Datasets:

shalib

/

test

like 0

import cv2 import sys import numpy as np import datetime import os import glob from retinaface import RetinaFace thresh = 0.8 scales = [1024, 1980] count = 1 gpuid = 0 detector = RetinaFace('./model/R50', 0, gpuid, 'net3') img = cv2.imread('t1.jpg') print(img.shape) im_shape = img.shape target_size = scales[0] max_size = scales[1] im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) #im_scale = 1.0 #if im_size_min>target_size or im_size_max>max_size: im_scale = float(target_size) / float(im_size_min) # prevent bigger axis from being more than max_size: if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) print('im_scale', im_scale) scales = [im_scale] flip = False for c in range(count): faces, landmarks = detector.detect(img, thresh, scales=scales, do_flip=flip) print(c, faces.shape, landmarks.shape) if faces is not None: print('find', faces.shape[0], 'faces') for i in range(faces.shape[0]): #print('score', faces[i][4]) box = faces[i].astype(np.int) #color = (255,0,0) color = (0, 0, 255) cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2) if landmarks is not None: landmark5 = landmarks[i].astype(np.int) #print(landmark.shape) for l in range(landmark5.shape[0]): color = (0, 0, 255) if l == 0 or l == 3: color = (0, 255, 0) cv2.circle(img, (landmark5[l][0], landmark5[l][1]), 1, color, 2) filename = './detector_test.jpg' print('writing', filename) cv2.imwrite(filename, img)

insightface/detection/retinaface/test.py/0

model = dict( type='FasterRCNN', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), roi_head=dict( type='StandardRoIHead', bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=80, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)))) # model training and testing settings train_cfg = dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=-1, pos_weight=-1, debug=False), rpn_proposal=dict( nms_across_levels=False, nms_pre=2000, nms_post=1000, max_num=1000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False)) test_cfg = dict( rpn=dict( nms_across_levels=False, nms_pre=1000, nms_post=1000, max_num=1000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100) # soft-nms is also supported for rcnn testing # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) )

insightface/detection/scrfd/configs/_base_/models/faster_rcnn_r50_fpn.py/0

from .inference import (async_inference_detector, inference_detector, init_detector, show_result_pyplot) from .test import multi_gpu_test, single_gpu_test from .train import get_root_logger, set_random_seed, train_detector __all__ = [ 'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector', 'async_inference_detector', 'inference_detector', 'show_result_pyplot', 'multi_gpu_test', 'single_gpu_test' ]

insightface/detection/scrfd/mmdet/apis/__init__.py/0

import torch from ..builder import BBOX_ASSIGNERS from ..iou_calculators import build_iou_calculator from .assign_result import AssignResult from .base_assigner import BaseAssigner def scale_boxes(bboxes, scale): """Expand an array of boxes by a given scale. Args: bboxes (Tensor): Shape (m, 4) scale (float): The scale factor of bboxes Returns: (Tensor): Shape (m, 4). Scaled bboxes """ assert bboxes.size(1) == 4 w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5 h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5 x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5 y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5 w_half *= scale h_half *= scale boxes_scaled = torch.zeros_like(bboxes) boxes_scaled[:, 0] = x_c - w_half boxes_scaled[:, 2] = x_c + w_half boxes_scaled[:, 1] = y_c - h_half boxes_scaled[:, 3] = y_c + h_half return boxes_scaled def is_located_in(points, bboxes): """Are points located in bboxes. Args: points (Tensor): Points, shape: (m, 2). bboxes (Tensor): Bounding boxes, shape: (n, 4). Return: Tensor: Flags indicating if points are located in bboxes, shape: (m, n). """ assert points.size(1) == 2 assert bboxes.size(1) == 4 return (points[:, 0].unsqueeze(1) > bboxes[:, 0].unsqueeze(0)) & \ (points[:, 0].unsqueeze(1) < bboxes[:, 2].unsqueeze(0)) & \ (points[:, 1].unsqueeze(1) > bboxes[:, 1].unsqueeze(0)) & \ (points[:, 1].unsqueeze(1) < bboxes[:, 3].unsqueeze(0)) def bboxes_area(bboxes): """Compute the area of an array of bboxes. Args: bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4) Returns: Tensor: Area of the bboxes. Shape: (m, ) """ assert bboxes.size(1) == 4 w = (bboxes[:, 2] - bboxes[:, 0]) h = (bboxes[:, 3] - bboxes[:, 1]) areas = w * h return areas @BBOX_ASSIGNERS.register_module() class CenterRegionAssigner(BaseAssigner): """Assign pixels at the center region of a bbox as positive. Each proposals will be assigned with `-1`, `0`, or a positive integer indicating the ground truth index. - -1: negative samples - semi-positive numbers: positive sample, index (0-based) of assigned gt Args: pos_scale (float): Threshold within which pixels are labelled as positive. neg_scale (float): Threshold above which pixels are labelled as positive. min_pos_iof (float): Minimum iof of a pixel with a gt to be labelled as positive. Default: 1e-2 ignore_gt_scale (float): Threshold within which the pixels are ignored when the gt is labelled as shadowed. Default: 0.5 foreground_dominate (bool): If True, the bbox will be assigned as positive when a gt's kernel region overlaps with another's shadowed (ignored) region, otherwise it is set as ignored. Default to False. """ def __init__(self, pos_scale, neg_scale, min_pos_iof=1e-2, ignore_gt_scale=0.5, foreground_dominate=False, iou_calculator=dict(type='BboxOverlaps2D')): self.pos_scale = pos_scale self.neg_scale = neg_scale self.min_pos_iof = min_pos_iof self.ignore_gt_scale = ignore_gt_scale self.foreground_dominate = foreground_dominate self.iou_calculator = build_iou_calculator(iou_calculator) def get_gt_priorities(self, gt_bboxes): """Get gt priorities according to their areas. Smaller gt has higher priority. Args: gt_bboxes (Tensor): Ground truth boxes, shape (k, 4). Returns: Tensor: The priority of gts so that gts with larger priority is \ more likely to be assigned. Shape (k, ) """ gt_areas = bboxes_area(gt_bboxes) # Rank all gt bbox areas. Smaller objects has larger priority _, sort_idx = gt_areas.sort(descending=True) sort_idx = sort_idx.argsort() return sort_idx def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): """Assign gt to bboxes. This method assigns gts to every bbox (proposal/anchor), each bbox \ will be assigned with -1, or a semi-positive number. -1 means \ negative sample, semi-positive number is the index (0-based) of \ assigned gt. Args: bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). gt_bboxes_ignore (tensor, optional): Ground truth bboxes that are labelled as `ignored`, e.g., crowd boxes in COCO. gt_labels (tensor, optional): Label of gt_bboxes, shape (num_gts,). Returns: :obj:`AssignResult`: The assigned result. Note that \ shadowed_labels of shape (N, 2) is also added as an \ `assign_result` attribute. `shadowed_labels` is a tensor \ composed of N pairs of anchor_ind, class_label], where N \ is the number of anchors that lie in the outer region of a \ gt, anchor_ind is the shadowed anchor index and class_label \ is the shadowed class label. Example: >>> self = CenterRegionAssigner(0.2, 0.2) >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) >>> gt_bboxes = torch.Tensor([[0, 0, 10, 10]]) >>> assign_result = self.assign(bboxes, gt_bboxes) >>> expected_gt_inds = torch.LongTensor([1, 0]) >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) """ # There are in total 5 steps in the pixel assignment # 1. Find core (the center region, say inner 0.2) # and shadow (the relatively ourter part, say inner 0.2-0.5) # regions of every gt. # 2. Find all prior bboxes that lie in gt_core and gt_shadow regions # 3. Assign prior bboxes in gt_core with a one-hot id of the gt in # the image. # 3.1. For overlapping objects, the prior bboxes in gt_core is # assigned with the object with smallest area # 4. Assign prior bboxes with class label according to its gt id. # 4.1. Assign -1 to prior bboxes lying in shadowed gts # 4.2. Assign positive prior boxes with the corresponding label # 5. Find pixels lying in the shadow of an object and assign them with # background label, but set the loss weight of its corresponding # gt to zero. assert bboxes.size(1) == 4, 'bboxes must have size of 4' # 1. Find core positive and shadow region of every gt gt_core = scale_boxes(gt_bboxes, self.pos_scale) gt_shadow = scale_boxes(gt_bboxes, self.neg_scale) # 2. Find prior bboxes that lie in gt_core and gt_shadow regions bbox_centers = (bboxes[:, 2:4] + bboxes[:, 0:2]) / 2 # The center points lie within the gt boxes is_bbox_in_gt = is_located_in(bbox_centers, gt_bboxes) # Only calculate bbox and gt_core IoF. This enables small prior bboxes # to match large gts bbox_and_gt_core_overlaps = self.iou_calculator( bboxes, gt_core, mode='iof') # The center point of effective priors should be within the gt box is_bbox_in_gt_core = is_bbox_in_gt & ( bbox_and_gt_core_overlaps > self.min_pos_iof) # shape (n, k) is_bbox_in_gt_shadow = ( self.iou_calculator(bboxes, gt_shadow, mode='iof') > self.min_pos_iof) # Rule out center effective positive pixels is_bbox_in_gt_shadow &= (~is_bbox_in_gt_core) num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0) if num_gts == 0 or num_bboxes == 0: # If no gts exist, assign all pixels to negative assigned_gt_ids = \ is_bbox_in_gt_core.new_zeros((num_bboxes,), dtype=torch.long) pixels_in_gt_shadow = assigned_gt_ids.new_empty((0, 2)) else: # Step 3: assign a one-hot gt id to each pixel, and smaller objects # have high priority to assign the pixel. sort_idx = self.get_gt_priorities(gt_bboxes) assigned_gt_ids, pixels_in_gt_shadow = \ self.assign_one_hot_gt_indices(is_bbox_in_gt_core, is_bbox_in_gt_shadow, gt_priority=sort_idx) if gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0: # No ground truth or boxes, return empty assignment gt_bboxes_ignore = scale_boxes( gt_bboxes_ignore, scale=self.ignore_gt_scale) is_bbox_in_ignored_gts = is_located_in(bbox_centers, gt_bboxes_ignore) is_bbox_in_ignored_gts = is_bbox_in_ignored_gts.any(dim=1) assigned_gt_ids[is_bbox_in_ignored_gts] = -1 # 4. Assign prior bboxes with class label according to its gt id. assigned_labels = None shadowed_pixel_labels = None if gt_labels is not None: # Default assigned label is the background (-1) assigned_labels = assigned_gt_ids.new_full((num_bboxes, ), -1) pos_inds = torch.nonzero( assigned_gt_ids > 0, as_tuple=False).squeeze() if pos_inds.numel() > 0: assigned_labels[pos_inds] = gt_labels[assigned_gt_ids[pos_inds] - 1] # 5. Find pixels lying in the shadow of an object shadowed_pixel_labels = pixels_in_gt_shadow.clone() if pixels_in_gt_shadow.numel() > 0: pixel_idx, gt_idx =\ pixels_in_gt_shadow[:, 0], pixels_in_gt_shadow[:, 1] assert (assigned_gt_ids[pixel_idx] != gt_idx).all(), \ 'Some pixels are dually assigned to ignore and gt!' shadowed_pixel_labels[:, 1] = gt_labels[gt_idx - 1] override = ( assigned_labels[pixel_idx] == shadowed_pixel_labels[:, 1]) if self.foreground_dominate: # When a pixel is both positive and shadowed, set it as pos shadowed_pixel_labels = shadowed_pixel_labels[~override] else: # When a pixel is both pos and shadowed, set it as shadowed assigned_labels[pixel_idx[override]] = -1 assigned_gt_ids[pixel_idx[override]] = 0 assign_result = AssignResult( num_gts, assigned_gt_ids, None, labels=assigned_labels) # Add shadowed_labels as assign_result property. Shape: (num_shadow, 2) assign_result.set_extra_property('shadowed_labels', shadowed_pixel_labels) return assign_result def assign_one_hot_gt_indices(self, is_bbox_in_gt_core, is_bbox_in_gt_shadow, gt_priority=None): """Assign only one gt index to each prior box. Gts with large gt_priority are more likely to be assigned. Args: is_bbox_in_gt_core (Tensor): Bool tensor indicating the bbox center is in the core area of a gt (e.g. 0-0.2). Shape: (num_prior, num_gt). is_bbox_in_gt_shadow (Tensor): Bool tensor indicating the bbox center is in the shadowed area of a gt (e.g. 0.2-0.5). Shape: (num_prior, num_gt). gt_priority (Tensor): Priorities of gts. The gt with a higher priority is more likely to be assigned to the bbox when the bbox match with multiple gts. Shape: (num_gt, ). Returns: tuple: Returns (assigned_gt_inds, shadowed_gt_inds). - assigned_gt_inds: The assigned gt index of each prior bbox \ (i.e. index from 1 to num_gts). Shape: (num_prior, ). - shadowed_gt_inds: shadowed gt indices. It is a tensor of \ shape (num_ignore, 2) with first column being the \ shadowed prior bbox indices and the second column the \ shadowed gt indices (1-based). """ num_bboxes, num_gts = is_bbox_in_gt_core.shape if gt_priority is None: gt_priority = torch.arange( num_gts, device=is_bbox_in_gt_core.device) assert gt_priority.size(0) == num_gts # The bigger gt_priority, the more preferable to be assigned # The assigned inds are by default 0 (background) assigned_gt_inds = is_bbox_in_gt_core.new_zeros((num_bboxes, ), dtype=torch.long) # Shadowed bboxes are assigned to be background. But the corresponding # label is ignored during loss calculation, which is done through # shadowed_gt_inds shadowed_gt_inds = torch.nonzero(is_bbox_in_gt_shadow, as_tuple=False) if is_bbox_in_gt_core.sum() == 0: # No gt match shadowed_gt_inds[:, 1] += 1 # 1-based. For consistency issue return assigned_gt_inds, shadowed_gt_inds # The priority of each prior box and gt pair. If one prior box is # matched bo multiple gts. Only the pair with the highest priority # is saved pair_priority = is_bbox_in_gt_core.new_full((num_bboxes, num_gts), -1, dtype=torch.long) # Each bbox could match with multiple gts. # The following codes deal with this situation # Matched bboxes (to any gt). Shape: (num_pos_anchor, ) inds_of_match = torch.any(is_bbox_in_gt_core, dim=1) # The matched gt index of each positive bbox. Length >= num_pos_anchor # , since one bbox could match multiple gts matched_bbox_gt_inds = torch.nonzero( is_bbox_in_gt_core, as_tuple=False)[:, 1] # Assign priority to each bbox-gt pair. pair_priority[is_bbox_in_gt_core] = gt_priority[matched_bbox_gt_inds] _, argmax_priority = pair_priority[inds_of_match].max(dim=1) assigned_gt_inds[inds_of_match] = argmax_priority + 1 # 1-based # Zero-out the assigned anchor box to filter the shadowed gt indices is_bbox_in_gt_core[inds_of_match, argmax_priority] = 0 # Concat the shadowed indices due to overlapping with that out side of # effective scale. shape: (total_num_ignore, 2) shadowed_gt_inds = torch.cat( (shadowed_gt_inds, torch.nonzero( is_bbox_in_gt_core, as_tuple=False)), dim=0) # `is_bbox_in_gt_core` should be changed back to keep arguments intact. is_bbox_in_gt_core[inds_of_match, argmax_priority] = 1 # 1-based shadowed gt indices, to be consistent with `assigned_gt_inds` if shadowed_gt_inds.numel() > 0: shadowed_gt_inds[:, 1] += 1 return assigned_gt_inds, shadowed_gt_inds

insightface/detection/scrfd/mmdet/core/bbox/assigners/center_region_assigner.py/0

from mmcv.utils import Registry, build_from_cfg IOU_CALCULATORS = Registry('IoU calculator') def build_iou_calculator(cfg, default_args=None): """Builder of IoU calculator.""" return build_from_cfg(cfg, IOU_CALCULATORS, default_args)

