# 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. import numpy as np from numbers import Integral import math import six from paddle import fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.regularizer import L2Decay from ppdet.core.workspace import register, serializable from ppdet.utils.bbox_utils import bbox_overlaps, box_to_delta __all__ = [ 'AnchorGenerator', 'DropBlock', 'RPNTargetAssign', 'GenerateProposals', 'MultiClassNMS', 'BBoxAssigner', 'MaskAssigner', 'RoIAlign', 'RoIPool', 'MultiBoxHead', 'SSDLiteMultiBoxHead', 'SSDOutputDecoder', 'RetinaTargetAssign', 'RetinaOutputDecoder', 'ConvNorm', 'DeformConvNorm', 'MultiClassSoftNMS', 'LibraBBoxAssigner' ] def _conv_offset(input, filter_size, stride, padding, act=None, name=None): out_channel = filter_size * filter_size * 3 out = fluid.layers.conv2d( input, num_filters=out_channel, filter_size=filter_size, stride=stride, padding=padding, param_attr=ParamAttr( initializer=fluid.initializer.Constant(value=0), name=name + ".w_0"), bias_attr=ParamAttr( initializer=fluid.initializer.Constant(value=0), name=name + ".b_0"), act=act, name=name) return out def DeformConvNorm(input, num_filters, filter_size, stride=1, groups=1, norm_decay=0., norm_type='affine_channel', norm_groups=32, dilation=1, lr_scale=1, freeze_norm=False, act=None, norm_name=None, initializer=None, bias_attr=False, name=None): assert norm_type in ['bn', 'sync_bn', 'affine_channel'] if bias_attr: bias_para = ParamAttr( name=name + "_bias", initializer=fluid.initializer.Constant(value=0), learning_rate=lr_scale * 2) else: bias_para = False offset_mask = _conv_offset( input=input, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, act=None, name=name + "_conv_offset") offset_channel = filter_size**2 * 2 mask_channel = filter_size**2 offset, mask = fluid.layers.split( input=offset_mask, num_or_sections=[offset_channel, mask_channel], dim=1) mask = fluid.layers.sigmoid(mask) conv = fluid.layers.deformable_conv( input=input, offset=offset, mask=mask, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2 * dilation, dilation=dilation, groups=groups, deformable_groups=1, im2col_step=1, param_attr=ParamAttr( name=name + "_weights", initializer=initializer, learning_rate=lr_scale), bias_attr=bias_para, name=name + ".conv2d.output.1") norm_lr = 0. if freeze_norm else 1. pattr = ParamAttr( name=norm_name + '_scale', learning_rate=norm_lr * lr_scale, regularizer=L2Decay(norm_decay)) battr = ParamAttr( name=norm_name + '_offset', learning_rate=norm_lr * lr_scale, regularizer=L2Decay(norm_decay)) if norm_type in ['bn', 'sync_bn']: global_stats = True if freeze_norm else False out = fluid.layers.batch_norm( input=conv, act=act, name=norm_name + '.output.1', param_attr=pattr, bias_attr=battr, moving_mean_name=norm_name + '_mean', moving_variance_name=norm_name + '_variance', use_global_stats=global_stats) scale = fluid.framework._get_var(pattr.name) bias = fluid.framework._get_var(battr.name) elif norm_type == 'gn': out = fluid.layers.group_norm( input=conv, act=act, name=norm_name + '.output.1', groups=norm_groups, param_attr=pattr, bias_attr=battr) scale = fluid.framework._get_var(pattr.name) bias = fluid.framework._get_var(battr.name) elif norm_type == 'affine_channel': scale = fluid.layers.create_parameter( shape=[conv.shape[1]], dtype=conv.dtype, attr=pattr, default_initializer=fluid.initializer.Constant(1.)) bias = fluid.layers.create_parameter( shape=[conv.shape[1]], dtype=conv.dtype, attr=battr, default_initializer=fluid.initializer.Constant(0.)) out = fluid.layers.affine_channel( x=conv, scale=scale, bias=bias, act=act) if freeze_norm: scale.stop_gradient = True bias.stop_gradient = True return out def ConvNorm(input, num_filters, filter_size, stride=1, groups=1, norm_decay=0., norm_type='affine_channel', norm_groups=32, dilation=1, lr_scale=1, freeze_norm=False, act=None, norm_name=None, initializer=None, bias_attr=False, name=None): fan = num_filters if bias_attr: bias_para = ParamAttr( name=name + "_bias", initializer=fluid.initializer.Constant(value=0), learning_rate=lr_scale * 2) else: bias_para = False conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=((filter_size - 1) // 2) * dilation, dilation=dilation, groups=groups, act=None, param_attr=ParamAttr( name=name + "_weights", initializer=initializer, learning_rate=lr_scale), bias_attr=bias_para, name=name + '.conv2d.output.1') norm_lr = 0. if freeze_norm else 1. pattr = ParamAttr( name=norm_name + '_scale', learning_rate=norm_lr * lr_scale, regularizer=L2Decay(norm_decay)) battr = ParamAttr( name=norm_name + '_offset', learning_rate=norm_lr * lr_scale, regularizer=L2Decay(norm_decay)) if norm_type in ['bn', 'sync_bn']: global_stats = True if freeze_norm else False out = fluid.layers.batch_norm( input=conv, act=act, name=norm_name + '.output.1', param_attr=pattr, bias_attr=battr, moving_mean_name=norm_name + '_mean', moving_variance_name=norm_name + '_variance', use_global_stats=global_stats) scale = fluid.framework._get_var(pattr.name) bias = fluid.framework._get_var(battr.name) elif norm_type == 'gn': out = fluid.layers.group_norm( input=conv, act=act, name=norm_name + '.output.1', groups=norm_groups, param_attr=pattr, bias_attr=battr) scale = fluid.framework._get_var(pattr.name) bias = fluid.framework._get_var(battr.name) elif norm_type == 'affine_channel': scale = fluid.layers.create_parameter( shape=[conv.shape[1]], dtype=conv.dtype, attr=pattr, default_initializer=fluid.initializer.Constant(1.)) bias = fluid.layers.create_parameter( shape=[conv.shape[1]], dtype=conv.dtype, attr=battr, default_initializer=fluid.initializer.Constant(0.)) out = fluid.layers.affine_channel( x=conv, scale=scale, bias=bias, act=act) if freeze_norm: scale.stop_gradient = True bias.stop_gradient = True return out def DropBlock(input, block_size, keep_prob, is_test): if is_test: return input def CalculateGamma(input, block_size, keep_prob): input_shape = fluid.layers.shape(input) feat_shape_tmp = fluid.layers.slice(input_shape, [0], [3], [4]) feat_shape_tmp = fluid.layers.cast(feat_shape_tmp, dtype="float32") feat_shape_t = fluid.layers.reshape(feat_shape_tmp, [1, 1, 1, 1]) feat_area = fluid.layers.pow(feat_shape_t, factor=2) block_shape_t = fluid.layers.fill_constant( shape=[1, 1, 1, 1], value=block_size, dtype='float32') block_area = fluid.layers.pow(block_shape_t, factor=2) useful_shape_t = feat_shape_t - block_shape_t + 1 useful_area = fluid.layers.pow(useful_shape_t, factor=2) upper_t = feat_area * (1 - keep_prob) bottom_t = block_area * useful_area output = upper_t / bottom_t return output gamma = CalculateGamma(input, block_size=block_size, keep_prob=keep_prob) input_shape = fluid.layers.shape(input) p = fluid.layers.expand_as(gamma, input) input_shape_tmp = fluid.layers.cast(input_shape, dtype="int64") random_matrix = fluid.layers.uniform_random( input_shape_tmp, dtype='float32', min=0.0, max=1.0) one_zero_m = fluid.layers.less_than(random_matrix, p) one_zero_m.stop_gradient = True one_zero_m = fluid.layers.cast(one_zero_m, dtype="float32") mask_flag = fluid.layers.pool2d( one_zero_m, pool_size=block_size, pool_type='max', pool_stride=1, pool_padding=block_size // 2) mask = 1.0 - mask_flag elem_numel = fluid.layers.reduce_prod(input_shape) elem_numel_m = fluid.layers.cast(elem_numel, dtype="float32") elem_numel_m.stop_gradient = True elem_sum = fluid.layers.reduce_sum(mask) elem_sum_m = fluid.layers.cast(elem_sum, dtype="float32") elem_sum_m.stop_gradient = True output = input * mask * elem_numel_m / elem_sum_m return output @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 __shared__ = ['num_classes'] 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 @serializable class MultiClassSoftNMS(object): def __init__( self, score_threshold=0.01, keep_top_k=300, softnms_sigma=0.5, normalized=False, background_label=0, ): super(MultiClassSoftNMS, self).