before_sample=True): super(BBoxAssigner, self).__init__() self.batch_size_per_im = batch_size_per_im self.fg_fraction = fg_fraction self.fg_thresh = fg_thresh self.bg_thresh_hi = bg_thresh_hi self.bg_thresh_lo = bg_thresh_lo self.bbox_reg_weights = bbox_reg_weights self.class_nums = num_classes self.use_random = shuffle_before_sample @register class RoIAlign(object): __op__ = fluid.layers.roi_align __append_doc__ = True def __init__(self, resolution=7, spatial_scale=1. / 16, sampling_ratio=0): super(RoIAlign, self).__init__() if isinstance(resolution, Integral): resolution = [resolution, resolution] self.pooled_height = resolution[0] self.pooled_width = resolution[1] self.spatial_scale = spatial_scale self.sampling_ratio = sampling_ratio @register class RoIPool(object): __op__ = fluid.layers.roi_pool __append_doc__ = True def __init__(self, resolution=7, spatial_scale=1. / 16): super(RoIPool, self).__init__() if isinstance(resolution, Integral): resolution = [resolution, resolution] self.pooled_height = resolution[0] self.pooled_width = resolution[1] self.spatial_scale = spatial_scale @register class MultiBoxHead(object): __op__ = fluid.layers.multi_box_head __append_doc__ = True def __init__(self, min_ratio=20, max_ratio=90, min_sizes=[60.0, 105.0, 150.0, 195.0, 240.0, 285.0], max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0], aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]], base_size=300, offset=0.5, flip=True): super(MultiBoxHead, self).__init__() self.min_ratio = min_ratio self.max_ratio = max_ratio self.min_sizes = min_sizes self.max_sizes = max_sizes self.aspect_ratios = aspect_ratios self.base_size = base_size self.offset = offset self.flip = flip @register @serializable class SSDOutputDecoder(object): __op__ = fluid.layers.detection_output __append_doc__ = True def __init__(self, nms_threshold=0.45, nms_top_k=400, keep_top_k=200, score_threshold=0.01, nms_eta=1.0, background_label=0): super(SSDOutputDecoder, self).__init__() self.nms_threshold = nms_threshold self.background_label = background_label self.nms_top_k = nms_top_k self.keep_top_k = keep_top_k self.score_threshold = score_threshold self.nms_eta = nms_eta @register @serializable class SSDMetric(object): __op__ = fluid.metrics.DetectionMAP __append_doc__ = True def __init__(self, overlap_threshold=0.5, evaluate_difficult=False, ap_version='integral'): super(SSDMetric, self).__init__() self.overlap_threshold = overlap_threshold self.evaluate_difficult = evaluate_difficult self.ap_version = ap_version @register @serializable class RetinaTargetAssign(object): __op__ = fluid.layers.retinanet_target_assign __append_doc__ = True def __init__(self, positive_overlap=0.5, negative_overlap=0.4): super(RetinaTargetAssign, self).__init__() self.positive_overlap = positive_overlap self.negative_overlap = negative_overlap @register @serializable class RetinaOutputDecoder(object): __op__ = fluid.layers.retinanet_detection_output __append_doc__ = True def __init__(self, score_thresh=0.05, nms_thresh=0.3, pre_nms_top_n=1000, detections_per_im=100, nms_eta=1.0): super(RetinaOutputDecoder, self).__init__() self.score_threshold = score_thresh self.nms_threshold = nms_thresh self.nms_top_k = pre_nms_top_n self.keep_top_k = detections_per_im self.nms_eta = nms_eta