insightface/detection/scrfd/mmdet/core/bbox/iou_calculators/builder.py/0

import os.path as osp import warnings from math import inf import mmcv from mmcv.runner import Hook from torch.utils.data import DataLoader from mmdet.utils import get_root_logger class EvalHook(Hook): """Evaluation hook. Notes: If new arguments are added for EvalHook, tools/test.py, tools/eval_metric.py may be effected. Attributes: dataloader (DataLoader): A PyTorch dataloader. start (int, optional): Evaluation starting epoch. It enables evaluation before the training starts if ``start`` <= the resuming epoch. If None, whether to evaluate is merely decided by ``interval``. Default: None. interval (int): Evaluation interval (by epochs). Default: 1. save_best (str, optional): If a metric is specified, it would measure the best checkpoint during evaluation. The information about best checkpoint would be save in best.json. Options are the evaluation metrics to the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance segmentation. ``AR@100`` for proposal recall. If ``save_best`` is ``auto``, the first key will be used. The interval of ``CheckpointHook`` should device EvalHook. Default: None. rule (str, optional): Comparison rule for best score. If set to None, it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will be inferred by 'greater' rule. Keys contain 'loss' will be inferred by 'less' rule. Options are 'greater', 'less'. Default: None. **eval_kwargs: Evaluation arguments fed into the evaluate function of the dataset. """ rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y} init_value_map = {'greater': -inf, 'less': inf} greater_keys = ['mAP', 'AR'] less_keys = ['loss'] def __init__(self, dataloader, start=None, interval=1, save_best=None, rule=None, **eval_kwargs): if not isinstance(dataloader, DataLoader): raise TypeError('dataloader must be a pytorch DataLoader, but got' f' {type(dataloader)}') if not interval > 0: raise ValueError(f'interval must be positive, but got {interval}') if start is not None and start < 0: warnings.warn( f'The evaluation start epoch {start} is smaller than 0, ' f'use 0 instead', UserWarning) start = 0 self.dataloader = dataloader self.interval = interval self.start = start assert isinstance(save_best, str) or save_best is None self.save_best = save_best self.eval_kwargs = eval_kwargs self.initial_epoch_flag = True self.logger = get_root_logger() if self.save_best is not None: self._init_rule(rule, self.save_best) def _init_rule(self, rule, key_indicator): """Initialize rule, key_indicator, comparison_func, and best score. Args: rule (str | None): Comparison rule for best score. key_indicator (str | None): Key indicator to determine the comparison rule. """ if rule not in self.rule_map and rule is not None: raise KeyError(f'rule must be greater, less or None, ' f'but got {rule}.') if rule is None: if key_indicator != 'auto': if any(key in key_indicator for key in self.greater_keys): rule = 'greater' elif any(key in key_indicator for key in self.less_keys): rule = 'less' else: raise ValueError(f'Cannot infer the rule for key ' f'{key_indicator}, thus a specific rule ' f'must be specified.') self.rule = rule self.key_indicator = key_indicator if self.rule is not None: self.compare_func = self.rule_map[self.rule] def before_run(self, runner): if self.save_best is not None: if runner.meta is None: warnings.warn('runner.meta is None. Creating a empty one.') runner.meta = dict() runner.meta.setdefault('hook_msgs', dict()) def before_train_epoch(self, runner): """Evaluate the model only at the start of training.""" if not self.initial_epoch_flag: return if self.start is not None and runner.epoch >= self.start: self.after_train_epoch(runner) self.initial_epoch_flag = False def evaluation_flag(self, runner): """Judge whether to perform_evaluation after this epoch. Returns: bool: The flag indicating whether to perform evaluation. """ if self.start is None: if not self.every_n_epochs(runner, self.interval): # No evaluation during the interval epochs. return False elif (runner.epoch + 1) < self.start: # No evaluation if start is larger than the current epoch. return False else: # Evaluation only at epochs 3, 5, 7... if start==3 and interval==2 if (runner.epoch + 1 - self.start) % self.interval: return False return True def after_train_epoch(self, runner): if not self.evaluation_flag(runner): return from mmdet.apis import single_gpu_test results = single_gpu_test(runner.model, self.dataloader, show=False) key_score = self.evaluate(runner, results) if self.save_best: best_score = runner.meta['hook_msgs'].get( 'best_score', self.init_value_map[self.rule]) if self.compare_func(key_score, best_score): best_score = key_score runner.meta['hook_msgs']['best_score'] = best_score last_ckpt = runner.meta['hook_msgs']['last_ckpt'] runner.meta['hook_msgs']['best_ckpt'] = last_ckpt mmcv.symlink( last_ckpt, osp.join(runner.work_dir, f'best_{self.key_indicator}.pth')) self.logger.info( f'Now best checkpoint is epoch_{runner.epoch + 1}.pth.' f'Best {self.key_indicator} is {best_score:0.4f}') def evaluate(self, runner, results): eval_res = self.dataloader.dataset.evaluate( results, logger=runner.logger, **self.eval_kwargs) for name, val in eval_res.items(): runner.log_buffer.output[name] = val runner.log_buffer.ready = True if self.save_best is not None: if self.key_indicator == 'auto': # infer from eval_results self._init_rule(self.rule, list(eval_res.keys())[0]) return eval_res[self.key_indicator] else: return None class DistEvalHook(EvalHook): """Distributed evaluation hook. Notes: If new arguments are added, tools/test.py may be effected. Attributes: dataloader (DataLoader): A PyTorch dataloader. start (int, optional): Evaluation starting epoch. It enables evaluation before the training starts if ``start`` <= the resuming epoch. If None, whether to evaluate is merely decided by ``interval``. Default: None. interval (int): Evaluation interval (by epochs). Default: 1. tmpdir (str | None): Temporary directory to save the results of all processes. Default: None. gpu_collect (bool): Whether to use gpu or cpu to collect results. Default: False. save_best (str, optional): If a metric is specified, it would measure the best checkpoint during evaluation. The information about best checkpoint would be save in best.json. Options are the evaluation metrics to the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance segmentation. ``AR@100`` for proposal recall. If ``save_best`` is ``auto``, the first key will be used. The interval of ``CheckpointHook`` should device EvalHook. Default: None. rule (str | None): Comparison rule for best score. If set to None, it will infer a reasonable rule. Default: 'None'. **eval_kwargs: Evaluation arguments fed into the evaluate function of the dataset. """ def __init__(self, dataloader, start=None, interval=1, tmpdir=None, gpu_collect=False, save_best=None, rule=None, **eval_kwargs): super().__init__( dataloader, start=start, interval=interval, save_best=save_best, rule=rule, **eval_kwargs) self.tmpdir = tmpdir self.gpu_collect = gpu_collect def after_train_epoch(self, runner): if not self.evaluation_flag(runner): return from mmdet.apis import multi_gpu_test tmpdir = self.tmpdir if tmpdir is None: tmpdir = osp.join(runner.work_dir, '.eval_hook') results = multi_gpu_test( runner.model, self.dataloader, tmpdir=tmpdir, gpu_collect=self.gpu_collect) if runner.rank == 0: print('\n') key_score = self.evaluate(runner, results) if self.save_best: best_score = runner.meta['hook_msgs'].get( 'best_score', self.init_value_map[self.rule]) if self.compare_func(key_score, best_score): best_score = key_score runner.meta['hook_msgs']['best_score'] = best_score last_ckpt = runner.meta['hook_msgs']['last_ckpt'] runner.meta['hook_msgs']['best_ckpt'] = last_ckpt mmcv.symlink( last_ckpt, osp.join(runner.work_dir, f'best_{self.key_indicator}.pth')) self.logger.info( f'Now best checkpoint is {last_ckpt}.' f'Best {self.key_indicator} is {best_score:0.4f}')

insightface/detection/scrfd/mmdet/core/evaluation/eval_hooks.py/0

import warnings from collections import OrderedDict import torch.distributed as dist from mmcv.runner import OptimizerHook from torch._utils import (_flatten_dense_tensors, _take_tensors, _unflatten_dense_tensors) def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): if bucket_size_mb > 0: bucket_size_bytes = bucket_size_mb * 1024 * 1024 buckets = _take_tensors(tensors, bucket_size_bytes) else: buckets = OrderedDict() for tensor in tensors: tp = tensor.type() if tp not in buckets: buckets[tp] = [] buckets[tp].append(tensor) buckets = buckets.values() for bucket in buckets: flat_tensors = _flatten_dense_tensors(bucket) dist.all_reduce(flat_tensors) flat_tensors.div_(world_size) for tensor, synced in zip( bucket, _unflatten_dense_tensors(flat_tensors, bucket)): tensor.copy_(synced) def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): """Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allreduce parameters as a whole. Defaults to True. bucket_size_mb (int, optional): Size of bucket, the unit is MB. Defaults to -1. """ grads = [ param.grad.data for param in params if param.requires_grad and param.grad is not None ] world_size = dist.get_world_size() if coalesce: _allreduce_coalesced(grads, world_size, bucket_size_mb) else: for tensor in grads: dist.all_reduce(tensor.div_(world_size)) class DistOptimizerHook(OptimizerHook): """Deprecated optimizer hook for distributed training.""" def __init__(self, *args, **kwargs): warnings.warn('"DistOptimizerHook" is deprecated, please switch to' '"mmcv.runner.OptimizerHook".') super().__init__(*args, **kwargs) def reduce_mean(tensor): """"Obtain the mean of tensor on different GPUs.""" if not (dist.is_available() and dist.is_initialized()): return tensor tensor = tensor.clone() dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM) return tensor

insightface/detection/scrfd/mmdet/core/utils/dist_utils.py/0

import warnings import mmcv from ..builder import PIPELINES from .compose import Compose @PIPELINES.register_module() class MultiScaleFlipAug(object): """Test-time augmentation with multiple scales and flipping. An example configuration is as followed: .. code-block:: img_scale=[(1333, 400), (1333, 800)], flip=True, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ] After MultiScaleFLipAug with above configuration, the results are wrapped into lists of the same length as followed: .. code-block:: dict( img=[...], img_shape=[...], scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)] flip=[False, True, False, True] ... ) Args: transforms (list[dict]): Transforms to apply in each augmentation. img_scale (tuple | list[tuple] | None): Images scales for resizing. scale_factor (float | list[float] | None): Scale factors for resizing. flip (bool): Whether apply flip augmentation. Default: False. flip_direction (str | list[str]): Flip augmentation directions, options are "horizontal" and "vertical". If flip_direction is list, multiple flip augmentations will be applied. It has no effect when flip == False. Default: "horizontal". """ def __init__(self, transforms, img_scale=None, scale_factor=None, flip=False, flip_direction='horizontal'): self.transforms = Compose(transforms) assert (img_scale is None) ^ (scale_factor is None), ( 'Must have but only one variable can be setted') if img_scale is not None: self.img_scale = img_scale if isinstance(img_scale, list) else [img_scale] self.scale_key = 'scale' assert mmcv.is_list_of(self.img_scale, tuple) else: self.img_scale = scale_factor if isinstance( scale_factor, list) else [scale_factor] self.scale_key = 'scale_factor' self.flip = flip self.flip_direction = flip_direction if isinstance( flip_direction, list) else [flip_direction] assert mmcv.is_list_of(self.flip_direction, str) if not self.flip and self.flip_direction != ['horizontal']: warnings.warn( 'flip_direction has no effect when flip is set to False') if (self.flip and not any([t['type'] == 'RandomFlip' for t in transforms])): warnings.warn( 'flip has no effect when RandomFlip is not in transforms') def __call__(self, results): """Call function to apply test time augment transforms on results. Args: results (dict): Result dict contains the data to transform. Returns: dict[str: list]: The augmented data, where each value is wrapped into a list. """ aug_data = [] flip_args = [(False, None)] if self.flip: flip_args += [(True, direction) for direction in self.flip_direction] for scale in self.img_scale: for flip, direction in flip_args: _results = results.copy() _results[self.scale_key] = scale _results['flip'] = flip _results['flip_direction'] = direction data = self.transforms(_results) aug_data.append(data) # list of dict to dict of list aug_data_dict = {key: [] for key in aug_data[0]} for data in aug_data: for key, val in data.items(): aug_data_dict[key].append(val) return aug_data_dict def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(transforms={self.transforms}, ' repr_str += f'img_scale={self.img_scale}, flip={self.flip})' repr_str += f'flip_direction={self.flip_direction}' return repr_str

insightface/detection/scrfd/mmdet/datasets/pipelines/test_time_aug.py/0

import torch.nn as nn from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init, kaiming_init) from mmcv.runner import load_checkpoint from torch.nn.modules.batchnorm import _BatchNorm from mmdet.utils import get_root_logger from ..builder import BACKBONES from .resnet import BasicBlock, Bottleneck class HRModule(nn.Module): """High-Resolution Module for HRNet. In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange is in this module. """ def __init__(self, num_branches, blocks, num_blocks, in_channels, num_channels, multiscale_output=True, with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN')): super(HRModule, self).__init__() self._check_branches(num_branches, num_blocks, in_channels, num_channels) self.in_channels = in_channels self.num_branches = num_branches self.multiscale_output = multiscale_output self.norm_cfg = norm_cfg self.conv_cfg = conv_cfg self.with_cp = with_cp self.branches = self._make_branches(num_branches, blocks, num_blocks, num_channels) self.fuse_layers = self._make_fuse_layers() self.relu = nn.ReLU(inplace=False) def _check_branches(self, num_branches, num_blocks, in_channels, num_channels): if num_branches != len(num_blocks): error_msg = f'NUM_BRANCHES({num_branches}) ' \ f'!= NUM_BLOCKS({len(num_blocks)})' raise ValueError(error_msg) if num_branches != len(num_channels): error_msg = f'NUM_BRANCHES({num_branches}) ' \ f'!= NUM_CHANNELS({len(num_channels)})' raise ValueError(error_msg) if num_branches != len(in_channels): error_msg = f'NUM_BRANCHES({num_branches}) ' \ f'!= NUM_INCHANNELS({len(in_channels)})' raise ValueError(error_msg) def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): downsample = None if stride != 1 or \ self.in_channels[branch_index] != \ num_channels[branch_index] * block.expansion: downsample = nn.Sequential( build_conv_layer( self.conv_cfg, self.in_channels[branch_index], num_channels[branch_index] * block.expansion, kernel_size=1, stride=stride, bias=False), build_norm_layer(self.norm_cfg, num_channels[branch_index] * block.expansion)[1]) layers = [] layers.append( block( self.in_channels[branch_index], num_channels[branch_index], stride, downsample=downsample, with_cp=self.with_cp, norm_cfg=self.norm_cfg, conv_cfg=self.conv_cfg)) self.in_channels[branch_index] = \ num_channels[branch_index] * block.expansion for i in range(1, num_blocks[branch_index]): layers.append( block( self.in_channels[branch_index], num_channels[branch_index], with_cp=self.with_cp, norm_cfg=self.norm_cfg, conv_cfg=self.conv_cfg)) return nn.Sequential(*layers) def _make_branches(self, num_branches, block, num_blocks, num_channels): branches = [] for i in range(num_branches): branches.append( self._make_one_branch(i, block, num_blocks, num_channels)) return nn.ModuleList(branches) def _make_fuse_layers(self): if self.num_branches == 1: return None num_branches = self.num_branches in_channels = self.in_channels fuse_layers = [] num_out_branches = num_branches if self.multiscale_output else 1 for i in range(num_out_branches): fuse_layer = [] for j in range(num_branches): if j > i: fuse_layer.append( nn.Sequential( build_conv_layer( self.conv_cfg, in_channels[j], in_channels[i], kernel_size=1, stride=1, padding=0, bias=False), build_norm_layer(self.norm_cfg, in_channels[i])[1], nn.Upsample( scale_factor=2**(j - i), mode='nearest'))) elif j == i: fuse_layer.append(None) else: conv_downsamples = [] for k in range(i - j): if k == i - j - 1: conv_downsamples.append( nn.Sequential( build_conv_layer( self.conv_cfg, in_channels[j], in_channels[i], kernel_size=3, stride=2, padding=1, bias=False), build_norm_layer(self.norm_cfg, in_channels[i])[1])) else: conv_downsamples.append( nn.Sequential( build_conv_layer( self.conv_cfg, in_channels[j], in_channels[j], kernel_size=3, stride=2, padding=1, bias=False), build_norm_layer(self.norm_cfg, in_channels[j])[1], nn.ReLU(inplace=False))) fuse_layer.append(nn.Sequential(*conv_downsamples)) fuse_layers.append(nn.ModuleList(fuse_layer)) return nn.ModuleList(fuse_layers) def forward(self, x): """Forward function.""" if self.num_branches == 1: return [self.branches[0](x[0])] for i in range(self.num_branches): x[i] = self.branches[i](x[i]) x_fuse = [] for i in range(len(self.fuse_layers)): y = 0 for j in range(self.num_branches): if i == j: y += x[j] else: y += self.fuse_layers[i][j](x[j]) x_fuse.append(self.relu(y)) return x_fuse @BACKBONES.register_module() class HRNet(nn.Module): """HRNet backbone. High-Resolution Representations for Labeling Pixels and Regions arXiv: https://arxiv.org/abs/1904.04514 Args: extra (dict): detailed configuration for each stage of HRNet. in_channels (int): Number of input image channels. Default: 3. conv_cfg (dict): dictionary to construct and config conv layer. norm_cfg (dict): dictionary to construct and config norm layer. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. zero_init_residual (bool): whether to use zero init for last norm layer in resblocks to let them behave as identity. Example: >>> from mmdet.models import HRNet >>> import torch >>> extra = dict( >>> stage1=dict( >>> num_modules=1, >>> num_branches=1, >>> block='BOTTLENECK', >>> num_blocks=(4, ), >>> num_channels=(64, )), >>> stage2=dict( >>> num_modules=1, >>> num_branches=2, >>> block='BASIC', >>> num_blocks=(4, 4), >>> num_channels=(32, 64)), >>> stage3=dict( >>> num_modules=4, >>> num_branches=3, >>> block='BASIC', >>> num_blocks=(4, 4, 4), >>> num_channels=(32, 64, 128)), >>> stage4=dict( >>> num_modules=3, >>> num_branches=4, >>> block='BASIC', >>> num_blocks=(4, 4, 4, 4), >>> num_channels=(32, 64, 128, 256))) >>> self = HRNet(extra, in_channels=1) >>> self.eval() >>> inputs = torch.rand(1, 1, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 32, 8, 8) (1, 64, 4, 4) (1, 128, 2, 2) (1, 256, 1, 1) """ blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} def __init__(self, extra, in_channels=3, conv_cfg=None, norm_cfg=dict(type='BN'), norm_eval=True, with_cp=False, zero_init_residual=False): super(HRNet, self).__init__() self.extra = extra self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.norm_eval = norm_eval self.with_cp = with_cp self.zero_init_residual = zero_init_residual # stem net self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1) self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2) self.conv1 = build_conv_layer( self.conv_cfg, in_channels, 64, kernel_size=3, stride=2, padding=1, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( self.conv_cfg, 64, 64, kernel_size=3, stride=2, padding=1, bias=False) self.add_module(self.norm2_name, norm2) self.relu = nn.ReLU(inplace=True) # stage 1 self.stage1_cfg = self.extra['stage1'] num_channels = self.stage1_cfg['num_channels'][0] block_type = self.stage1_cfg['block'] num_blocks = self.stage1_cfg['num_blocks'][0] block = self.blocks_dict[block_type] stage1_out_channels = num_channels * block.expansion self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) # stage 2 self.stage2_cfg = self.extra['stage2'] num_channels = self.stage2_cfg['num_channels'] block_type = self.stage2_cfg['block'] block = self.blocks_dict[block_type] num_channels = [channel * block.expansion for channel in num_channels] self.transition1 = self._make_transition_layer([stage1_out_channels], num_channels) self.stage2, pre_stage_channels = self._make_stage( self.stage2_cfg, num_channels) # stage 3 self.stage3_cfg = self.extra['stage3'] num_channels = self.stage3_cfg['num_channels'] block_type = self.stage3_cfg['block'] block = self.blocks_dict[block_type] num_channels = [channel * block.expansion for channel in num_channels] self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels) self.stage3, pre_stage_channels = self._make_stage( self.stage3_cfg, num_channels) # stage 4 self.stage4_cfg = self.extra['stage4'] num_channels = self.stage4_cfg['num_channels'] block_type = self.stage4_cfg['block'] block = self.blocks_dict[block_type] num_channels = [channel * block.expansion for channel in num_channels] self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels) self.stage4, pre_stage_channels = self._make_stage( self.stage4_cfg, num_channels) @property def norm1(self): """nn.Module: the normalization layer named "norm1" """ return getattr(self, self.norm1_name) @property def norm2(self): """nn.Module: the normalization layer named "norm2" """ return getattr(self, self.norm2_name) def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer): num_branches_cur = len(num_channels_cur_layer) num_branches_pre = len(num_channels_pre_layer) transition_layers = [] for i in range(num_branches_cur): if i < num_branches_pre: if num_channels_cur_layer[i] != num_channels_pre_layer[i]: transition_layers.append( nn.Sequential( build_conv_layer( self.conv_cfg, num_channels_pre_layer[i], num_channels_cur_layer[i], kernel_size=3, stride=1, padding=1, bias=False), build_norm_layer(self.norm_cfg, num_channels_cur_layer[i])[1], nn.ReLU(inplace=True))) else: transition_layers.append(None) else: conv_downsamples = [] for j in range(i + 1 - num_branches_pre): in_channels = num_channels_pre_layer[-1] out_channels = num_channels_cur_layer[i] \ if j == i - num_branches_pre else in_channels conv_downsamples.append( nn.Sequential( build_conv_layer( self.conv_cfg, in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False), build_norm_layer(self.norm_cfg, out_channels)[1], nn.ReLU(inplace=True))) transition_layers.append(nn.Sequential(*conv_downsamples)) return nn.ModuleList(transition_layers) def _make_layer(self, block, inplanes, planes, blocks, stride=1): downsample = None if stride != 1 or inplanes != planes * block.expansion: downsample = nn.Sequential( build_conv_layer( self.conv_cfg, inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), build_norm_layer(self.norm_cfg, planes * block.expansion)[1]) layers = [] layers.append( block( inplanes, planes, stride, downsample=downsample, with_cp=self.with_cp, norm_cfg=self.norm_cfg, conv_cfg=self.conv_cfg)) inplanes = planes * block.expansion for i in range(1, blocks): layers.append( block( inplanes, planes, with_cp=self.with_cp, norm_cfg=self.norm_cfg, conv_cfg=self.conv_cfg)) return nn.Sequential(*layers) def _make_stage(self, layer_config, in_channels, multiscale_output=True): num_modules = layer_config['num_modules'] num_branches = layer_config['num_branches'] num_blocks = layer_config['num_blocks'] num_channels = layer_config['num_channels'] block = self.blocks_dict[layer_config['block']] hr_modules = [] for i in range(num_modules): # multi_scale_output is only used for the last module if not multiscale_output and i == num_modules - 1: reset_multiscale_output = False else: reset_multiscale_output = True hr_modules.append( HRModule( num_branches, block, num_blocks, in_channels, num_channels, reset_multiscale_output, with_cp=self.with_cp, norm_cfg=self.norm_cfg, conv_cfg=self.conv_cfg)) return nn.Sequential(*hr_modules), in_channels def init_weights(self, pretrained=None): """Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to None. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=False, logger=logger) elif pretrained is None: for m in self.modules(): if isinstance(m, nn.Conv2d): kaiming_init(m) elif isinstance(m, (_BatchNorm, nn.GroupNorm)): constant_init(m, 1) if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): constant_init(m.norm3, 0) elif isinstance(m, BasicBlock): constant_init(m.norm2, 0) else: raise TypeError('pretrained must be a str or None') def forward(self, x): """Forward function.""" x = self.conv1(x) x = self.norm1(x) x = self.relu(x) x = self.conv2(x) x = self.norm2(x) x = self.relu(x) x = self.layer1(x) x_list = [] for i in range(self.stage2_cfg['num_branches']): if self.transition1[i] is not None: x_list.append(self.transition1[i](x)) else: x_list.append(x) y_list = self.stage2(x_list) x_list = [] for i in range(self.stage3_cfg['num_branches']): if self.transition2[i] is not None: x_list.append(self.transition2[i](y_list[-1])) else: x_list.append(y_list[i]) y_list = self.stage3(x_list) x_list = [] for i in range(self.stage4_cfg['num_branches']): if self.transition3[i] is not None: x_list.append(self.transition3[i](y_list[-1])) else: x_list.append(y_list[i]) y_list = self.stage4(x_list) return y_list def train(self, mode=True): """Convert the model into training mode whill keeping the normalization layer freezed.""" super(HRNet, self).train(mode) if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval()