__init__() self.score_threshold = score_threshold self.keep_top_k = keep_top_k self.softnms_sigma = softnms_sigma self.normalized = normalized self.background_label = background_label def __call__(self, bboxes, scores): def create_tmp_var(program, name, dtype, shape, lod_level): return program.current_block().create_var( name=name, dtype=dtype, shape=shape, lod_level=lod_level) def _soft_nms_for_cls(dets, sigma, thres): """soft_nms_for_cls""" dets_final = [] while len(dets) > 0: maxpos = np.argmax(dets[:, 0]) dets_final.append(dets[maxpos].copy()) ts, tx1, ty1, tx2, ty2 = dets[maxpos] scores = dets[:, 0] # force remove bbox at maxpos scores[maxpos] = -1 x1 = dets[:, 1] y1 = dets[:, 2] x2 = dets[:, 3] y2 = dets[:, 4] eta = 0 if self.normalized else 1 areas = (x2 - x1 + eta) * (y2 - y1 + eta) xx1 = np.maximum(tx1, x1) yy1 = np.maximum(ty1, y1) xx2 = np.minimum(tx2, x2) yy2 = np.minimum(ty2, y2) w = np.maximum(0.0, xx2 - xx1 + eta) h = np.maximum(0.0, yy2 - yy1 + eta) inter = w * h ovr = inter / (areas + areas[maxpos] - inter) weight = np.exp(-(ovr * ovr) / sigma) scores = scores * weight idx_keep = np.where(scores >= thres) dets[:, 0] = scores dets = dets[idx_keep] dets_final = np.array(dets_final).reshape(-1, 5) return dets_final def _soft_nms(bboxes, scores): bboxes = np.array(bboxes) scores = np.array(scores) class_nums = scores.shape[-1] softnms_thres = self.score_threshold softnms_sigma = self.softnms_sigma keep_top_k = self.keep_top_k cls_boxes = [[] for _ in range(class_nums)] cls_ids = [[] for _ in range(class_nums)] start_idx = 1 if self.background_label == 0 else 0 for j in range(start_idx, class_nums): inds = np.where(scores[:, j] >= softnms_thres)[0] scores_j = scores[inds, j] rois_j = bboxes[inds, j, :] dets_j = np.hstack((scores_j[:, np.newaxis], rois_j)).astype( np.float32, copy=False) cls_rank = np.argsort(-dets_j[:, 0]) dets_j = dets_j[cls_rank] cls_boxes[j] = _soft_nms_for_cls( dets_j, sigma=softnms_sigma, thres=softnms_thres) cls_ids[j] = np.array([j] * cls_boxes[j].shape[0]).reshape(-1, 1) cls_boxes = np.vstack(cls_boxes[start_idx:]) cls_ids = np.vstack(cls_ids[start_idx:]) pred_result = np.hstack([cls_ids, cls_boxes]) # Limit to max_per_image detections **over all classes** image_scores = cls_boxes[:, 0] if len(image_scores) > keep_top_k: image_thresh = np.sort(image_scores)[-keep_top_k] keep = np.where(cls_boxes[:, 0] >= image_thresh)[0] pred_result = pred_result[keep, :] res = fluid.LoDTensor() res.set_lod([[0, pred_result.shape[0]]]) if pred_result.shape[0] == 0: pred_result = np.array([[1]], dtype=np.float32) res.set(pred_result, fluid.CPUPlace()) return res pred_result = create_tmp_var( fluid.default_main_program(), name='softnms_pred_result', dtype='float32', shape=[6], lod_level=1) fluid.layers.py_func( func=_soft_nms, x=[bboxes, scores], out=pred_result) return pred_result @register @serializable class MultiClassDiouNMS(object): def __init__( self, score_threshold=0.05, keep_top_k=100, nms_threshold=0.5, normalized=False, background_label=0, ): super(MultiClassDiouNMS, self).__init__() self.score_threshold = score_threshold self.nms_threshold = nms_threshold self.keep_top_k = keep_top_k self.normalized = normalized self.background_label = background_label def __call__(self, bboxes, scores): def create_tmp_var(program, name, dtype, shape, lod_level): return program.current_block().create_var( name=name, dtype=dtype, shape=shape, lod_level=lod_level) def _calc_diou_term(dets1, dets2): eps = 1.e-10 eta = 0 if self.normalized else 1 x1, y1, x2, y2 = dets1[0], dets1[1], dets1[2], dets1[3] x1g, y1g, x2g, y2g = dets2[0], dets2[1], dets2[2], dets2[3] cx = (x1 + x2) / 2 cy = (y1 + y2) / 2 w = x2 - x1 + eta h = y2 - y1 + eta cxg = (x1g + x2g) / 2 cyg = (y1g + y2g) / 2 wg = x2g - x1g + eta hg = y2g - y1g + eta x2 = np.maximum(x1, x2) y2 = np.maximum(y1, y2) # A or B xc1 = np.minimum(x1, x1g) yc1 = np.minimum(y1, y1g) xc2 = np.maximum(x2, x2g) yc2 = np.maximum(y2, y2g) # DIOU term dist_intersection = (cx - cxg)**2 + (cy - cyg)**2 dist_union = (xc2 - xc1)**2 + (yc2 - yc1)**2 diou_term = (dist_intersection + eps) / (dist_union + eps) return diou_term def _diou_nms_for_cls(dets, thres): """_diou_nms_for_cls""" scores = dets[:, 0] x1 = dets[:, 1] y1 = dets[:, 2] x2 = dets[:, 3] y2 = dets[:, 4] eta = 0 if self.normalized