insightface/detection/scrfd/mmdet/models/backbones/hrnet.py/0

from math import ceil, log import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import ConvModule, bias_init_with_prob from mmcv.ops import CornerPool, batched_nms from mmdet.core import multi_apply from ..builder import HEADS, build_loss from ..utils import gaussian_radius, gen_gaussian_target from .base_dense_head import BaseDenseHead class BiCornerPool(nn.Module): """Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.) Args: in_channels (int): Input channels of module. out_channels (int): Output channels of module. feat_channels (int): Feature channels of module. directions (list[str]): Directions of two CornerPools. norm_cfg (dict): Dictionary to construct and config norm layer. """ def __init__(self, in_channels, directions, feat_channels=128, out_channels=128, norm_cfg=dict(type='BN', requires_grad=True)): super(BiCornerPool, self).__init__() self.direction1_conv = ConvModule( in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg) self.direction2_conv = ConvModule( in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg) self.aftpool_conv = ConvModule( feat_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg, act_cfg=None) self.conv1 = ConvModule( in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None) self.conv2 = ConvModule( in_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg) self.direction1_pool = CornerPool(directions[0]) self.direction2_pool = CornerPool(directions[1]) self.relu = nn.ReLU(inplace=True) def forward(self, x): """Forward features from the upstream network. Args: x (tensor): Input feature of BiCornerPool. Returns: conv2 (tensor): Output feature of BiCornerPool. """ direction1_conv = self.direction1_conv(x) direction2_conv = self.direction2_conv(x) direction1_feat = self.direction1_pool(direction1_conv) direction2_feat = self.direction2_pool(direction2_conv) aftpool_conv = self.aftpool_conv(direction1_feat + direction2_feat) conv1 = self.conv1(x) relu = self.relu(aftpool_conv + conv1) conv2 = self.conv2(relu) return conv2 @HEADS.register_module() class CornerHead(BaseDenseHead): """Head of CornerNet: Detecting Objects as Paired Keypoints. Code is modified from the `official github repo <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/ kp.py#L73>`_ . More details can be found in the `paper <https://arxiv.org/abs/1808.01244>`_ . Args: num_classes (int): Number of categories excluding the background category. in_channels (int): Number of channels in the input feature map. num_feat_levels (int): Levels of feature from the previous module. 2 for HourglassNet-104 and 1 for HourglassNet-52. Because HourglassNet-104 outputs the final feature and intermediate supervision feature and HourglassNet-52 only outputs the final feature. Default: 2. corner_emb_channels (int): Channel of embedding vector. Default: 1. train_cfg (dict | None): Training config. Useless in CornerHead, but we keep this variable for SingleStageDetector. Default: None. test_cfg (dict | None): Testing config of CornerHead. Default: None. loss_heatmap (dict | None): Config of corner heatmap loss. Default: GaussianFocalLoss. loss_embedding (dict | None): Config of corner embedding loss. Default: AssociativeEmbeddingLoss. loss_offset (dict | None): Config of corner offset loss. Default: SmoothL1Loss. """ def __init__(self, num_classes, in_channels, num_feat_levels=2, corner_emb_channels=1, train_cfg=None, test_cfg=None, loss_heatmap=dict( type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), loss_embedding=dict( type='AssociativeEmbeddingLoss', pull_weight=0.25, push_weight=0.25), loss_offset=dict( type='SmoothL1Loss', beta=1.0, loss_weight=1)): super(CornerHead, self).__init__() self.num_classes = num_classes self.in_channels = in_channels self.corner_emb_channels = corner_emb_channels self.with_corner_emb = self.corner_emb_channels > 0 self.corner_offset_channels = 2 self.num_feat_levels = num_feat_levels self.loss_heatmap = build_loss( loss_heatmap) if loss_heatmap is not None else None self.loss_embedding = build_loss( loss_embedding) if loss_embedding is not None else None self.loss_offset = build_loss( loss_offset) if loss_offset is not None else None self.train_cfg = train_cfg self.test_cfg = test_cfg self._init_layers() def _make_layers(self, out_channels, in_channels=256, feat_channels=256): """Initialize conv sequential for CornerHead.""" return nn.Sequential( ConvModule(in_channels, feat_channels, 3, padding=1), ConvModule( feat_channels, out_channels, 1, norm_cfg=None, act_cfg=None)) def _init_corner_kpt_layers(self): """Initialize corner keypoint layers. Including corner heatmap branch and corner offset branch. Each branch has two parts: prefix `tl_` for top-left and `br_` for bottom-right. """ self.tl_pool, self.br_pool = nn.ModuleList(), nn.ModuleList() self.tl_heat, self.br_heat = nn.ModuleList(), nn.ModuleList() self.tl_off, self.br_off = nn.ModuleList(), nn.ModuleList() for _ in range(self.num_feat_levels): self.tl_pool.append( BiCornerPool( self.in_channels, ['top', 'left'], out_channels=self.in_channels)) self.br_pool.append( BiCornerPool( self.in_channels, ['bottom', 'right'], out_channels=self.in_channels)) self.tl_heat.append( self._make_layers( out_channels=self.num_classes, in_channels=self.in_channels)) self.br_heat.append( self._make_layers( out_channels=self.num_classes, in_channels=self.in_channels)) self.tl_off.append( self._make_layers( out_channels=self.corner_offset_channels, in_channels=self.in_channels)) self.br_off.append( self._make_layers( out_channels=self.corner_offset_channels, in_channels=self.in_channels)) def _init_corner_emb_layers(self): """Initialize corner embedding layers. Only include corner embedding branch with two parts: prefix `tl_` for top-left and `br_` for bottom-right. """ self.tl_emb, self.br_emb = nn.ModuleList(), nn.ModuleList() for _ in range(self.num_feat_levels): self.tl_emb.append( self._make_layers( out_channels=self.corner_emb_channels, in_channels=self.in_channels)) self.br_emb.append( self._make_layers( out_channels=self.corner_emb_channels, in_channels=self.in_channels)) def _init_layers(self): """Initialize layers for CornerHead. Including two parts: corner keypoint layers and corner embedding layers """ self._init_corner_kpt_layers() if self.with_corner_emb: self._init_corner_emb_layers() def init_weights(self): """Initialize weights of the head.""" bias_init = bias_init_with_prob(0.1) for i in range(self.num_feat_levels): # The initialization of parameters are different between nn.Conv2d # and ConvModule. Our experiments show that using the original # initialization of nn.Conv2d increases the final mAP by about 0.2% self.tl_heat[i][-1].conv.reset_parameters() self.tl_heat[i][-1].conv.bias.data.fill_(bias_init) self.br_heat[i][-1].conv.reset_parameters() self.br_heat[i][-1].conv.bias.data.fill_(bias_init) self.tl_off[i][-1].conv.reset_parameters() self.br_off[i][-1].conv.reset_parameters() if self.with_corner_emb: self.tl_emb[i][-1].conv.reset_parameters() self.br_emb[i][-1].conv.reset_parameters() def forward(self, feats): """Forward features from the upstream network. Args: feats (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor. Returns: tuple: Usually a tuple of corner heatmaps, offset heatmaps and embedding heatmaps. - tl_heats (list[Tensor]): Top-left corner heatmaps for all levels, each is a 4D-tensor, the channels number is num_classes. - br_heats (list[Tensor]): Bottom-right corner heatmaps for all levels, each is a 4D-tensor, the channels number is num_classes. - tl_embs (list[Tensor] | list[None]): Top-left embedding heatmaps for all levels, each is a 4D-tensor or None. If not None, the channels number is corner_emb_channels. - br_embs (list[Tensor] | list[None]): Bottom-right embedding heatmaps for all levels, each is a 4D-tensor or None. If not None, the channels number is corner_emb_channels. - tl_offs (list[Tensor]): Top-left offset heatmaps for all levels, each is a 4D-tensor. The channels number is corner_offset_channels. - br_offs (list[Tensor]): Bottom-right offset heatmaps for all levels, each is a 4D-tensor. The channels number is corner_offset_channels. """ lvl_ind = list(range(self.num_feat_levels)) return multi_apply(self.forward_single, feats, lvl_ind) def forward_single(self, x, lvl_ind, return_pool=False): """Forward feature of a single level. Args: x (Tensor): Feature of a single level. lvl_ind (int): Level index of current feature. return_pool (bool): Return corner pool feature or not. Returns: tuple[Tensor]: A tuple of CornerHead's output for current feature level. Containing the following Tensors: - tl_heat (Tensor): Predicted top-left corner heatmap. - br_heat (Tensor): Predicted bottom-right corner heatmap. - tl_emb (Tensor | None): Predicted top-left embedding heatmap. None for `self.with_corner_emb == False`. - br_emb (Tensor | None): Predicted bottom-right embedding heatmap. None for `self.with_corner_emb == False`. - tl_off (Tensor): Predicted top-left offset heatmap. - br_off (Tensor): Predicted bottom-right offset heatmap. - tl_pool (Tensor): Top-left corner pool feature. Not must have. - br_pool (Tensor): Bottom-right corner pool feature. Not must have. """ tl_pool = self.tl_pool[lvl_ind](x) tl_heat = self.tl_heat[lvl_ind](tl_pool) br_pool = self.br_pool[lvl_ind](x) br_heat = self.br_heat[lvl_ind](br_pool) tl_emb, br_emb = None, None if self.with_corner_emb: tl_emb = self.tl_emb[lvl_ind](tl_pool) br_emb = self.br_emb[lvl_ind](br_pool) tl_off = self.tl_off[lvl_ind](tl_pool) br_off = self.br_off[lvl_ind](br_pool) result_list = [tl_heat, br_heat, tl_emb, br_emb, tl_off, br_off] if return_pool: result_list.append(tl_pool) result_list.append(br_pool) return result_list def get_targets(self, gt_bboxes, gt_labels, feat_shape, img_shape, with_corner_emb=False, with_guiding_shift=False, with_centripetal_shift=False): """Generate corner targets. Including corner heatmap, corner offset. Optional: corner embedding, corner guiding shift, centripetal shift. For CornerNet, we generate corner heatmap, corner offset and corner embedding from this function. For CentripetalNet, we generate corner heatmap, corner offset, guiding shift and centripetal shift from this function. Args: gt_bboxes (list[Tensor]): Ground truth bboxes of each image, each has shape (num_gt, 4). gt_labels (list[Tensor]): Ground truth labels of each box, each has shape (num_gt,). feat_shape (list[int]): Shape of output feature, [batch, channel, height, width]. img_shape (list[int]): Shape of input image, [height, width, channel]. with_corner_emb (bool): Generate corner embedding target or not. Default: False. with_guiding_shift (bool): Generate guiding shift target or not. Default: False. with_centripetal_shift (bool): Generate centripetal shift target or not. Default: False. Returns: dict: Ground truth of corner heatmap, corner offset, corner embedding, guiding shift and centripetal shift. Containing the following keys: - topleft_heatmap (Tensor): Ground truth top-left corner heatmap. - bottomright_heatmap (Tensor): Ground truth bottom-right corner heatmap. - topleft_offset (Tensor): Ground truth top-left corner offset. - bottomright_offset (Tensor): Ground truth bottom-right corner offset. - corner_embedding (list[list[list[int]]]): Ground truth corner embedding. Not must have. - topleft_guiding_shift (Tensor): Ground truth top-left corner guiding shift. Not must have. - bottomright_guiding_shift (Tensor): Ground truth bottom-right corner guiding shift. Not must have. - topleft_centripetal_shift (Tensor): Ground truth top-left corner centripetal shift. Not must have. - bottomright_centripetal_shift (Tensor): Ground truth bottom-right corner centripetal shift. Not must have. """ batch_size, _, height, width = feat_shape img_h, img_w = img_shape[:2] width_ratio = float(width / img_w) height_ratio = float(height / img_h) gt_tl_heatmap = gt_bboxes[-1].new_zeros( [batch_size, self.num_classes, height, width]) gt_br_heatmap = gt_bboxes[-1].new_zeros( [batch_size, self.num_classes, height, width]) gt_tl_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width]) gt_br_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width]) if with_corner_emb: match = [] # Guiding shift is a kind of offset, from center to corner if with_guiding_shift: gt_tl_guiding_shift = gt_bboxes[-1].new_zeros( [batch_size, 2, height, width]) gt_br_guiding_shift = gt_bboxes[-1].new_zeros( [batch_size, 2, height, width]) # Centripetal shift is also a kind of offset, from center to corner # and normalized by log. if with_centripetal_shift: gt_tl_centripetal_shift = gt_bboxes[-1].new_zeros( [batch_size, 2, height, width]) gt_br_centripetal_shift = gt_bboxes[-1].new_zeros( [batch_size, 2, height, width]) for batch_id in range(batch_size): # Ground truth of corner embedding per image is a list of coord set corner_match = [] for box_id in range(len(gt_labels[batch_id])): left, top, right, bottom = gt_bboxes[batch_id][box_id] center_x = (left + right) / 2.0 center_y = (top + bottom) / 2.0 label = gt_labels[batch_id][box_id] # Use coords in the feature level to generate ground truth scale_left = left * width_ratio scale_right = right * width_ratio scale_top = top * height_ratio scale_bottom = bottom * height_ratio scale_center_x = center_x * width_ratio scale_center_y = center_y * height_ratio # Int coords on feature map/ground truth tensor left_idx = int(min(scale_left, width - 1)) right_idx = int(min(scale_right, width - 1)) top_idx = int(min(scale_top, height - 1)) bottom_idx = int(min(scale_bottom, height - 1)) # Generate gaussian heatmap scale_box_width = ceil(scale_right - scale_left) scale_box_height = ceil(scale_bottom - scale_top) radius = gaussian_radius((scale_box_height, scale_box_width), min_overlap=0.3) radius = max(0, int(radius)) gt_tl_heatmap[batch_id, label] = gen_gaussian_target( gt_tl_heatmap[batch_id, label], [left_idx, top_idx], radius) gt_br_heatmap[batch_id, label] = gen_gaussian_target( gt_br_heatmap[batch_id, label], [right_idx, bottom_idx], radius) # Generate corner offset left_offset = scale_left - left_idx top_offset = scale_top - top_idx right_offset = scale_right - right_idx bottom_offset = scale_bottom - bottom_idx gt_tl_offset[batch_id, 0, top_idx, left_idx] = left_offset gt_tl_offset[batch_id, 1, top_idx, left_idx] = top_offset gt_br_offset[batch_id, 0, bottom_idx, right_idx] = right_offset gt_br_offset[batch_id, 1, bottom_idx, right_idx] = bottom_offset # Generate corner embedding if with_corner_emb: corner_match.append([[top_idx, left_idx], [bottom_idx, right_idx]]) # Generate guiding shift if with_guiding_shift: gt_tl_guiding_shift[batch_id, 0, top_idx, left_idx] = scale_center_x - left_idx gt_tl_guiding_shift[batch_id, 1, top_idx, left_idx] = scale_center_y - top_idx gt_br_guiding_shift[batch_id, 0, bottom_idx, right_idx] = right_idx - scale_center_x gt_br_guiding_shift[ batch_id, 1, bottom_idx, right_idx] = bottom_idx - scale_center_y # Generate centripetal shift if with_centripetal_shift: gt_tl_centripetal_shift[batch_id, 0, top_idx, left_idx] = log(scale_center_x - scale_left) gt_tl_centripetal_shift[batch_id, 1, top_idx, left_idx] = log(scale_center_y - scale_top) gt_br_centripetal_shift[batch_id, 0, bottom_idx, right_idx] = log(scale_right - scale_center_x) gt_br_centripetal_shift[batch_id, 1, bottom_idx, right_idx] = log(scale_bottom - scale_center_y) if with_corner_emb: match.append(corner_match) target_result = dict( topleft_heatmap=gt_tl_heatmap, topleft_offset=gt_tl_offset, bottomright_heatmap=gt_br_heatmap, bottomright_offset=gt_br_offset) if with_corner_emb: target_result.update(corner_embedding=match) if with_guiding_shift: target_result.update( topleft_guiding_shift=gt_tl_guiding_shift, bottomright_guiding_shift=gt_br_guiding_shift) if with_centripetal_shift: target_result.update( topleft_centripetal_shift=gt_tl_centripetal_shift, bottomright_centripetal_shift=gt_br_centripetal_shift) return target_result def loss(self, tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore=None): """Compute losses of the head. Args: tl_heats (list[Tensor]): Top-left corner heatmaps for each level with shape (N, num_classes, H, W). br_heats (list[Tensor]): Bottom-right corner heatmaps for each level with shape (N, num_classes, H, W). tl_embs (list[Tensor]): Top-left corner embeddings for each level with shape (N, corner_emb_channels, H, W). br_embs (list[Tensor]): Bottom-right corner embeddings for each level with shape (N, corner_emb_channels, H, W). tl_offs (list[Tensor]): Top-left corner offsets for each level with shape (N, corner_offset_channels, H, W). br_offs (list[Tensor]): Bottom-right corner offsets for each level with shape (N, corner_offset_channels, H, W). gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [left, top, right, bottom] format. gt_labels (list[Tensor]): Class indices corresponding to each box. img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. gt_bboxes_ignore (list[Tensor] | None): Specify which bounding boxes can be ignored when computing the loss. Returns: dict[str, Tensor]: A dictionary of loss components. Containing the following losses: - det_loss (list[Tensor]): Corner keypoint losses of all feature levels. - pull_loss (list[Tensor]): Part one of AssociativeEmbedding losses of all feature levels. - push_loss (list[Tensor]): Part two of AssociativeEmbedding losses of all feature levels. - off_loss (list[Tensor]): Corner offset losses of all feature levels. """ targets = self.get_targets( gt_bboxes, gt_labels, tl_heats[-1].shape, img_metas[0]['pad_shape'], with_corner_emb=self.with_corner_emb) mlvl_targets = [targets for _ in range(self.num_feat_levels)] det_losses, pull_losses, push_losses, off_losses = multi_apply( self.loss_single, tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs, mlvl_targets) loss_dict = dict(det_loss=det_losses, off_loss=off_losses) if self.with_corner_emb: loss_dict.update(pull_loss=pull_losses, push_loss=push_losses) return loss_dict def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off, targets): """Compute losses for single level. Args: tl_hmp (Tensor): Top-left corner heatmap for current level with shape (N, num_classes, H, W). br_hmp (Tensor): Bottom-right corner heatmap for current level with shape (N, num_classes, H, W). tl_emb (Tensor): Top-left corner embedding for current level with shape (N, corner_emb_channels, H, W). br_emb (Tensor): Bottom-right corner embedding for current level with shape (N, corner_emb_channels, H, W). tl_off (Tensor): Top-left corner offset for current level with shape (N, corner_offset_channels, H, W). br_off (Tensor): Bottom-right corner offset for current level with shape (N, corner_offset_channels, H, W). targets (dict): Corner target generated by `get_targets`. Returns: tuple[torch.Tensor]: Losses of the head's differnet branches containing the following losses: - det_loss (Tensor): Corner keypoint loss. - pull_loss (Tensor): Part one of AssociativeEmbedding loss. - push_loss (Tensor): Part two of AssociativeEmbedding loss. - off_loss (Tensor): Corner offset loss. """ gt_tl_hmp = targets['topleft_heatmap'] gt_br_hmp = targets['bottomright_heatmap'] gt_tl_off = targets['topleft_offset'] gt_br_off = targets['bottomright_offset'] gt_embedding = targets['corner_embedding'] # Detection loss tl_det_loss = self.loss_heatmap( tl_hmp.sigmoid(), gt_tl_hmp, avg_factor=max(1, gt_tl_hmp.eq(1).sum())) br_det_loss = self.loss_heatmap( br_hmp.sigmoid(), gt_br_hmp, avg_factor=max(1, gt_br_hmp.eq(1).sum())) det_loss = (tl_det_loss + br_det_loss) / 2.0 # AssociativeEmbedding loss if self.with_corner_emb and self.loss_embedding is not None: pull_loss, push_loss = self.loss_embedding(tl_emb, br_emb, gt_embedding) else: pull_loss, push_loss = None, None # Offset loss # We only compute the offset loss at the real corner position. # The value of real corner would be 1 in heatmap ground truth. # The mask is computed in class agnostic mode and its shape is # batch * 1 * width * height. tl_off_mask = gt_tl_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as( gt_tl_hmp) br_off_mask = gt_br_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as( gt_br_hmp) tl_off_loss = self.loss_offset( tl_off, gt_tl_off, tl_off_mask, avg_factor=max(1, tl_off_mask.sum())) br_off_loss = self.loss_offset( br_off, gt_br_off, br_off_mask, avg_factor=max(1, br_off_mask.sum())) off_loss = (tl_off_loss + br_off_loss) / 2.0 return det_loss, pull_loss, push_loss, off_loss def get_bboxes(self, tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs, img_metas, rescale=False, with_nms=True): """Transform network output for a batch into bbox predictions. Args: tl_heats (list[Tensor]): Top-left corner heatmaps for each level with shape (N, num_classes, H, W). br_heats (list[Tensor]): Bottom-right corner heatmaps for each level with shape (N, num_classes, H, W). tl_embs (list[Tensor]): Top-left corner embeddings for each level with shape (N, corner_emb_channels, H, W). br_embs (list[Tensor]): Bottom-right corner embeddings for each level with shape (N, corner_emb_channels, H, W). tl_offs (list[Tensor]): Top-left corner offsets for each level with shape (N, corner_offset_channels, H, W). br_offs (list[Tensor]): Bottom-right corner offsets for each level with shape (N, corner_offset_channels, H, W). img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. rescale (bool): If True, return boxes in original image space. Default: False. with_nms (bool): If True, do nms before return boxes. Default: True. """ assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas) result_list = [] for img_id in range(len(img_metas)): result_list.append( self._get_bboxes_single( tl_heats[-1][img_id:img_id + 1, :], br_heats[-1][img_id:img_id + 1, :], tl_offs[-1][img_id:img_id + 1, :], br_offs[-1][img_id:img_id + 1, :], img_metas[img_id], tl_emb=tl_embs[-1][img_id:img_id + 1, :], br_emb=br_embs[-1][img_id:img_id + 1, :], rescale=rescale, with_nms=with_nms)) return result_list def _get_bboxes_single(self, tl_heat, br_heat, tl_off, br_off, img_meta, tl_emb=None, br_emb=None, tl_centripetal_shift=None, br_centripetal_shift=None, rescale=False, with_nms=True): """Transform outputs for a single batch item into bbox predictions. Args: tl_heat (Tensor): Top-left corner heatmap for current level with shape (N, num_classes, H, W). br_heat (Tensor): Bottom-right corner heatmap for current level with shape (N, num_classes, H, W). tl_off (Tensor): Top-left corner offset for current level with shape (N, corner_offset_channels, H, W). br_off (Tensor): Bottom-right corner offset for current level with shape (N, corner_offset_channels, H, W). img_meta (dict): Meta information of current image, e.g., image size, scaling factor, etc. tl_emb (Tensor): Top-left corner embedding for current level with shape (N, corner_emb_channels, H, W). br_emb (Tensor): Bottom-right corner embedding for current level with shape (N, corner_emb_channels, H, W). tl_centripetal_shift: Top-left corner's centripetal shift for current level with shape (N, 2, H, W). br_centripetal_shift: Bottom-right corner's centripetal shift for current level with shape (N, 2, H, W). rescale (bool): If True, return boxes in original image space. Default: False. with_nms (bool): If True, do nms before return boxes. Default: True. """ if isinstance(img_meta, (list, tuple)): img_meta = img_meta[0] batch_bboxes, batch_scores, batch_clses = self.decode_heatmap( tl_heat=tl_heat.sigmoid(), br_heat=br_heat.sigmoid(), tl_off=tl_off, br_off=br_off, tl_emb=tl_emb, br_emb=br_emb, tl_centripetal_shift=tl_centripetal_shift, br_centripetal_shift=br_centripetal_shift, img_meta=img_meta, k=self.test_cfg.corner_topk, kernel=self.test_cfg.local_maximum_kernel, distance_threshold=self.test_cfg.distance_threshold) if rescale: batch_bboxes /= img_meta['scale_factor'] bboxes = batch_bboxes.view([-1, 4]) scores = batch_scores.view([-1, 1]) clses = batch_clses.view([-1, 1]) idx = scores.argsort(dim=0, descending=True) bboxes = bboxes[idx].view([-1, 4]) scores = scores[idx].view(-1) clses = clses[idx].view(-1) detections = torch.cat([bboxes, scores.unsqueeze(-1)], -1) keepinds = (detections[:, -1] > -0.1) detections = detections[keepinds] labels = clses[keepinds] if with_nms: detections, labels = self._bboxes_nms(detections, labels, self.test_cfg) return detections, labels def _bboxes_nms(self, bboxes, labels, cfg): out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels, cfg.nms_cfg) out_labels = labels[keep] if len(out_bboxes) > 0: idx = torch.argsort(out_bboxes[:, -1], descending=True) idx = idx[:cfg.max_per_img] out_bboxes = out_bboxes[idx] out_labels = out_labels[idx] return out_bboxes, out_labels def _gather_feat(self, feat, ind, mask=None): """Gather feature according to index. Args: feat (Tensor): Target feature map. ind (Tensor): Target coord index. mask (Tensor | None): Mask of featuremap. Default: None. Returns: feat (Tensor): Gathered feature. """ dim = feat.size(2) ind = ind.unsqueeze(2).repeat(1, 1, dim) feat = feat.gather(1, ind) if mask is not None: mask = mask.unsqueeze(2).expand_as(feat) feat = feat[mask] feat = feat.view(-1, dim) return feat def _local_maximum(self, heat, kernel=3): """Extract local maximum pixel with given kernal. Args: heat (Tensor): Target heatmap. kernel (int): Kernel size of max pooling. Default: 3. Returns: heat (Tensor): A heatmap where local maximum pixels maintain its own value and other positions are 0. """ pad = (kernel - 1) // 2 hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad) keep = (hmax == heat).float() return heat * keep def _transpose_and_gather_feat(self, feat, ind): """Transpose and gather feature according to index. Args: feat (Tensor): Target feature map. ind (Tensor): Target coord index. Returns: feat (Tensor): Transposed and gathered feature. """ feat = feat.permute(0, 2, 3, 1).contiguous() feat = feat.view(feat.size(0), -1, feat.size(3)) feat = self._gather_feat(feat, ind) return feat def _topk(self, scores, k=20): """Get top k positions from heatmap. Args: scores (Tensor): Target heatmap with shape [batch, num_classes, height, width]. k (int): Target number. Default: 20. Returns: tuple[torch.Tensor]: Scores, indexes, categories and coords of topk keypoint. Containing following Tensors: - topk_scores (Tensor): Max scores of each topk keypoint. - topk_inds (Tensor): Indexes of each topk keypoint. - topk_clses (Tensor): Categories of each topk keypoint. - topk_ys (Tensor): Y-coord of each topk keypoint. - topk_xs (Tensor): X-coord of each topk keypoint. """ batch, _, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, -1), k) topk_clses = topk_inds // (height * width) topk_inds = topk_inds % (height * width) topk_ys = topk_inds // width topk_xs = (topk_inds % width).int().float() return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs def decode_heatmap(self, tl_heat, br_heat, tl_off, br_off, tl_emb=None, br_emb=None, tl_centripetal_shift=None, br_centripetal_shift=None, img_meta=None, k=100, kernel=3, distance_threshold=0.5, num_dets=1000): """Transform outputs for a single batch item into raw bbox predictions. Args: tl_heat (Tensor): Top-left corner heatmap for current level with shape (N, num_classes, H, W). br_heat (Tensor): Bottom-right corner heatmap for current level with shape (N, num_classes, H, W). tl_off (Tensor): Top-left corner offset for current level with shape (N, corner_offset_channels, H, W). br_off (Tensor): Bottom-right corner offset for current level with shape (N, corner_offset_channels, H, W). tl_emb (Tensor | None): Top-left corner embedding for current level with shape (N, corner_emb_channels, H, W). br_emb (Tensor | None): Bottom-right corner embedding for current level with shape (N, corner_emb_channels, H, W). tl_centripetal_shift (Tensor | None): Top-left centripetal shift for current level with shape (N, 2, H, W). br_centripetal_shift (Tensor | None): Bottom-right centripetal shift for current level with shape (N, 2, H, W). img_meta (dict): Meta information of current image, e.g., image size, scaling factor, etc. k (int): Get top k corner keypoints from heatmap. kernel (int): Max pooling kernel for extract local maximum pixels. distance_threshold (float): Distance threshold. Top-left and bottom-right corner keypoints with feature distance less than the threshold will be regarded as keypoints from same object. num_dets (int): Num of raw boxes before doing nms. Returns: tuple[torch.Tensor]: Decoded output of CornerHead, containing the following Tensors: - bboxes (Tensor): Coords of each box. - scores (Tensor): Scores of each box. - clses (Tensor): Categories of each box. """ with_embedding = tl_emb is not None and br_emb is not None with_centripetal_shift = ( tl_centripetal_shift is not None and br_centripetal_shift is not None) assert with_embedding + with_centripetal_shift == 1 batch, _, height, width = tl_heat.size() inp_h, inp_w, _ = img_meta['pad_shape'] # perform nms on heatmaps tl_heat = self._local_maximum(tl_heat, kernel=kernel) br_heat = self._local_maximum(br_heat, kernel=kernel) tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = self._topk(tl_heat, k=k) br_scores, br_inds, br_clses, br_ys, br_xs = self._topk(br_heat, k=k) # We use repeat instead of expand here because expand is a # shallow-copy function. Thus it could cause unexpected testing result # sometimes. Using expand will decrease about 10% mAP during testing # compared to repeat. tl_ys = tl_ys.view(batch, k, 1).repeat(1, 1, k) tl_xs = tl_xs.view(batch, k, 1).repeat(1, 1, k) br_ys = br_ys.view(batch, 1, k).repeat(1, k, 1) br_xs = br_xs.view(batch, 1, k).repeat(1, k, 1) tl_off = self._transpose_and_gather_feat(tl_off, tl_inds) tl_off = tl_off.view(batch, k, 1, 2) br_off = self._transpose_and_gather_feat(br_off, br_inds) br_off = br_off.view(batch, 1, k, 2) tl_xs = tl_xs + tl_off[..., 0] tl_ys = tl_ys + tl_off[..., 1] br_xs = br_xs + br_off[..., 0] br_ys = br_ys + br_off[..., 1] if with_centripetal_shift: tl_centripetal_shift = self._transpose_and_gather_feat( tl_centripetal_shift, tl_inds).view(batch, k, 1, 2).exp() br_centripetal_shift = self._transpose_and_gather_feat( br_centripetal_shift, br_inds).view(batch, 1, k, 2).exp() tl_ctxs = tl_xs + tl_centripetal_shift[..., 0] tl_ctys = tl_ys + tl_centripetal_shift[..., 1] br_ctxs = br_xs - br_centripetal_shift[..., 0] br_ctys = br_ys - br_centripetal_shift[..., 1] # all possible boxes based on top k corners (ignoring class) tl_xs *= (inp_w / width) tl_ys *= (inp_h / height) br_xs *= (inp_w / width) br_ys *= (inp_h / height) if with_centripetal_shift: tl_ctxs *= (inp_w / width) tl_ctys *= (inp_h / height) br_ctxs *= (inp_w / width) br_ctys *= (inp_h / height) x_off = img_meta['border'][2] y_off = img_meta['border'][0] tl_xs -= x_off tl_ys -= y_off br_xs -= x_off br_ys -= y_off tl_xs *= tl_xs.gt(0.0).type_as(tl_xs) tl_ys *= tl_ys.gt(0.0).type_as(tl_ys) br_xs *= br_xs.gt(0.0).type_as(br_xs) br_ys *= br_ys.gt(0.0).type_as(br_ys) bboxes = torch.stack((tl_xs, tl_ys, br_xs, br_ys), dim=3) area_bboxes = ((br_xs - tl_xs) * (br_ys - tl_ys)).abs() if with_centripetal_shift: tl_ctxs -= x_off tl_ctys -= y_off br_ctxs -= x_off br_ctys -= y_off tl_ctxs *= tl_ctxs.gt(0.0).type_as(tl_ctxs) tl_ctys *= tl_ctys.gt(0.0).type_as(tl_ctys) br_ctxs *= br_ctxs.gt(0.0).type_as(br_ctxs) br_ctys *= br_ctys.gt(0.0).type_as(br_ctys) ct_bboxes = torch.stack((tl_ctxs, tl_ctys, br_ctxs, br_ctys), dim=3) area_ct_bboxes = ((br_ctxs - tl_ctxs) * (br_ctys - tl_ctys)).abs() rcentral = torch.zeros_like(ct_bboxes) # magic nums from paper section 4.1 mu = torch.ones_like(area_bboxes) / 2.4 mu[area_bboxes > 3500] = 1 / 2.1 # large bbox have smaller mu bboxes_center_x = (bboxes[..., 0] + bboxes[..., 2]) / 2 bboxes_center_y = (bboxes[..., 1] + bboxes[..., 3]) / 2 rcentral[..., 0] = bboxes_center_x - mu * (bboxes[..., 2] - bboxes[..., 0]) / 2 rcentral[..., 1] = bboxes_center_y - mu * (bboxes[..., 3] - bboxes[..., 1]) / 2 rcentral[..., 2] = bboxes_center_x + mu * (bboxes[..., 2] - bboxes[..., 0]) / 2 rcentral[..., 3] = bboxes_center_y + mu * (bboxes[..., 3] - bboxes[..., 1]) / 2 area_rcentral = ((rcentral[..., 2] - rcentral[..., 0]) * (rcentral[..., 3] - rcentral[..., 1])).abs() dists = area_ct_bboxes / area_rcentral tl_ctx_inds = (ct_bboxes[..., 0] <= rcentral[..., 0]) | ( ct_bboxes[..., 0] >= rcentral[..., 2]) tl_cty_inds = (ct_bboxes[..., 1] <= rcentral[..., 1]) | ( ct_bboxes[..., 1] >= rcentral[..., 3]) br_ctx_inds = (ct_bboxes[..., 2] <= rcentral[..., 0]) | ( ct_bboxes[..., 2] >= rcentral[..., 2]) br_cty_inds = (ct_bboxes[..., 3] <= rcentral[..., 1]) | ( ct_bboxes[..., 3] >= rcentral[..., 3]) if with_embedding: tl_emb = self._transpose_and_gather_feat(tl_emb, tl_inds) tl_emb = tl_emb.view(batch, k, 1) br_emb = self._transpose_and_gather_feat(br_emb, br_inds) br_emb = br_emb.view(batch, 1, k) dists = torch.abs(tl_emb - br_emb) tl_scores = tl_scores.view(batch, k, 1).repeat(1, 1, k) br_scores = br_scores.view(batch, 1, k).repeat(1, k, 1) scores = (tl_scores + br_scores) / 2 # scores for all possible boxes # tl and br should have same class tl_clses = tl_clses.view(batch, k, 1).repeat(1, 1, k) br_clses = br_clses.view(batch, 1, k).repeat(1, k, 1) cls_inds = (tl_clses != br_clses) # reject boxes based on distances dist_inds = dists > distance_threshold # reject boxes based on widths and heights width_inds = (br_xs <= tl_xs) height_inds = (br_ys <= tl_ys) scores[cls_inds] = -1 scores[width_inds] = -1 scores[height_inds] = -1 scores[dist_inds] = -1 if with_centripetal_shift: scores[tl_ctx_inds] = -1 scores[tl_cty_inds] = -1 scores[br_ctx_inds] = -1 scores[br_cty_inds] = -1 scores = scores.view(batch, -1) scores, inds = torch.topk(scores, num_dets) scores = scores.unsqueeze(2) bboxes = bboxes.view(batch, -1, 4) bboxes = self._gather_feat(bboxes, inds) clses = tl_clses.contiguous().view(batch, -1, 1) clses = self._gather_feat(clses, inds).float() return bboxes, scores, clses