else 1 areas = (x2 - x1 + eta) * (y2 - y1 + eta) dt_num = dets.shape[0] order = np.array(range(dt_num)) keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1 + eta) h = np.maximum(0.0, yy2 - yy1 + eta) inter = w * h ovr = inter / (areas[i] + areas[order[1:]] - inter) diou_term = _calc_diou_term([x1[i], y1[i], x2[i], y2[i]], [ x1[order[1:]], y1[order[1:]], x2[order[1:]], y2[order[1:]] ]) inds = np.where(ovr - diou_term <= thres)[0] order = order[inds + 1] dets_final = dets[keep] return dets_final def _diou_nms(bboxes, scores): bboxes = np.array(bboxes) scores = np.array(scores) class_nums = scores.shape[-1] score_threshold = self.score_threshold nms_threshold = self.nms_threshold keep_top_k = self.keep_top_k cls_boxes = [[] for _ in range(class_nums)] cls_ids = [[] for _ in range(class_nums)] start_idx = 1 if self.background_label == 0 else 0 for j in range(start_idx, class_nums): inds = np.where(scores[:, j] >= score_threshold)[0] scores_j = scores[inds, j] rois_j = bboxes[inds, j, :] dets_j = np.hstack((scores_j[:, np.newaxis], rois_j)).astype( np.float32, copy=False) cls_rank = np.argsort(-dets_j[:, 0]) dets_j = dets_j[cls_rank] cls_boxes[j] = _diou_nms_for_cls(dets_j, thres=nms_threshold) cls_ids[j] = np.array([j] * cls_boxes[j].shape[0]).reshape(-1, 1) cls_boxes = np.vstack(cls_boxes[start_idx:]) cls_ids = np.vstack(cls_ids[start_idx:]) pred_result = np.hstack([cls_ids, cls_boxes]) # Limit to max_per_image detections **over all classes** image_scores = cls_boxes[:, 0] if len(image_scores) > keep_top_k: image_thresh = np.sort(image_scores)[-keep_top_k] keep = np.where(cls_boxes[:, 0] >= image_thresh)[0] pred_result = pred_result[keep, :] res = fluid.LoDTensor() res.set_lod([[0, pred_result.shape[0]]]) if pred_result.shape[0] == 0: pred_result = np.array([[1]], dtype=np.float32) res.set(pred_result, fluid.CPUPlace()) return res pred_result = create_tmp_var( fluid.default_main_program(), name='diou_nms_pred_result', dtype='float32', shape=[6], lod_level=1) fluid.layers.py_func( func=_diou_nms, x=[bboxes, scores], out=pred_result) return pred_result @register class BBoxAssigner(object): __op__ = fluid.layers.generate_proposal_labels __append_doc__ = True __shared__ = ['num_classes'] 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 LibraBBoxAssigner(object): __shared__ = ['num_classes'] 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, is_cls_agnostic=False, num_bins=3): super(LibraBBoxAssigner, 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 self.is_cls_agnostic = is_cls_agnostic self.num_bins = num_bins def __call__( self, rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, ): return self.generate_proposal_label_libra( rpn_rois=rpn_rois, gt_classes=gt_classes, is_crowd=is_crowd, gt_boxes=gt_boxes, im_info=im_info, batch_size_per_im=self.batch_size_per_im, fg_fraction=self.fg_fraction, fg_thresh=self.fg_thresh, bg_thresh_hi=self.bg_thresh_hi, bg_thresh_lo=self.bg_thresh_lo, bbox_reg_weights=self.bbox_reg_weights, class_nums=self.class_nums, use_random=self.use_random, is_cls_agnostic=self.is_cls_agnostic, is_cascade_rcnn=False) def generate_proposal_label_libra( self, rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums, use_random, is_cls_agnostic, is_cascade_rcnn): num_bins = self.num_bins def create_tmp_var(program, name, dtype, shape, lod_level=None): return program.current_block().create_var( name=name, dtype=dtype, shape=shape, lod_level=lod_level) def _sample_pos(max_overlaps, max_classes, pos_inds, num_expected): if len(pos_inds) <= num_expected: return pos_inds else: unique_gt_inds = np.unique(max_classes[pos_inds]) num_gts = len(unique_gt_inds) num_per_gt = int(round(num_expected / float(num_gts)) + 1) sampled_inds = [] for i in unique_gt_inds: inds = np.nonzero(max_classes == i)[0] before_len = len(inds) inds = list(set(inds) & set(pos_inds)) after_len = len(inds) if len(inds) > num_per_gt: inds = np.random.choice( inds, size=num_per_gt, replace=False) sampled_inds.extend(list(inds)) # combine as a new sampler if len(sampled_inds) < num_expected: num_extra = num_expected - len(sampled_inds) extra_inds = np.array( list(set(pos_inds) - set(sampled_inds))) assert len(sampled_inds)+len(extra_inds) == len(pos_inds), \ "sum of sampled_inds({}) and extra_inds({}) length must be equal with pos_inds({})!".format( len(sampled_inds), len(extra_inds), len(pos_inds)) if len(extra_inds) > num_extra: extra_inds = np.random.choice( extra_inds, size=num_extra, replace=False) sampled_inds.extend(extra_inds.tolist()) elif len(sampled_inds) > num_expected: sampled_inds = np.random.choice( sampled_inds, size=num_expected, replace=False) return sampled_inds def sample_via_interval(max_overlaps, full_set, num_expected, floor_thr, num_bins, bg_thresh_hi): max_iou = max_overlaps.max() iou_interval = (max_iou - floor_thr) / num_bins per_num_expected = int(num_expected / num_bins) sampled_inds = [] for i in range(num_bins): start_iou = floor_thr + i * iou_interval end_iou = floor_thr + (i + 1) * iou_interval tmp_set = set( np.where( np.logical_and(max_overlaps >= start_iou, max_overlaps < end_iou))[0]) tmp_inds = list(tmp_set & full_set) if len(tmp_inds) > per_num_expected: tmp_sampled_set = np.random.choice( tmp_inds, size=per_num_expected, replace=False) else: tmp_sampled_set = np.array(tmp_inds, dtype=np.int) sampled_inds.append(tmp_sampled_set) sampled_inds = np.concatenate(sampled_inds) if len(sampled_inds) < num_expected: num_extra = num_expected - len(sampled_inds) extra_inds = np.array(list(full_set - set(sampled_inds))) assert len(sampled_inds)+len(extra_inds) == len(full_set), \ "sum of sampled_inds({}) and extra_inds({}) length must be equal with full_set({})!".format( len(sampled_inds), len(extra_inds), len(full_set)) if len(extra_inds) > num_extra: extra_inds = np.random.choice( extra_inds, num_extra, replace=False) sampled_inds = np.concatenate([sampled_inds, extra_inds]) return sampled_inds def _sample_neg(max_overlaps, max_classes, neg_inds, num_expected, floor_thr=-1, floor_fraction=0, num_bins=3, bg_thresh_hi=0.5): if len(neg_inds) <= num_expected: return neg_inds else: # balance sampling for negative samples neg_set = set(neg_inds) if floor_thr > 0: floor_set = set( np.where( np.logical_and(max_overlaps >= 0, max_overlaps < floor_thr))[0]) iou_sampling_set = set( np.where(max_overlaps >= floor_thr)[0]) elif floor_thr == 0: floor_set = set(np.where(max_overlaps == 0)[0]) iou_sampling_set = set( np.where(max_overlaps > floor_thr)[0]) else: floor_set = set() iou_sampling_set = set( np.where(max_overlaps > floor_thr)[0]) floor_thr = 0 floor_neg_inds = list(floor_set & neg_set) iou_sampling_neg_inds = list(iou_sampling_set & neg_set) num_expected_iou_sampling = int(num_expected * (1 - floor_fraction)) if len(iou_sampling_neg_inds) > num_expected_iou_sampling: if num_bins >= 2: iou_sampled_inds = sample_via_interval( max_overlaps, set(iou_sampling_neg_inds), num_expected_iou_sampling, floor_thr, num_bins, bg_thresh_hi) else: iou_sampled_inds = np.random.choice( iou_sampling_neg_inds, size=num_expected_iou_sampling, replace=False) else: iou_sampled_inds = np.array( iou_sampling_neg_inds, dtype=np.int) num_expected_floor = num_expected - len(iou_sampled_inds) if len(floor_neg_inds) > num_expected_floor: sampled_floor_inds = np.random.choice( floor_neg_inds, size=num_expected_floor, replace=False) else: sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int) sampled_inds = np.concatenate( (sampled_floor_inds, iou_sampled_inds)) if len(sampled_inds) < num_expected: num_extra = num_expected - len(sampled_inds) extra_inds = np.array(list(neg_set - set(sampled_inds))) if len(extra_inds) > num_extra: extra_inds = np.random.choice( extra_inds, size=num_extra, replace=False) sampled_inds = np.concatenate((sampled_inds, extra_inds)) return sampled_inds def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums, use_random, is_cls_agnostic, is_cascade_rcnn): rois_per_image = int(batch_size_per_im) fg_rois_per_im = int(np.round(fg_fraction * rois_per_image)) # Roidb im_scale = im_info[2] inv_im_scale = 1. / im_scale rpn_rois = rpn_rois * inv_im_scale