insightface/detection/scrfd/mmdet/models/dense_heads/corner_head.py/0

import torch.nn as nn from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init from ..builder import HEADS from .anchor_head import AnchorHead @HEADS.register_module() class RetinaSepBNHead(AnchorHead): """"RetinaHead with separate BN. In RetinaHead, conv/norm layers are shared across different FPN levels, while in RetinaSepBNHead, conv layers are shared across different FPN levels, but BN layers are separated. """ def __init__(self, num_classes, num_ins, in_channels, stacked_convs=4, conv_cfg=None, norm_cfg=None, **kwargs): self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.num_ins = num_ins super(RetinaSepBNHead, self).__init__(num_classes, in_channels, **kwargs) def _init_layers(self): """Initialize layers of the head.""" self.relu = nn.ReLU(inplace=True) self.cls_convs = nn.ModuleList() self.reg_convs = nn.ModuleList() for i in range(self.num_ins): cls_convs = nn.ModuleList() reg_convs = nn.ModuleList() for i in range(self.stacked_convs): chn = self.in_channels if i == 0 else self.feat_channels cls_convs.append( ConvModule( chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) reg_convs.append( ConvModule( chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) self.cls_convs.append(cls_convs) self.reg_convs.append(reg_convs) for i in range(self.stacked_convs): for j in range(1, self.num_ins): self.cls_convs[j][i].conv = self.cls_convs[0][i].conv self.reg_convs[j][i].conv = self.reg_convs[0][i].conv self.retina_cls = nn.Conv2d( self.feat_channels, self.num_anchors * self.cls_out_channels, 3, padding=1) self.retina_reg = nn.Conv2d( self.feat_channels, self.num_anchors * 4, 3, padding=1) def init_weights(self): """Initialize weights of the head.""" for m in self.cls_convs[0]: normal_init(m.conv, std=0.01) for m in self.reg_convs[0]: normal_init(m.conv, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.retina_cls, std=0.01, bias=bias_cls) normal_init(self.retina_reg, std=0.01) def forward(self, feats): """Forward features from the upstream network. Args: feats (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor. Returns: tuple: Usually a tuple of classification scores and bbox prediction cls_scores (list[Tensor]): Classification scores for all scale levels, each is a 4D-tensor, the channels number is num_anchors * num_classes. bbox_preds (list[Tensor]): Box energies / deltas for all scale levels, each is a 4D-tensor, the channels number is num_anchors * 4. """ cls_scores = [] bbox_preds = [] for i, x in enumerate(feats): cls_feat = feats[i] reg_feat = feats[i] for cls_conv in self.cls_convs[i]: cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs[i]: reg_feat = reg_conv(reg_feat) cls_score = self.retina_cls(cls_feat) bbox_pred = self.retina_reg(reg_feat) cls_scores.append(cls_score) bbox_preds.append(bbox_pred) return cls_scores, bbox_preds

insightface/detection/scrfd/mmdet/models/dense_heads/retina_sepbn_head.py/0

from ..builder import DETECTORS from .two_stage import TwoStageDetector @DETECTORS.register_module() class FastRCNN(TwoStageDetector): """Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_""" def __init__(self, backbone, roi_head, train_cfg, test_cfg, neck=None, pretrained=None): super(FastRCNN, self).__init__( backbone=backbone, neck=neck, roi_head=roi_head, train_cfg=train_cfg, test_cfg=test_cfg, pretrained=pretrained) def forward_test(self, imgs, img_metas, proposals, **kwargs): """ Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape NxCxHxW, which contains all images in the batch. img_metas (List[List[dict]]): the outer list indicates test-time augs (multiscale, flip, etc.) and the inner list indicates images in a batch. proposals (List[List[Tensor]]): the outer list indicates test-time augs (multiscale, flip, etc.) and the inner list indicates images in a batch. The Tensor should have a shape Px4, where P is the number of proposals. """ for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: if not isinstance(var, list): raise TypeError(f'{name} must be a list, but got {type(var)}') num_augs = len(imgs) if num_augs != len(img_metas): raise ValueError(f'num of augmentations ({len(imgs)}) ' f'!= num of image meta ({len(img_metas)})') if num_augs == 1: return self.simple_test(imgs[0], img_metas[0], proposals[0], **kwargs) else: # TODO: support test-time augmentation assert NotImplementedError

insightface/detection/scrfd/mmdet/models/detectors/fast_rcnn.py/0

from mmdet.core import bbox2result from ..builder import DETECTORS from .single_stage import SingleStageDetector import torch @DETECTORS.register_module() class SCRFD(SingleStageDetector): def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None): super(SCRFD, self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, pretrained) def forward_train(self, img, img_metas, gt_bboxes, gt_labels, gt_keypointss=None, gt_bboxes_ignore=None): """ Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered and std scaled. img_metas (list[dict]): A List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see :class:`mmdet.datasets.pipelines.Collect`. gt_bboxes (list[Tensor]): Each item are the truth boxes for each image in [tl_x, tl_y, br_x, br_y] format. gt_labels (list[Tensor]): Class indices corresponding to each box gt_bboxes_ignore (None | list[Tensor]): Specify which bounding boxes can be ignored when computing the loss. Returns: dict[str, Tensor]: A dictionary of loss components. """ super(SingleStageDetector, self).forward_train(img, img_metas) x = self.extract_feat(img) losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes, gt_labels, gt_keypointss, gt_bboxes_ignore) return losses def simple_test(self, img, img_metas, rescale=False): """Test function without test time augmentation. Args: imgs (list[torch.Tensor]): List of multiple images img_metas (list[dict]): List of image information. rescale (bool, optional): Whether to rescale the results. Defaults to False. Returns: list[list[np.ndarray]]: BBox results of each image and classes. The outer list corresponds to each image. The inner list corresponds to each class. """ x = self.extract_feat(img) outs = self.bbox_head(x) #print(len(outs)) if torch.onnx.is_in_onnx_export(): print('single_stage.py in-onnx-export') print(outs.__class__) cls_score, bbox_pred, kps_pred = outs for c in cls_score: print(c.shape) for c in bbox_pred: print(c.shape) #print(outs[0].shape, outs[1].shape) if self.bbox_head.use_kps: for c in kps_pred: print(c.shape) return (cls_score, bbox_pred, kps_pred) else: return (cls_score, bbox_pred) #return outs bbox_list = self.bbox_head.get_bboxes( *outs, img_metas, rescale=rescale) # skip post-processing when exporting to ONNX #if torch.onnx.is_in_onnx_export(): # return bbox_list bbox_results = [ bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) for det_bboxes, det_labels in bbox_list ] return bbox_results def feature_test(self, img): x = self.extract_feat(img) outs = self.bbox_head(x) return outs

insightface/detection/scrfd/mmdet/models/detectors/scrfd.py/0

import math import torch import torch.nn as nn from mmdet.core import bbox_overlaps from ..builder import LOSSES from .utils import weighted_loss @weighted_loss def iou_loss(pred, target, eps=1e-6): """IoU loss. Computing the IoU loss between a set of predicted bboxes and target bboxes. The loss is calculated as negative log of IoU. Args: pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (torch.Tensor): Corresponding gt bboxes, shape (n, 4). eps (float): Eps to avoid log(0). Return: torch.Tensor: Loss tensor. """ ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps) loss = -ious.log() return loss @weighted_loss def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3): """BIoULoss. This is an implementation of paper `Improving Object Localization with Fitness NMS and Bounded IoU Loss. <https://arxiv.org/abs/1711.00164>`_. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Target bboxes. beta (float): beta parameter in smoothl1. eps (float): eps to avoid NaN. """ pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5 pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5 pred_w = pred[:, 2] - pred[:, 0] pred_h = pred[:, 3] - pred[:, 1] with torch.no_grad(): target_ctrx = (target[:, 0] + target[:, 2]) * 0.5 target_ctry = (target[:, 1] + target[:, 3]) * 0.5 target_w = target[:, 2] - target[:, 0] target_h = target[:, 3] - target[:, 1] dx = target_ctrx - pred_ctrx dy = target_ctry - pred_ctry loss_dx = 1 - torch.max( (target_w - 2 * dx.abs()) / (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx)) loss_dy = 1 - torch.max( (target_h - 2 * dy.abs()) / (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy)) loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w / (target_w + eps)) loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h / (target_h + eps)) loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh], dim=-1).view(loss_dx.size(0), -1) loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta, loss_comb - 0.5 * beta) return loss @weighted_loss def giou_loss(pred, target, eps=1e-7): r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_. Args: pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (torch.Tensor): Corresponding gt bboxes, shape (n, 4). eps (float): Eps to avoid log(0). Return: Tensor: Loss tensor. """ gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps) loss = 1 - gious return loss @weighted_loss def diou_loss(pred, target, eps=1e-7): r"""`Implementation of Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_. Code is modified from https://github.com/Zzh-tju/DIoU. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (Tensor): Corresponding gt bboxes, shape (n, 4). eps (float): Eps to avoid log(0). Return: Tensor: Loss tensor. """ # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw**2 + ch**2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4 rho2 = left + right # DIoU dious = ious - rho2 / c2 loss = 1 - dious return loss @weighted_loss def ciou_loss(pred, target, eps=1e-7): r"""`Implementation of paper `Enhancing Geometric Factors into Model Learning and Inference for Object Detection and Instance Segmentation <https://arxiv.org/abs/2005.03572>`_. Code is modified from https://github.com/Zzh-tju/CIoU. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (Tensor): Corresponding gt bboxes, shape (n, 4). eps (float): Eps to avoid log(0). Return: Tensor: Loss tensor. """ # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw**2 + ch**2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4 rho2 = left + right factor = 4 / math.pi**2 v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) # CIoU cious = ious - (rho2 / c2 + v**2 / (1 - ious + v)) loss = 1 - cious return loss @LOSSES.register_module() class IoULoss(nn.Module): """IoULoss. Computing the IoU loss between a set of predicted bboxes and target bboxes. Args: eps (float): Eps to avoid log(0). reduction (str): Options are "none", "mean" and "sum". loss_weight (float): Weight of loss. """ def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): super(IoULoss, self).__init__() self.eps = eps self.reduction = reduction self.loss_weight = loss_weight def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): """Forward function. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning target of the prediction. weight (torch.Tensor, optional): The weight of loss for each prediction. Defaults to None. avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. reduction_override (str, optional): The reduction method used to override the original reduction method of the loss. Defaults to None. Options are "none", "mean" and "sum". """ assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( reduction_override if reduction_override else self.reduction) if (weight is not None) and (not torch.any(weight > 0)) and ( reduction != 'none'): return (pred * weight).sum() # 0 if weight is not None and weight.dim() > 1: # TODO: remove this in the future # reduce the weight of shape (n, 4) to (n,) to match the # iou_loss of shape (n,) assert weight.shape == pred.shape weight = weight.mean(-1) loss = self.loss_weight * iou_loss( pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor, **kwargs) return loss @LOSSES.register_module() class BoundedIoULoss(nn.Module): def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0): super(BoundedIoULoss, self).__init__() self.beta = beta self.eps = eps self.reduction = reduction self.loss_weight = loss_weight def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( reduction_override if reduction_override else self.reduction) loss = self.loss_weight * bounded_iou_loss( pred, target, weight, beta=self.beta, eps=self.eps, reduction=reduction, avg_factor=avg_factor, **kwargs) return loss @LOSSES.register_module() class GIoULoss(nn.Module): def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): super(GIoULoss, self).__init__() self.eps = eps self.reduction = reduction self.loss_weight = loss_weight def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): print('XXXXX', pred.shape, weight.shape) return (weight.unsqueeze(1) * pred).sum() #return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( reduction_override if reduction_override else self.reduction) if weight is not None and weight.dim() > 1: # TODO: remove this in the future # reduce the weight of shape (n, 4) to (n,) to match the # giou_loss of shape (n,) assert weight.shape == pred.shape weight = weight.mean(-1) loss = self.loss_weight * giou_loss( pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor, **kwargs) return loss @LOSSES.register_module() class DIoULoss(nn.Module): def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): super(DIoULoss, self).__init__() self.eps = eps self.reduction = reduction self.loss_weight = loss_weight def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( reduction_override if reduction_override else self.reduction) if weight is not None and weight.dim() > 1: # TODO: remove this in the future # reduce the weight of shape (n, 4) to (n,) to match the # giou_loss of shape (n,) assert weight.shape == pred.shape weight = weight.mean(-1) loss = self.loss_weight * diou_loss( pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor, **kwargs) return loss @LOSSES.register_module() class CIoULoss(nn.Module): def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): super(CIoULoss, self).__init__() self.eps = eps self.reduction = reduction self.loss_weight = loss_weight def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( reduction_override if reduction_override else self.reduction) if weight is not None and weight.dim() > 1: # TODO: remove this in the future # reduce the weight of shape (n, 4) to (n,) to match the # giou_loss of shape (n,) assert weight.shape == pred.shape weight = weight.mean(-1) loss = self.loss_weight * ciou_loss( pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor, **kwargs) return loss

insightface/detection/scrfd/mmdet/models/losses/iou_loss.py/0

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import constant_init, kaiming_init, xavier_init from ..builder import NECKS, build_backbone from .fpn import FPN class ASPP(nn.Module): """ASPP (Atrous Spatial Pyramid Pooling) This is an implementation of the ASPP module used in DetectoRS (https://arxiv.org/pdf/2006.02334.pdf) Args: in_channels (int): Number of input channels. out_channels (int): Number of channels produced by this module dilations (tuple[int]): Dilations of the four branches. Default: (1, 3, 6, 1) """ def __init__(self, in_channels, out_channels, dilations=(1, 3, 6, 1)): super().__init__() assert dilations[-1] == 1 self.aspp = nn.ModuleList() for dilation in dilations: kernel_size = 3 if dilation > 1 else 1 padding = dilation if dilation > 1 else 0 conv = nn.Conv2d( in_channels, out_channels, kernel_size=kernel_size, stride=1, dilation=dilation, padding=padding, bias=True) self.aspp.append(conv) self.gap = nn.AdaptiveAvgPool2d(1) self.init_weights() def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): kaiming_init(m) def forward(self, x): avg_x = self.gap(x) out = [] for aspp_idx in range(len(self.aspp)): inp = avg_x if (aspp_idx == len(self.aspp) - 1) else x out.append(F.relu_(self.aspp[aspp_idx](inp))) out[-1] = out[-1].expand_as(out[-2]) out = torch.cat(out, dim=1) return out @NECKS.register_module() class RFP(FPN): """RFP (Recursive Feature Pyramid) This is an implementation of RFP in `DetectoRS <https://arxiv.org/pdf/2006.02334.pdf>`_. Different from standard FPN, the input of RFP should be multi level features along with origin input image of backbone. Args: rfp_steps (int): Number of unrolled steps of RFP. rfp_backbone (dict): Configuration of the backbone for RFP. aspp_out_channels (int): Number of output channels of ASPP module. aspp_dilations (tuple[int]): Dilation rates of four branches. Default: (1, 3, 6, 1) """ def __init__(self, rfp_steps, rfp_backbone, aspp_out_channels, aspp_dilations=(1, 3, 6, 1), **kwargs): super().__init__(**kwargs) self.rfp_steps = rfp_steps self.rfp_modules = nn.ModuleList() for rfp_idx in range(1, rfp_steps): rfp_module = build_backbone(rfp_backbone) self.rfp_modules.append(rfp_module) self.rfp_aspp = ASPP(self.out_channels, aspp_out_channels, aspp_dilations) self.rfp_weight = nn.Conv2d( self.out_channels, 1, kernel_size=1, stride=1, padding=0, bias=True) def init_weights(self): # Avoid using super().init_weights(), which may alter the default # initialization of the modules in self.rfp_modules that have missing # keys in the pretrained checkpoint. for convs in [self.lateral_convs, self.fpn_convs]: for m in convs.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') for rfp_idx in range(self.rfp_steps - 1): self.rfp_modules[rfp_idx].init_weights( self.rfp_modules[rfp_idx].pretrained) constant_init(self.rfp_weight, 0) def forward(self, inputs): inputs = list(inputs) assert len(inputs) == len(self.in_channels) + 1 # +1 for input image img = inputs.pop(0) # FPN forward x = super().forward(tuple(inputs)) for rfp_idx in range(self.rfp_steps - 1): rfp_feats = [x[0]] + list( self.rfp_aspp(x[i]) for i in range(1, len(x))) x_idx = self.rfp_modules[rfp_idx].rfp_forward(img, rfp_feats) # FPN forward x_idx = super().forward(x_idx) x_new = [] for ft_idx in range(len(x_idx)): add_weight = torch.sigmoid(self.rfp_weight(x_idx[ft_idx])) x_new.append(add_weight * x_idx[ft_idx] + (1 - add_weight) * x[ft_idx]) x = x_new return x

insightface/detection/scrfd/mmdet/models/necks/rfp.py/0

import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import Conv2d, ConvModule, build_upsample_layer from mmcv.ops.carafe import CARAFEPack from mmcv.runner import auto_fp16, force_fp32 from torch.nn.modules.utils import _pair from mmdet.core import mask_target from mmdet.models.builder import HEADS, build_loss BYTES_PER_FLOAT = 4 # TODO: This memory limit may be too much or too little. It would be better to # determine it based on available resources. GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit @HEADS.register_module() class FCNMaskHead(nn.Module): def __init__(self, num_convs=4, roi_feat_size=14, in_channels=256, conv_kernel_size=3, conv_out_channels=256, num_classes=80, class_agnostic=False, upsample_cfg=dict(type='deconv', scale_factor=2), conv_cfg=None, norm_cfg=None, loss_mask=dict( type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)): super(FCNMaskHead, self).__init__() self.upsample_cfg = upsample_cfg.copy() if self.upsample_cfg['type'] not in [ None, 'deconv', 'nearest', 'bilinear', 'carafe' ]: raise ValueError( f'Invalid upsample method {self.upsample_cfg["type"]}, ' 'accepted methods are "deconv", "nearest", "bilinear", ' '"carafe"') self.num_convs = num_convs # WARN: roi_feat_size is reserved and not used self.roi_feat_size = _pair(roi_feat_size) self.in_channels = in_channels self.conv_kernel_size = conv_kernel_size self.conv_out_channels = conv_out_channels self.upsample_method = self.upsample_cfg.get('type') self.scale_factor = self.upsample_cfg.pop('scale_factor', None) self.num_classes = num_classes self.class_agnostic = class_agnostic self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.fp16_enabled = False self.loss_mask = build_loss(loss_mask) self.convs = nn.ModuleList() for i in range(self.num_convs): in_channels = ( self.in_channels if i == 0 else self.conv_out_channels) padding = (self.conv_kernel_size - 1) // 2 self.convs.append( ConvModule( in_channels, self.conv_out_channels, self.conv_kernel_size, padding=padding, conv_cfg=conv_cfg, norm_cfg=norm_cfg)) upsample_in_channels = ( self.conv_out_channels if self.num_convs > 0 else in_channels) upsample_cfg_ = self.upsample_cfg.copy() if self.upsample_method is None: self.upsample = None elif self.upsample_method == 'deconv': upsample_cfg_.update( in_channels=upsample_in_channels, out_channels=self.conv_out_channels, kernel_size=self.scale_factor, stride=self.scale_factor) self.upsample = build_upsample_layer(upsample_cfg_) elif self.upsample_method == 'carafe': upsample_cfg_.update( channels=upsample_in_channels, scale_factor=self.scale_factor) self.upsample = build_upsample_layer(upsample_cfg_) else: # suppress warnings align_corners = (None if self.upsample_method == 'nearest' else False) upsample_cfg_.update( scale_factor=self.scale_factor, mode=self.upsample_method, align_corners=align_corners) self.upsample = build_upsample_layer(upsample_cfg_) out_channels = 1 if self.class_agnostic else self.num_classes logits_in_channel = ( self.conv_out_channels if self.upsample_method == 'deconv' else upsample_in_channels) self.conv_logits = Conv2d(logits_in_channel, out_channels, 1) self.relu = nn.ReLU(inplace=True) self.debug_imgs = None def init_weights(self): for m in [self.upsample, self.conv_logits]: if m is None: continue elif isinstance(m, CARAFEPack): m.init_weights() else: nn.init.kaiming_normal_( m.weight, mode='fan_out', nonlinearity='relu') nn.init.constant_(m.bias, 0) @auto_fp16() def forward(self, x): for conv in self.convs: x = conv(x) if self.upsample is not None: x = self.upsample(x) if self.upsample_method == 'deconv': x = self.relu(x) mask_pred = self.conv_logits(x) return mask_pred def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg): pos_proposals = [res.pos_bboxes for res in sampling_results] pos_assigned_gt_inds = [ res.pos_assigned_gt_inds for res in sampling_results ] mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds, gt_masks, rcnn_train_cfg) return mask_targets @force_fp32(apply_to=('mask_pred', )) def loss(self, mask_pred, mask_targets, labels): loss = dict() if mask_pred.size(0) == 0: loss_mask = mask_pred.sum() else: if self.class_agnostic: loss_mask = self.loss_mask(mask_pred, mask_targets, torch.zeros_like(labels)) else: loss_mask = self.loss_mask(mask_pred, mask_targets, labels) loss['loss_mask'] = loss_mask return loss def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, scale_factor, rescale): """Get segmentation masks from mask_pred and bboxes. Args: mask_pred (Tensor or ndarray): shape (n, #class, h, w). For single-scale testing, mask_pred is the direct output of model, whose type is Tensor, while for multi-scale testing, it will be converted to numpy array outside of this method. det_bboxes (Tensor): shape (n, 4/5) det_labels (Tensor): shape (n, ) img_shape (Tensor): shape (3, ) rcnn_test_cfg (dict): rcnn testing config ori_shape: original image size Returns: list[list]: encoded masks """ if isinstance(mask_pred, torch.Tensor): mask_pred = mask_pred.sigmoid() else: mask_pred = det_bboxes.new_tensor(mask_pred) device = mask_pred.device cls_segms = [[] for _ in range(self.num_classes) ] # BG is not included in num_classes bboxes = det_bboxes[:, :4] labels = det_labels if rescale: img_h, img_w = ori_shape[:2] else: if isinstance(scale_factor, float): img_h = np.round(ori_shape[0] * scale_factor).astype(np.int32) img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32) else: w_scale, h_scale = scale_factor[0], scale_factor[1] img_h = np.round(ori_shape[0] * h_scale.item()).astype( np.int32) img_w = np.round(ori_shape[1] * w_scale.item()).astype( np.int32) scale_factor = 1.0 if not isinstance(scale_factor, (float, torch.Tensor)): scale_factor = bboxes.new_tensor(scale_factor) bboxes = bboxes / scale_factor if torch.onnx.is_in_onnx_export(): # TODO: Remove after F.grid_sample is supported. from torchvision.models.detection.roi_heads \ import paste_masks_in_image masks = paste_masks_in_image(mask_pred, bboxes, ori_shape[:2]) thr = rcnn_test_cfg.get('mask_thr_binary', 0) if thr > 0: masks = masks >= thr return masks N = len(mask_pred) # The actual implementation split the input into chunks, # and paste them chunk by chunk. if device.type == 'cpu': # CPU is most efficient when they are pasted one by one with # skip_empty=True, so that it performs minimal number of # operations. num_chunks = N else: # GPU benefits from parallelism for larger chunks, # but may have memory issue num_chunks = int( np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) assert (num_chunks <= N), 'Default GPU_MEM_LIMIT is too small; try increasing it' chunks = torch.chunk(torch.arange(N, device=device), num_chunks) threshold = rcnn_test_cfg.mask_thr_binary im_mask = torch.zeros( N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8) if not self.class_agnostic: mask_pred = mask_pred[range(N), labels][:, None] for inds in chunks: masks_chunk, spatial_inds = _do_paste_mask( mask_pred[inds], bboxes[inds], img_h, img_w, skip_empty=device.type == 'cpu') if threshold >= 0: masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) else: # for visualization and debugging masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) im_mask[(inds, ) + spatial_inds] = masks_chunk for i in range(N): cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy()) return cls_segms def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): """Paste instance masks acoording to boxes. This implementation is modified from https://github.com/facebookresearch/detectron2/ Args: masks (Tensor): N, 1, H, W boxes (Tensor): N, 4 img_h (int): Height of the image to be pasted. img_w (int): Width of the image to be pasted. skip_empty (bool): Only paste masks within the region that tightly bound all boxes, and returns the results this region only. An important optimization for CPU. Returns: tuple: (Tensor, tuple). The first item is mask tensor, the second one is the slice object. If skip_empty == False, the whole image will be pasted. It will return a mask of shape (N, img_h, img_w) and an empty tuple. If skip_empty == True, only area around the mask will be pasted. A mask of shape (N, h', w') and its start and end coordinates in the original image will be returned. """ # On GPU, paste all masks together (up to chunk size) # by using the entire image to sample the masks # Compared to pasting them one by one, # this has more operations but is faster on COCO-scale dataset. device = masks.device if skip_empty: x0_int, y0_int = torch.clamp( boxes.min(dim=0).values.floor()[:2] - 1, min=0).to(dtype=torch.int32) x1_int = torch.clamp( boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) y1_int = torch.clamp( boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) else: x0_int, y0_int = 0, 0 x1_int, y1_int = img_w, img_h x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 N = masks.shape[0] img_y = torch.arange( y0_int, y1_int, device=device, dtype=torch.float32) + 0.5 img_x = torch.arange( x0_int, x1_int, device=device, dtype=torch.float32) + 0.5 img_y = (img_y - y0) / (y1 - y0) * 2 - 1 img_x = (img_x - x0) / (x1 - x0) * 2 - 1 # img_x, img_y have shapes (N, w), (N, h) if torch.isinf(img_x).any(): inds = torch.where(torch.isinf(img_x)) img_x[inds] = 0 if torch.isinf(img_y).any(): inds = torch.where(torch.isinf(img_y)) img_y[inds] = 0 gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) grid = torch.stack([gx, gy], dim=3) if torch.onnx.is_in_onnx_export(): raise RuntimeError( 'Exporting F.grid_sample from Pytorch to ONNX is not supported.') img_masks = F.grid_sample( masks.to(dtype=torch.float32), grid, align_corners=False) if skip_empty: return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) else: return img_masks[:, 0], ()

insightface/detection/scrfd/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py/0

import logging import sys import torch from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes, merge_aug_masks, multiclass_nms) logger = logging.getLogger(__name__) if sys.version_info >= (3, 7): from mmdet.utils.contextmanagers import completed class BBoxTestMixin(object): if sys.version_info >= (3, 7): async def async_test_bboxes(self, x, img_metas, proposals, rcnn_test_cfg, rescale=False, bbox_semaphore=None, global_lock=None): """Asynchronized test for box head without augmentation.""" rois = bbox2roi(proposals) roi_feats = self.bbox_roi_extractor( x[:len(self.bbox_roi_extractor.featmap_strides)], rois) if self.with_shared_head: roi_feats = self.shared_head(roi_feats) sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017) async with completed( __name__, 'bbox_head_forward', sleep_interval=sleep_interval): cls_score, bbox_pred = self.bbox_head(roi_feats) img_shape = img_metas[0]['img_shape'] scale_factor = img_metas[0]['scale_factor'] det_bboxes, det_labels = self.bbox_head.get_bboxes( rois, cls_score, bbox_pred, img_shape, scale_factor, rescale=rescale, cfg=rcnn_test_cfg) return det_bboxes, det_labels def simple_test_bboxes(self, x, img_metas, proposals, rcnn_test_cfg, rescale=False): """Test only det bboxes without augmentation.""" rois = bbox2roi(proposals) bbox_results = self._bbox_forward(x, rois) img_shapes = tuple(meta['img_shape'] for meta in img_metas) scale_factors = tuple(meta['scale_factor'] for meta in img_metas) # split batch bbox prediction back to each image cls_score = bbox_results['cls_score'] bbox_pred = bbox_results['bbox_pred'] num_proposals_per_img = tuple(len(p) for p in proposals) rois = rois.split(num_proposals_per_img, 0) cls_score = cls_score.split(num_proposals_per_img, 0) # some detector with_reg is False, bbox_pred will be None if bbox_pred is not None: # the bbox prediction of some detectors like SABL is not Tensor if isinstance(bbox_pred, torch.Tensor): bbox_pred = bbox_pred.split(num_proposals_per_img, 0) else: bbox_pred = self.bbox_head.bbox_pred_split( bbox_pred, num_proposals_per_img) else: bbox_pred = (None, ) * len(proposals) # apply bbox post-processing to each image individually det_bboxes = [] det_labels = [] for i in range(len(proposals)): det_bbox, det_label = self.bbox_head.get_bboxes( rois[i], cls_score[i], bbox_pred[i], img_shapes[i], scale_factors[i], rescale=rescale, cfg=rcnn_test_cfg) det_bboxes.append(det_bbox) det_labels.append(det_label) return det_bboxes, det_labels def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg): """Test det bboxes with test time augmentation.""" aug_bboxes = [] aug_scores = [] for x, img_meta in zip(feats, img_metas): # only one image in the batch img_shape = img_meta[0]['img_shape'] scale_factor = img_meta[0]['scale_factor'] flip = img_meta[0]['flip'] flip_direction = img_meta[0]['flip_direction'] # TODO more flexible proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, scale_factor, flip, flip_direction) rois = bbox2roi([proposals]) bbox_results = self._bbox_forward(x, rois) bboxes, scores = self.bbox_head.get_bboxes( rois, bbox_results['cls_score'], bbox_results['bbox_pred'], img_shape, scale_factor, rescale=False, cfg=None) aug_bboxes.append(bboxes) aug_scores.append(scores) # after merging, bboxes will be rescaled to the original image size merged_bboxes, merged_scores = merge_aug_bboxes( aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, rcnn_test_cfg.score_thr, rcnn_test_cfg.nms, rcnn_test_cfg.max_per_img) return det_bboxes, det_labels class MaskTestMixin(object): if sys.version_info >= (3, 7): async def async_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False, mask_test_cfg=None): """Asynchronized test for mask head without augmentation.""" # image shape of the first image in the batch (only one) ori_shape = img_metas[0]['ori_shape'] scale_factor = img_metas[0]['scale_factor'] if det_bboxes.shape[0] == 0: segm_result = [[] for _ in range(self.mask_head.num_classes)] else: _bboxes = ( det_bboxes[:, :4] * scale_factor if rescale else det_bboxes) mask_rois = bbox2roi([_bboxes]) mask_feats = self.mask_roi_extractor( x[:len(self.mask_roi_extractor.featmap_strides)], mask_rois) if self.with_shared_head: mask_feats = self.shared_head(mask_feats) if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'): sleep_interval = mask_test_cfg['async_sleep_interval'] else: sleep_interval = 0.035 async with completed( __name__, 'mask_head_forward', sleep_interval=sleep_interval): mask_pred = self.mask_head(mask_feats) segm_result = self.mask_head.get_seg_masks( mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape, scale_factor, rescale) return segm_result def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False): """Simple test for mask head without augmentation.""" # image shapes of images in the batch ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) scale_factors = tuple(meta['scale_factor'] for meta in img_metas) num_imgs = len(det_bboxes) if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): segm_results = [[[] for _ in range(self.mask_head.num_classes)] for _ in range(num_imgs)] else: # if det_bboxes is rescaled to the original image size, we need to # rescale it back to the testing scale to obtain RoIs. if rescale and not isinstance(scale_factors[0], float): scale_factors = [ torch.from_numpy(scale_factor).to(det_bboxes[0].device) for scale_factor in scale_factors ] if torch.onnx.is_in_onnx_export(): # avoid mask_pred.split with static number of prediction mask_preds = [] _bboxes = [] for i, boxes in enumerate(det_bboxes): boxes = boxes[:, :4] if rescale: boxes *= scale_factors[i] _bboxes.append(boxes) img_inds = boxes[:, :1].clone() * 0 + i mask_rois = torch.cat([img_inds, boxes], dim=-1) mask_result = self._mask_forward(x, mask_rois) mask_preds.append(mask_result['mask_pred']) else: _bboxes = [ det_bboxes[i][:, :4] * scale_factors[i] if rescale else det_bboxes[i][:, :4] for i in range(len(det_bboxes)) ] mask_rois = bbox2roi(_bboxes) mask_results = self._mask_forward(x, mask_rois) mask_pred = mask_results['mask_pred'] # split batch mask prediction back to each image num_mask_roi_per_img = [ det_bbox.shape[0] for det_bbox in det_bboxes ] mask_preds = mask_pred.split(num_mask_roi_per_img, 0) # apply mask post-processing to each image individually segm_results = [] for i in range(num_imgs): if det_bboxes[i].shape[0] == 0: segm_results.append( [[] for _ in range(self.mask_head.num_classes)]) else: segm_result = self.mask_head.get_seg_masks( mask_preds[i], _bboxes[i], det_labels[i], self.test_cfg, ori_shapes[i], scale_factors[i], rescale) segm_results.append(segm_result) return segm_results def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): """Test for mask head with test time augmentation.""" if det_bboxes.shape[0] == 0: segm_result = [[] for _ in range(self.mask_head.num_classes)] else: aug_masks = [] for x, img_meta in zip(feats, img_metas): img_shape = img_meta[0]['img_shape'] scale_factor = img_meta[0]['scale_factor'] flip = img_meta[0]['flip'] flip_direction = img_meta[0]['flip_direction'] _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, scale_factor, flip, flip_direction) mask_rois = bbox2roi([_bboxes]) mask_results = self._mask_forward(x, mask_rois) # convert to numpy array to save memory aug_masks.append( mask_results['mask_pred'].sigmoid().cpu().numpy()) merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg) ori_shape = img_metas[0][0]['ori_shape'] segm_result = self.mask_head.get_seg_masks( merged_masks, det_bboxes, det_labels, self.test_cfg, ori_shape, scale_factor=1.0, rescale=False) return segm_result