if is_cascade_rcnn: rpn_rois = rpn_rois[gt_boxes.shape[0]:, :] boxes = np.vstack([gt_boxes, rpn_rois]) gt_overlaps = np.zeros((boxes.shape[0], class_nums)) box_to_gt_ind_map = np.zeros((boxes.shape[0]), dtype=np.int32) if len(gt_boxes) > 0: proposal_to_gt_overlaps = bbox_overlaps(boxes, gt_boxes) overlaps_argmax = proposal_to_gt_overlaps.argmax(axis=1) overlaps_max = proposal_to_gt_overlaps.max(axis=1) # Boxes which with non-zero overlap with gt boxes overlapped_boxes_ind = np.where(overlaps_max > 0)[0] overlapped_boxes_gt_classes = gt_classes[overlaps_argmax[ overlapped_boxes_ind]] for idx in range(len(overlapped_boxes_ind)): gt_overlaps[overlapped_boxes_ind[ idx], overlapped_boxes_gt_classes[idx]] = overlaps_max[ overlapped_boxes_ind[idx]] box_to_gt_ind_map[overlapped_boxes_ind[ idx]] = overlaps_argmax[overlapped_boxes_ind[idx]] crowd_ind = np.where(is_crowd)[0] gt_overlaps[crowd_ind] = -1 max_overlaps = gt_overlaps.max(axis=1) max_classes = gt_overlaps.argmax(axis=1) # Cascade RCNN Decode Filter if is_cascade_rcnn: ws = boxes[:, 2] - boxes[:, 0] + 1 hs = boxes[:, 3] - boxes[:, 1] + 1 keep = np.where((ws > 0) & (hs > 0))[0] boxes = boxes[keep] max_overlaps = max_overlaps[keep] fg_inds = np.where(max_overlaps >= fg_thresh)[0] bg_inds = np.where((max_overlaps < bg_thresh_hi) & ( max_overlaps >= bg_thresh_lo))[0] fg_rois_per_this_image = fg_inds.shape[0] bg_rois_per_this_image = bg_inds.shape[0] else: # Foreground fg_inds = np.where(max_overlaps >= fg_thresh)[0] fg_rois_per_this_image = np.minimum(fg_rois_per_im, fg_inds.shape[0]) # Sample foreground if there are too many if fg_inds.shape[0] > fg_rois_per_this_image: if use_random: fg_inds = _sample_pos(max_overlaps, max_classes, fg_inds, fg_rois_per_this_image) fg_inds = fg_inds[:fg_rois_per_this_image] # Background bg_inds = np.where((max_overlaps < bg_thresh_hi) & ( max_overlaps >= bg_thresh_lo))[0] bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image bg_rois_per_this_image = np.minimum(bg_rois_per_this_image, bg_inds.shape[0]) assert bg_rois_per_this_image >= 0, "bg_rois_per_this_image must be >= 0 but got {}".format( bg_rois_per_this_image) # Sample background if there are too many if bg_inds.shape[0] > bg_rois_per_this_image: if use_random: # libra neg sample bg_inds = _sample_neg( max_overlaps, max_classes, bg_inds, bg_rois_per_this_image, num_bins=num_bins, bg_thresh_hi=bg_thresh_hi) bg_inds = bg_inds[:bg_rois_per_this_image] keep_inds = np.append(fg_inds, bg_inds) sampled_labels = max_classes[keep_inds] # N x 1 sampled_labels[fg_rois_per_this_image:] = 0 sampled_boxes = boxes[keep_inds] # N x 324 sampled_gts = gt_boxes[box_to_gt_ind_map[keep_inds]] sampled_gts[fg_rois_per_this_image:, :] = gt_boxes[0] bbox_label_targets = _compute_targets( sampled_boxes, sampled_gts, sampled_labels, bbox_reg_weights) bbox_targets, bbox_inside_weights = _expand_bbox_targets( bbox_label_targets, class_nums, is_cls_agnostic) bbox_outside_weights = np.array( bbox_inside_weights > 0, dtype=bbox_inside_weights.dtype) # Scale rois sampled_rois = sampled_boxes * im_scale # Faster RCNN blobs frcn_blobs = dict( rois=sampled_rois, labels_int32=sampled_labels, bbox_targets=bbox_targets, bbox_inside_weights=bbox_inside_weights, bbox_outside_weights=bbox_outside_weights) return frcn_blobs def _compute_targets(roi_boxes, gt_boxes, labels, bbox_reg_weights): assert roi_boxes.shape[0] == gt_boxes.shape[0] assert roi_boxes.shape[1] == 4 assert gt_boxes.shape[1] == 4 targets = np.zeros(roi_boxes.shape) bbox_reg_weights = np.asarray(bbox_reg_weights) targets = box_to_delta( ex_boxes=roi_boxes, gt_boxes=gt_boxes, weights=bbox_reg_weights) return np.hstack([labels[:, np.newaxis], targets]).astype( np.float32, copy=False) def _expand_bbox_targets(bbox_targets_input, class_nums, is_cls_agnostic): class_labels = bbox_targets_input[:, 0] fg_inds = np.where(class_labels > 0)[0] bbox_targets = np.zeros((class_labels.shape[0], 4 * class_nums if not is_cls_agnostic else 4 * 2)) bbox_inside_weights = np.zeros(bbox_targets.shape) for ind in fg_inds: class_label = int(class_labels[ ind]) if not is_cls_agnostic else 1 start_ind = class_label * 4 end_ind = class_label * 4 + 4 bbox_targets[ind, start_ind:end_ind] = bbox_targets_input[ind, 1:] bbox_inside_weights[ind, start_ind:end_ind] = (1.0, 1.0, 1.0, 1.0) return bbox_targets, bbox_inside_weights def generate_func( rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, ): rpn_rois_lod = rpn_rois.lod()[0] gt_classes_lod = gt_classes.lod()[0] # convert rpn_rois = np.array(rpn_rois) gt_classes = np.array(gt_classes) is_crowd = np.array(is_crowd) gt_boxes = np.array(gt_boxes) im_info = np.array(im_info) rois = [] labels_int32 = [] bbox_targets = [] bbox_inside_weights = [] bbox_outside_weights = [] lod = [0] for idx in range(len(rpn_rois_lod) - 1): rois_si = rpn_rois_lod[idx] rois_ei = rpn_rois_lod[idx + 1] gt_si = gt_classes_lod[idx] gt_ei = gt_classes_lod[idx + 1] frcn_blobs = _sample_rois( rpn_rois[rois_si:rois_ei], gt_classes[gt_si:gt_ei], is_crowd[gt_si:gt_ei], gt_boxes[gt_si:gt_ei], im_info[idx], batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums, use_random, is_cls_agnostic, is_cascade_rcnn) lod.append(frcn_blobs['rois'].shape[0] + lod[-1]) rois.append(frcn_blobs['rois']) labels_int32.append(frcn_blobs['labels_int32'].reshape(-1, 1)) bbox_targets.append(frcn_blobs['bbox_targets']) bbox_inside_weights.append(frcn_blobs['bbox_inside_weights']) bbox_outside_weights.append(frcn_blobs['bbox_outside_weights']) rois = np.vstack(rois) labels_int32 = np.vstack(labels_int32) bbox_targets = np.vstack(bbox_targets) bbox_inside_weights = np.vstack(bbox_inside_weights) bbox_outside_weights = np.vstack(bbox_outside_weights) # create lod-tensor for return # notice that the func create_lod_tensor does not work well here ret_rois = fluid.LoDTensor() ret_rois.set_lod([lod]) ret_rois.set(rois.astype("float32"), fluid.CPUPlace()) ret_labels_int32 = fluid.LoDTensor() ret_labels_int32.set_lod([lod]) ret_labels_int32.set(labels_int32.astype("int32"), fluid.CPUPlace()) ret_bbox_targets = fluid.LoDTensor() ret_bbox_targets.set_lod([lod]) ret_bbox_targets.set( bbox_targets.astype("float32"), fluid.CPUPlace()) ret_bbox_inside_weights = fluid.LoDTensor() ret_bbox_inside_weights.set_lod([lod]) ret_bbox_inside_weights.set( bbox_inside_weights.astype("float32"), fluid.CPUPlace()) ret_bbox_outside_weights = fluid.LoDTensor() ret_bbox_outside_weights.set_lod([lod]) ret_bbox_outside_weights.set( bbox_outside_weights.astype("float32"), fluid.CPUPlace()) return ret_rois, ret_labels_int32, ret_bbox_targets, ret_bbox_inside_weights, ret_bbox_outside_weights rois = create_tmp_var( fluid.default_main_program(), name=None, #'rois', dtype='float32', shape=[-1, 4], ) bbox_inside_weights = create_tmp_var( fluid.default_main_program(), name=None, #'bbox_inside_weights', dtype='float32', shape=[-1, 8 if self.is_cls_agnostic else self.class_nums * 4], ) bbox_outside_weights = create_tmp_var( fluid.default_main_program(), name=None, #'bbox_outside_weights', dtype='float32', shape=[-1, 8 if self.is_cls_agnostic else self.class_nums * 4], ) bbox_targets = create_tmp_var( fluid.default_main_program(), name=None, #'bbox_targets', dtype='float32', shape=[-1, 8 if self.is_cls_agnostic else self.class_nums * 4], ) labels_int32 = create_tmp_var( fluid.default_main_program(), name=None, #'labels_int32', dtype='int32', shape=[-1, 1], ) outs = [ rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights ] fluid.layers.py_func( func=generate_func, x=[rpn_rois, gt_classes, is_crowd, gt_boxes, im_info], out=outs) return outs @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, base_size=300, 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.]], steps=None, offset=0.5, flip=True, min_max_aspect_ratios_order=False, kernel_size=1, pad=0): super(MultiBoxHead, self).