insightface/detection/scrfd/mmdet/models/roi_heads/test_mixins.py/0

[pytest] addopts = --xdoctest --xdoctest-style=auto norecursedirs = .git ignore build __pycache__ data docker docs .eggs filterwarnings= default ignore:.*No cfgstr given in Cacher constructor or call.*:Warning ignore:.*Define the __nice__ method for.*:Warning

insightface/detection/scrfd/pytest.ini/0

import argparse import os from pathlib import Path import mmcv from mmcv import Config from mmdet.datasets.builder import build_dataset def parse_args(): parser = argparse.ArgumentParser(description='Browse a dataset') parser.add_argument('config', help='train config file path') parser.add_argument( '--skip-type', type=str, nargs='+', default=['DefaultFormatBundle', 'Normalize', 'Collect'], help='skip some useless pipeline') parser.add_argument( '--output-dir', default=None, type=str, help='If there is no display interface, you can save it') parser.add_argument('--not-show', default=False, action='store_true') parser.add_argument( '--show-interval', type=int, default=999, help='the interval of show (ms)') args = parser.parse_args() return args def retrieve_data_cfg(config_path, skip_type): cfg = Config.fromfile(config_path) train_data_cfg = cfg.data.train train_data_cfg['pipeline'] = [ x for x in train_data_cfg.pipeline if x['type'] not in skip_type ] return cfg def main(): args = parse_args() cfg = retrieve_data_cfg(args.config, args.skip_type) dataset = build_dataset(cfg.data.train) progress_bar = mmcv.ProgressBar(len(dataset)) for item in dataset: filename = os.path.join(args.output_dir, Path(item['filename']).name ) if args.output_dir is not None else None mmcv.imshow_det_bboxes( item['img'], item['gt_bboxes'], item['gt_labels'], class_names=dataset.CLASSES, show=not args.not_show, out_file=filename, wait_time=args.show_interval) progress_bar.update() if __name__ == '__main__': main()

insightface/detection/scrfd/tools/browse_dataset.py/0

#!/usr/bin/env python # -*- encoding: utf-8 -*- """ @Author : Qingping Zheng @Contact : qingpingzheng2014@gmail.com @File : dml_csr.py @Time : 10/01/21 00:00 PM @Desc : @License : Licensed under the Apache License, Version 2.0 (the "License"); @Copyright : Copyright 2015 The Authors. All Rights Reserved. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch.nn as nn from torch.nn import functional as F from inplace_abn import InPlaceABNSync from .modules.ddgcn import DDualGCNHead from .modules.parsing import Parsing from .modules.edges import Edges from .modules.util import Bottleneck def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class DML_CSR(nn.Module): def __init__(self, num_classes, abn=InPlaceABNSync, trained=True): super().__init__() self.inplanes = 128 self.is_trained = trained self.conv1 = conv3x3(3, 64, stride=2) self.bn1 = abn(64) self.relu1 = nn.ReLU(inplace=False) self.conv2 = conv3x3(64, 64) self.bn2 = abn(64) self.relu2 = nn.ReLU(inplace=False) self.conv3 = conv3x3(64, 128) self.bn3 = abn(128) self.relu3 = nn.ReLU(inplace=False) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layers = [3, 4, 23, 3] self.abn = abn strides = [1, 2, 1, 1] dilations = [1, 1, 1, 2] self.layer1 = self._make_layer(Bottleneck, 64, self.layers[0], stride=strides[0], dilation=dilations[0]) self.layer2 = self._make_layer(Bottleneck, 128, self.layers[1], stride=strides[1], dilation=dilations[1]) self.layer3 = self._make_layer(Bottleneck, 256, self.layers[2], stride=strides[2], dilation=dilations[2]) self.layer4 = self._make_layer(Bottleneck, 512, self.layers[3], stride=strides[3], dilation=dilations[3], multi_grid=(1,1,1)) # Context Aware self.context = DDualGCNHead(2048, 512, abn) self.layer6 = Parsing(512, 256, num_classes, abn) # edge if self.is_trained: self.edge_layer = Edges(abn, out_fea=num_classes) def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), self.abn(planes * block.expansion, affine=True)) layers = [] generate_multi_grid = lambda index, grids: grids[index%len(grids)] if isinstance(grids, tuple) else 1 layers.append(block(self.inplanes, planes, stride, abn=self.abn, dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid))) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes, abn=self.abn, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid))) return nn.Sequential(*layers) def forward(self, x): input = x x = self.relu1(self.bn1(self.conv1(x))) x = self.relu2(self.bn2(self.conv2(x))) x1 = self.relu3(self.bn3(self.conv3(x))) x = self.maxpool(x1) x2 = self.layer1(x) # 119 x 119 x3 = self.layer2(x2) # 60 x 60 x4 = self.layer3(x3) # 60 x 60 x5 = self.layer4(x4) # 60 x 60 x = self.context(x5) seg, x = self.layer6(x, x2) if self.is_trained: binary_edge, semantic_edge, edge_fea = self.edge_layer(x2,x3,x4) return seg, binary_edge, semantic_edge return seg

insightface/parsing/dml_csr/networks/dml_csr.py/0

from .face_analysis import * from .mask_renderer import *

insightface/python-package/insightface/app/__init__.py/0

/* functions that can not be optimazed by vertorization in python. 1. rasterization.(need process each triangle) 2. normal of each vertex.(use one-ring, need process each vertex) 3. write obj(seems that it can be verctorized? anyway, writing it in c++ is simple, so also add function here. --> however, why writting in c++ is still slow?) Author: Yao Feng Mail: yaofeng1995@gmail.com */ #include "mesh_core.h" /* Judge whether the point is in the triangle Method: http://blackpawn.com/texts/pointinpoly/ Args: point: [x, y] tri_points: three vertices(2d points) of a triangle. 2 coords x 3 vertices Returns: bool: true for in triangle */ bool isPointInTri(point p, point p0, point p1, point p2) { // vectors point v0, v1, v2; v0 = p2 - p0; v1 = p1 - p0; v2 = p - p0; // dot products float dot00 = v0.dot(v0); //v0.x * v0.x + v0.y * v0.y //np.dot(v0.T, v0) float dot01 = v0.dot(v1); //v0.x * v1.x + v0.y * v1.y //np.dot(v0.T, v1) float dot02 = v0.dot(v2); //v0.x * v2.x + v0.y * v2.y //np.dot(v0.T, v2) float dot11 = v1.dot(v1); //v1.x * v1.x + v1.y * v1.y //np.dot(v1.T, v1) float dot12 = v1.dot(v2); //v1.x * v2.x + v1.y * v2.y//np.dot(v1.T, v2) // barycentric coordinates float inverDeno; if(dot00*dot11 - dot01*dot01 == 0) inverDeno = 0; else inverDeno = 1/(dot00*dot11 - dot01*dot01); float u = (dot11*dot02 - dot01*dot12)*inverDeno; float v = (dot00*dot12 - dot01*dot02)*inverDeno; // check if point in triangle return (u >= 0) && (v >= 0) && (u + v < 1); } void get_point_weight(float* weight, point p, point p0, point p1, point p2) { // vectors point v0, v1, v2; v0 = p2 - p0; v1 = p1 - p0; v2 = p - p0; // dot products float dot00 = v0.dot(v0); //v0.x * v0.x + v0.y * v0.y //np.dot(v0.T, v0) float dot01 = v0.dot(v1); //v0.x * v1.x + v0.y * v1.y //np.dot(v0.T, v1) float dot02 = v0.dot(v2); //v0.x * v2.x + v0.y * v2.y //np.dot(v0.T, v2) float dot11 = v1.dot(v1); //v1.x * v1.x + v1.y * v1.y //np.dot(v1.T, v1) float dot12 = v1.dot(v2); //v1.x * v2.x + v1.y * v2.y//np.dot(v1.T, v2) // barycentric coordinates float inverDeno; if(dot00*dot11 - dot01*dot01 == 0) inverDeno = 0; else inverDeno = 1/(dot00*dot11 - dot01*dot01); float u = (dot11*dot02 - dot01*dot12)*inverDeno; float v = (dot00*dot12 - dot01*dot02)*inverDeno; // weight weight[0] = 1 - u - v; weight[1] = v; weight[2] = u; } void _get_normal_core( float* normal, float* tri_normal, int* triangles, int ntri) { int i, j; int tri_p0_ind, tri_p1_ind, tri_p2_ind; for(i = 0; i < ntri; i++) { tri_p0_ind = triangles[3*i]; tri_p1_ind = triangles[3*i + 1]; tri_p2_ind = triangles[3*i + 2]; for(j = 0; j < 3; j++) { normal[3*tri_p0_ind + j] = normal[3*tri_p0_ind + j] + tri_normal[3*i + j]; normal[3*tri_p1_ind + j] = normal[3*tri_p1_ind + j] + tri_normal[3*i + j]; normal[3*tri_p2_ind + j] = normal[3*tri_p2_ind + j] + tri_normal[3*i + j]; } } } void _rasterize_triangles_core( float* vertices, int* triangles, float* depth_buffer, int* triangle_buffer, float* barycentric_weight, int nver, int ntri, int h, int w) { int i; int x, y, k; int tri_p0_ind, tri_p1_ind, tri_p2_ind; point p0, p1, p2, p; int x_min, x_max, y_min, y_max; float p_depth, p0_depth, p1_depth, p2_depth; float weight[3]; for(i = 0; i < ntri; i++) { tri_p0_ind = triangles[3*i]; tri_p1_ind = triangles[3*i + 1]; tri_p2_ind = triangles[3*i + 2]; p0.x = vertices[3*tri_p0_ind]; p0.y = vertices[3*tri_p0_ind + 1]; p0_depth = vertices[3*tri_p0_ind + 2]; p1.x = vertices[3*tri_p1_ind]; p1.y = vertices[3*tri_p1_ind + 1]; p1_depth = vertices[3*tri_p1_ind + 2]; p2.x = vertices[3*tri_p2_ind]; p2.y = vertices[3*tri_p2_ind + 1]; p2_depth = vertices[3*tri_p2_ind + 2]; x_min = max((int)ceil(min(p0.x, min(p1.x, p2.x))), 0); x_max = min((int)floor(max(p0.x, max(p1.x, p2.x))), w - 1); y_min = max((int)ceil(min(p0.y, min(p1.y, p2.y))), 0); y_max = min((int)floor(max(p0.y, max(p1.y, p2.y))), h - 1); if(x_max < x_min || y_max < y_min) { continue; } for(y = y_min; y <= y_max; y++) //h { for(x = x_min; x <= x_max; x++) //w { p.x = x; p.y = y; if(p.x < 2 || p.x > w - 3 || p.y < 2 || p.y > h - 3 || isPointInTri(p, p0, p1, p2)) { get_point_weight(weight, p, p0, p1, p2); p_depth = weight[0]*p0_depth + weight[1]*p1_depth + weight[2]*p2_depth; if((p_depth > depth_buffer[y*w + x])) { depth_buffer[y*w + x] = p_depth; triangle_buffer[y*w + x] = i; for(k = 0; k < 3; k++) { barycentric_weight[y*w*3 + x*3 + k] = weight[k]; } } } } } } } void _render_colors_core( float* image, float* vertices, int* triangles, float* colors, float* depth_buffer, int nver, int ntri, int h, int w, int c) { int i; int x, y, k; int tri_p0_ind, tri_p1_ind, tri_p2_ind; point p0, p1, p2, p; int x_min, x_max, y_min, y_max; float p_depth, p0_depth, p1_depth, p2_depth; float p_color, p0_color, p1_color, p2_color; float weight[3]; for(i = 0; i < ntri; i++) { tri_p0_ind = triangles[3*i]; tri_p1_ind = triangles[3*i + 1]; tri_p2_ind = triangles[3*i + 2]; p0.x = vertices[3*tri_p0_ind]; p0.y = vertices[3*tri_p0_ind + 1]; p0_depth = vertices[3*tri_p0_ind + 2]; p1.x = vertices[3*tri_p1_ind]; p1.y = vertices[3*tri_p1_ind + 1]; p1_depth = vertices[3*tri_p1_ind + 2]; p2.x = vertices[3*tri_p2_ind]; p2.y = vertices[3*tri_p2_ind + 1]; p2_depth = vertices[3*tri_p2_ind + 2]; x_min = max((int)ceil(min(p0.x, min(p1.x, p2.x))), 0); x_max = min((int)floor(max(p0.x, max(p1.x, p2.x))), w - 1); y_min = max((int)ceil(min(p0.y, min(p1.y, p2.y))), 0); y_max = min((int)floor(max(p0.y, max(p1.y, p2.y))), h - 1); if(x_max < x_min || y_max < y_min) { continue; } for(y = y_min; y <= y_max; y++) //h { for(x = x_min; x <= x_max; x++) //w { p.x = x; p.y = y; if(p.x < 2 || p.x > w - 3 || p.y < 2 || p.y > h - 3 || isPointInTri(p, p0, p1, p2)) { get_point_weight(weight, p, p0, p1, p2); p_depth = weight[0]*p0_depth + weight[1]*p1_depth + weight[2]*p2_depth; if((p_depth > depth_buffer[y*w + x])) { for(k = 0; k < c; k++) // c { p0_color = colors[c*tri_p0_ind + k]; p1_color = colors[c*tri_p1_ind + k]; p2_color = colors[c*tri_p2_ind + k]; p_color = weight[0]*p0_color + weight[1]*p1_color + weight[2]*p2_color; image[y*w*c + x*c + k] = p_color; } depth_buffer[y*w + x] = p_depth; } } } } } } void _render_texture_core( float* image, float* vertices, int* triangles, float* texture, float* tex_coords, int* tex_triangles, float* depth_buffer, int nver, int tex_nver, int ntri, int h, int w, int c, int tex_h, int tex_w, int tex_c, int mapping_type) { int i; int x, y, k; int tri_p0_ind, tri_p1_ind, tri_p2_ind; int tex_tri_p0_ind, tex_tri_p1_ind, tex_tri_p2_ind; point p0, p1, p2, p; point tex_p0, tex_p1, tex_p2, tex_p; int x_min, x_max, y_min, y_max; float weight[3]; float p_depth, p0_depth, p1_depth, p2_depth; float xd, yd; float ul, ur, dl, dr; for(i = 0; i < ntri; i++) { // mesh tri_p0_ind = triangles[3*i]; tri_p1_ind = triangles[3*i + 1]; tri_p2_ind = triangles[3*i + 2]; p0.x = vertices[3*tri_p0_ind]; p0.y = vertices[3*tri_p0_ind + 1]; p0_depth = vertices[3*tri_p0_ind + 2]; p1.x = vertices[3*tri_p1_ind]; p1.y = vertices[3*tri_p1_ind + 1]; p1_depth = vertices[3*tri_p1_ind + 2]; p2.x = vertices[3*tri_p2_ind]; p2.y = vertices[3*tri_p2_ind + 1]; p2_depth = vertices[3*tri_p2_ind + 2]; // texture tex_tri_p0_ind = tex_triangles[3*i]; tex_tri_p1_ind = tex_triangles[3*i + 1]; tex_tri_p2_ind = tex_triangles[3*i + 2]; tex_p0.x = tex_coords[3*tex_tri_p0_ind]; tex_p0.y = tex_coords[3*tri_p0_ind + 1]; tex_p1.x = tex_coords[3*tex_tri_p1_ind]; tex_p1.y = tex_coords[3*tri_p1_ind + 1]; tex_p2.x = tex_coords[3*tex_tri_p2_ind]; tex_p2.y = tex_coords[3*tri_p2_ind + 1]; x_min = max((int)ceil(min(p0.x, min(p1.x, p2.x))), 0); x_max = min((int)floor(max(p0.x, max(p1.x, p2.x))), w - 1); y_min = max((int)ceil(min(p0.y, min(p1.y, p2.y))), 0); y_max = min((int)floor(max(p0.y, max(p1.y, p2.y))), h - 1); if(x_max < x_min || y_max < y_min) { continue; } for(y = y_min; y <= y_max; y++) //h { for(x = x_min; x <= x_max; x++) //w { p.x = x; p.y = y; if(p.x < 2 || p.x > w - 3 || p.y < 2 || p.y > h - 3 || isPointInTri(p, p0, p1, p2)) { get_point_weight(weight, p, p0, p1, p2); p_depth = weight[0]*p0_depth + weight[1]*p1_depth + weight[2]*p2_depth; if((p_depth > depth_buffer[y*w + x])) { // -- color from texture // cal weight in mesh tri get_point_weight(weight, p, p0, p1, p2); // cal coord in texture tex_p = tex_p0*weight[0] + tex_p1*weight[1] + tex_p2*weight[2]; tex_p.x = max(min(tex_p.x, float(tex_w - 1)), float(0)); tex_p.y = max(min(tex_p.y, float(tex_h - 1)), float(0)); yd = tex_p.y - floor(tex_p.y); xd = tex_p.x - floor(tex_p.x); for(k = 0; k < c; k++) { if(mapping_type==0)// nearest { image[y*w*c + x*c + k] = texture[int(round(tex_p.y))*tex_w*tex_c + int(round(tex_p.x))*tex_c + k]; } else//bilinear interp { ul = texture[(int)floor(tex_p.y)*tex_w*tex_c + (int)floor(tex_p.x)*tex_c + k]; ur = texture[(int)floor(tex_p.y)*tex_w*tex_c + (int)ceil(tex_p.x)*tex_c + k]; dl = texture[(int)ceil(tex_p.y)*tex_w*tex_c + (int)floor(tex_p.x)*tex_c + k]; dr = texture[(int)ceil(tex_p.y)*tex_w*tex_c + (int)ceil(tex_p.x)*tex_c + k]; image[y*w*c + x*c + k] = ul*(1-xd)*(1-yd) + ur*xd*(1-yd) + dl*(1-xd)*yd + dr*xd*yd; } } depth_buffer[y*w + x] = p_depth; } } } } } } // ------------------------------------------------- write // obj write // Ref: https://github.com/patrikhuber/eos/blob/master/include/eos/core/Mesh.hpp void _write_obj_with_colors_texture(string filename, string mtl_name, float* vertices, int* triangles, float* colors, float* uv_coords, int nver, int ntri, int ntexver) { int i; ofstream obj_file(filename.c_str()); // first line of the obj file: the mtl name obj_file << "mtllib " << mtl_name << endl; // write vertices for (i = 0; i < nver; ++i) { obj_file << "v " << vertices[3*i] << " " << vertices[3*i + 1] << " " << vertices[3*i + 2] << colors[3*i] << " " << colors[3*i + 1] << " " << colors[3*i + 2] << endl; } // write uv coordinates for (i = 0; i < ntexver; ++i) { //obj_file << "vt " << uv_coords[2*i] << " " << (1 - uv_coords[2*i + 1]) << endl; obj_file << "vt " << uv_coords[2*i] << " " << uv_coords[2*i + 1] << endl; } obj_file << "usemtl FaceTexture" << endl; // write triangles for (i = 0; i < ntri; ++i) { // obj_file << "f " << triangles[3*i] << "/" << triangles[3*i] << " " << triangles[3*i + 1] << "/" << triangles[3*i + 1] << " " << triangles[3*i + 2] << "/" << triangles[3*i + 2] << endl; obj_file << "f " << triangles[3*i + 2] << "/" << triangles[3*i + 2] << " " << triangles[3*i + 1] << "/" << triangles[3*i + 1] << " " << triangles[3*i] << "/" << triangles[3*i] << endl; } }