__init__() self.min_ratio = min_ratio self.max_ratio = max_ratio self.base_size = base_size self.min_sizes = min_sizes self.max_sizes = max_sizes self.aspect_ratios = aspect_ratios self.steps = steps self.offset = offset self.flip = flip self.min_max_aspect_ratios_order = min_max_aspect_ratios_order self.kernel_size = kernel_size self.pad = pad @register @serializable class SSDLiteMultiBoxHead(object): def __init__(self, min_ratio=20, max_ratio=90, base_size=300, min_sizes=None, max_sizes=None, aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]], steps=None, offset=0.5, flip=True, clip=False, pad=0, conv_decay=0.0): super(SSDLiteMultiBoxHead, self).__init__() self.min_ratio = min_ratio self.max_ratio = max_ratio self.base_size = base_size self.min_sizes = min_sizes self.max_sizes = max_sizes self.aspect_ratios = aspect_ratios self.steps = steps self.offset = offset self.flip = flip self.pad = pad self.clip = clip self.conv_decay = conv_decay def _separable_conv(self, input, num_filters, name): dwconv_param_attr = ParamAttr( name=name + 'dw_weights', regularizer=L2Decay(self.conv_decay)) num_filter1 = input.shape[1] depthwise_conv = fluid.layers.conv2d( input=input, num_filters=num_filter1, filter_size=3, stride=1, padding="SAME", groups=int(num_filter1), act=None, use_cudnn=False, param_attr=dwconv_param_attr, bias_attr=False) bn_name = name + '_bn' bn_param_attr = ParamAttr( name=bn_name + "_scale", regularizer=L2Decay(0.0)) bn_bias_attr = ParamAttr( name=bn_name + "_offset", regularizer=L2Decay(0.0)) bn = fluid.layers.batch_norm( input=depthwise_conv, param_attr=bn_param_attr, bias_attr=bn_bias_attr, moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance') bn = fluid.layers.relu6(bn) pwconv_param_attr = ParamAttr( name=name + 'pw_weights', regularizer=L2Decay(self.conv_decay)) pointwise_conv = fluid.layers.conv2d( input=bn, num_filters=num_filters, filter_size=1, stride=1, act=None, use_cudnn=True, param_attr=pwconv_param_attr, bias_attr=False) return pointwise_conv def __call__(self, inputs, image, num_classes): def _permute_and_reshape(input, last_dim): trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1]) compile_shape = [0, -1, last_dim] return fluid.layers.reshape(trans, shape=compile_shape) def _is_list_or_tuple_(data): return (isinstance(data, list) or isinstance(data, tuple)) if self.min_sizes is None and self.max_sizes is None: num_layer = len(inputs) self.min_sizes = [] self.max_sizes = [] step = int( math.floor(((self.max_ratio - self.min_ratio)) / (num_layer - 2 ))) for ratio in six.moves.range(self.min_ratio, self.max_ratio + 1, step): self.min_sizes.append(self.base_size * ratio / 100.) self.max_sizes.append(self.base_size * (ratio + step) / 100.) self.min_sizes = [self.base_size * .10] + self.min_sizes self.max_sizes = [self.base_size * .20] + self.max_sizes locs, confs = [], [] boxes, mvars = [], [] for i, input in enumerate(inputs): min_size = self.min_sizes[i] max_size = self.max_sizes[i] if not _is_list_or_tuple_(min_size): min_size = [min_size] if not _is_list_or_tuple_(max_size): max_size = [max_size] step = [ self.steps[i] if self.steps else 0.0, self.steps[i] if self.steps else 0.0 ] box, var = fluid.layers.prior_box( input, image, min_sizes=min_size, max_sizes=max_size, steps=step, aspect_ratios=self.aspect_ratios[i], variance=[0.1, 0.1, 0.2, 0.2], clip=self.clip, flip=self.flip, offset=0.5) num_boxes = box.shape[2] box = fluid.layers.reshape(box, shape=[-1, 4]) var = fluid.layers.reshape(var, shape=[-1, 4]) num_loc_output = num_boxes * 4 num_conf_output = num_boxes * num_classes # get loc mbox_loc = self._separable_conv(input, num_loc_output, "loc_{}".format(i + 1)) loc = _permute_and_reshape(mbox_loc, 4) # get conf mbox_conf = self._separable_conv(input, num_conf_output, "conf_{}".format(i + 1)) conf = _permute_and_reshape(mbox_conf, num_classes) locs.append(loc) confs.append(conf) boxes.append(box) mvars.append(var) ssd_mbox_loc = fluid.layers.concat(locs, axis=1) ssd_mbox_conf = fluid.layers.concat(confs, axis=1) prior_boxes = fluid.layers.concat(boxes) box_vars = fluid.layers.concat(mvars) prior_boxes.stop_gradient = True box_vars.stop_gradient = True return ssd_mbox_loc, ssd_mbox_conf, prior_boxes, box_vars @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 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