insightface/python-package/insightface/thirdparty/face3d/mesh/cython/mesh_core.cpp/0

''' Functions about transforming mesh(changing the position: modify vertices). 1. forward: transform(transform, camera, project). 2. backward: estimate transform matrix from correspondences. Preparation knowledge: transform&camera model: https://cs184.eecs.berkeley.edu/lecture/transforms-2 Part I: camera geometry and single view geometry in MVGCV ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import math from math import cos, sin def angle2matrix(angles): ''' get rotation matrix from three rotation angles(degree). right-handed. Args: angles: [3,]. x, y, z angles x: pitch. positive for looking down. y: yaw. positive for looking left. z: roll. positive for tilting head right. Returns: R: [3, 3]. rotation matrix. ''' x, y, z = np.deg2rad(angles[0]), np.deg2rad(angles[1]), np.deg2rad(angles[2]) # x Rx=np.array([[1, 0, 0], [0, cos(x), -sin(x)], [0, sin(x), cos(x)]]) # y Ry=np.array([[ cos(y), 0, sin(y)], [ 0, 1, 0], [-sin(y), 0, cos(y)]]) # z Rz=np.array([[cos(z), -sin(z), 0], [sin(z), cos(z), 0], [ 0, 0, 1]]) R=Rz.dot(Ry.dot(Rx)) return R.astype(np.float32) def angle2matrix_3ddfa(angles): ''' get rotation matrix from three rotation angles(radian). The same as in 3DDFA. Args: angles: [3,]. x, y, z angles x: pitch. y: yaw. z: roll. Returns: R: 3x3. rotation matrix. ''' # x, y, z = np.deg2rad(angles[0]), np.deg2rad(angles[1]), np.deg2rad(angles[2]) x, y, z = angles[0], angles[1], angles[2] # x Rx=np.array([[1, 0, 0], [0, cos(x), sin(x)], [0, -sin(x), cos(x)]]) # y Ry=np.array([[ cos(y), 0, -sin(y)], [ 0, 1, 0], [sin(y), 0, cos(y)]]) # z Rz=np.array([[cos(z), sin(z), 0], [-sin(z), cos(z), 0], [ 0, 0, 1]]) R = Rx.dot(Ry).dot(Rz) return R.astype(np.float32) ## ------------------------------------------ 1. transform(transform, project, camera). ## ---------- 3d-3d transform. Transform obj in world space def rotate(vertices, angles): ''' rotate vertices. X_new = R.dot(X). X: 3 x 1 Args: vertices: [nver, 3]. rx, ry, rz: degree angles rx: pitch. positive for looking down ry: yaw. positive for looking left rz: roll. positive for tilting head right Returns: rotated vertices: [nver, 3] ''' R = angle2matrix(angles) rotated_vertices = vertices.dot(R.T) return rotated_vertices def similarity_transform(vertices, s, R, t3d): ''' similarity transform. dof = 7. 3D: s*R.dot(X) + t Homo: M = [[sR, t],[0^T, 1]]. M.dot(X) Args:(float32) vertices: [nver, 3]. s: [1,]. scale factor. R: [3,3]. rotation matrix. t3d: [3,]. 3d translation vector. Returns: transformed vertices: [nver, 3] ''' t3d = np.squeeze(np.array(t3d, dtype = np.float32)) transformed_vertices = s * vertices.dot(R.T) + t3d[np.newaxis, :] return transformed_vertices ## -------------- Camera. from world space to camera space # Ref: https://cs184.eecs.berkeley.edu/lecture/transforms-2 def normalize(x): epsilon = 1e-12 norm = np.sqrt(np.sum(x**2, axis = 0)) norm = np.maximum(norm, epsilon) return x/norm def lookat_camera(vertices, eye, at = None, up = None): """ 'look at' transformation: from world space to camera space standard camera space: camera located at the origin. looking down negative z-axis. vertical vector is y-axis. Xcam = R(X - C) Homo: [[R, -RC], [0, 1]] Args: vertices: [nver, 3] eye: [3,] the XYZ world space position of the camera. at: [3,] a position along the center of the camera's gaze. up: [3,] up direction Returns: transformed_vertices: [nver, 3] """ if at is None: at = np.array([0, 0, 0], np.float32) if up is None: up = np.array([0, 1, 0], np.float32) eye = np.array(eye).astype(np.float32) at = np.array(at).astype(np.float32) z_aixs = -normalize(at - eye) # look forward x_aixs = normalize(np.cross(up, z_aixs)) # look right y_axis = np.cross(z_aixs, x_aixs) # look up R = np.stack((x_aixs, y_axis, z_aixs))#, axis = 0) # 3 x 3 transformed_vertices = vertices - eye # translation transformed_vertices = transformed_vertices.dot(R.T) # rotation return transformed_vertices ## --------- 3d-2d project. from camera space to image plane # generally, image plane only keeps x,y channels, here reserve z channel for calculating z-buffer. def orthographic_project(vertices): ''' scaled orthographic projection(just delete z) assumes: variations in depth over the object is small relative to the mean distance from camera to object x -> x*f/z, y -> x*f/z, z -> f. for point i,j. zi~=zj. so just delete z ** often used in face Homo: P = [[1,0,0,0], [0,1,0,0], [0,0,1,0]] Args: vertices: [nver, 3] Returns: projected_vertices: [nver, 3] if isKeepZ=True. [nver, 2] if isKeepZ=False. ''' return vertices.copy() def perspective_project(vertices, fovy, aspect_ratio = 1., near = 0.1, far = 1000.): ''' perspective projection. Args: vertices: [nver, 3] fovy: vertical angular field of view. degree. aspect_ratio : width / height of field of view near : depth of near clipping plane far : depth of far clipping plane Returns: projected_vertices: [nver, 3] ''' fovy = np.deg2rad(fovy) top = near*np.tan(fovy) bottom = -top right = top*aspect_ratio left = -right #-- homo P = np.array([[near/right, 0, 0, 0], [0, near/top, 0, 0], [0, 0, -(far+near)/(far-near), -2*far*near/(far-near)], [0, 0, -1, 0]]) vertices_homo = np.hstack((vertices, np.ones((vertices.shape[0], 1)))) # [nver, 4] projected_vertices = vertices_homo.dot(P.T) projected_vertices = projected_vertices/projected_vertices[:,3:] projected_vertices = projected_vertices[:,:3] projected_vertices[:,2] = -projected_vertices[:,2] #-- non homo. only fovy # projected_vertices = vertices.copy() # projected_vertices[:,0] = -(near/right)*vertices[:,0]/vertices[:,2] # projected_vertices[:,1] = -(near/top)*vertices[:,1]/vertices[:,2] return projected_vertices def to_image(vertices, h, w, is_perspective = False): ''' change vertices to image coord system 3d system: XYZ, center(0, 0, 0) 2d image: x(u), y(v). center(w/2, h/2), flip y-axis. Args: vertices: [nver, 3] h: height of the rendering w : width of the rendering Returns: projected_vertices: [nver, 3] ''' image_vertices = vertices.copy() if is_perspective: # if perspective, the projected vertices are normalized to [-1, 1]. so change it to image size first. image_vertices[:,0] = image_vertices[:,0]*w/2 image_vertices[:,1] = image_vertices[:,1]*h/2 # move to center of image image_vertices[:,0] = image_vertices[:,0] + w/2 image_vertices[:,1] = image_vertices[:,1] + h/2 # flip vertices along y-axis. image_vertices[:,1] = h - image_vertices[:,1] - 1 return image_vertices #### -------------------------------------------2. estimate transform matrix from correspondences. def estimate_affine_matrix_3d23d(X, Y): ''' Using least-squares solution Args: X: [n, 3]. 3d points(fixed) Y: [n, 3]. corresponding 3d points(moving). Y = PX Returns: P_Affine: (3, 4). Affine camera matrix (the third row is [0, 0, 0, 1]). ''' X_homo = np.hstack((X, np.ones([X.shape[1],1]))) #n x 4 P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4 return P def estimate_affine_matrix_3d22d(X, x): ''' Using Golden Standard Algorithm for estimating an affine camera matrix P from world to image correspondences. See Alg.7.2. in MVGCV Code Ref: https://github.com/patrikhuber/eos/blob/master/include/eos/fitting/affine_camera_estimation.hpp x_homo = X_homo.dot(P_Affine) Args: X: [n, 3]. corresponding 3d points(fixed) x: [n, 2]. n>=4. 2d points(moving). x = PX Returns: P_Affine: [3, 4]. Affine camera matrix ''' X = X.T; x = x.T assert(x.shape[1] == X.shape[1]) n = x.shape[1] assert(n >= 4) #--- 1. normalization # 2d points mean = np.mean(x, 1) # (2,) x = x - np.tile(mean[:, np.newaxis], [1, n]) average_norm = np.mean(np.sqrt(np.sum(x**2, 0))) scale = np.sqrt(2) / average_norm x = scale * x T = np.zeros((3,3), dtype = np.float32) T[0, 0] = T[1, 1] = scale T[:2, 2] = -mean*scale T[2, 2] = 1 # 3d points X_homo = np.vstack((X, np.ones((1, n)))) mean = np.mean(X, 1) # (3,) X = X - np.tile(mean[:, np.newaxis], [1, n]) m = X_homo[:3,:] - X average_norm = np.mean(np.sqrt(np.sum(X**2, 0))) scale = np.sqrt(3) / average_norm X = scale * X U = np.zeros((4,4), dtype = np.float32) U[0, 0] = U[1, 1] = U[2, 2] = scale U[:3, 3] = -mean*scale U[3, 3] = 1 # --- 2. equations A = np.zeros((n*2, 8), dtype = np.float32); X_homo = np.vstack((X, np.ones((1, n)))).T A[:n, :4] = X_homo A[n:, 4:] = X_homo b = np.reshape(x, [-1, 1]) # --- 3. solution p_8 = np.linalg.pinv(A).dot(b) P = np.zeros((3, 4), dtype = np.float32) P[0, :] = p_8[:4, 0] P[1, :] = p_8[4:, 0] P[-1, -1] = 1 # --- 4. denormalization P_Affine = np.linalg.inv(T).dot(P.dot(U)) return P_Affine def P2sRt(P): ''' decompositing camera matrix P Args: P: (3, 4). Affine Camera Matrix. Returns: s: scale factor. R: (3, 3). rotation matrix. t: (3,). translation. ''' t = P[:, 3] R1 = P[0:1, :3] R2 = P[1:2, :3] s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2.0 r1 = R1/np.linalg.norm(R1) r2 = R2/np.linalg.norm(R2) r3 = np.cross(r1, r2) R = np.concatenate((r1, r2, r3), 0) return s, R, t #Ref: https://www.learnopencv.com/rotation-matrix-to-euler-angles/ def isRotationMatrix(R): ''' checks if a matrix is a valid rotation matrix(whether orthogonal or not) ''' Rt = np.transpose(R) shouldBeIdentity = np.dot(Rt, R) I = np.identity(3, dtype = R.dtype) n = np.linalg.norm(I - shouldBeIdentity) return n < 1e-6 def matrix2angle(R): ''' get three Euler angles from Rotation Matrix Args: R: (3,3). rotation matrix Returns: x: pitch y: yaw z: roll ''' assert(isRotationMatrix) sy = math.sqrt(R[0,0] * R[0,0] + R[1,0] * R[1,0]) singular = sy < 1e-6 if not singular : x = math.atan2(R[2,1] , R[2,2]) y = math.atan2(-R[2,0], sy) z = math.atan2(R[1,0], R[0,0]) else : x = math.atan2(-R[1,2], R[1,1]) y = math.atan2(-R[2,0], sy) z = 0 # rx, ry, rz = np.rad2deg(x), np.rad2deg(y), np.rad2deg(z) rx, ry, rz = x*180/np.pi, y*180/np.pi, z*180/np.pi return rx, ry, rz # def matrix2angle(R): # ''' compute three Euler angles from a Rotation Matrix. Ref: http://www.gregslabaugh.net/publications/euler.pdf # Args: # R: (3,3). rotation matrix # Returns: # x: yaw # y: pitch # z: roll # ''' # # assert(isRotationMatrix(R)) # if R[2,0] !=1 or R[2,0] != -1: # x = math.asin(R[2,0]) # y = math.atan2(R[2,1]/cos(x), R[2,2]/cos(x)) # z = math.atan2(R[1,0]/cos(x), R[0,0]/cos(x)) # else:# Gimbal lock # z = 0 #can be anything # if R[2,0] == -1: # x = np.pi/2 # y = z + math.atan2(R[0,1], R[0,2]) # else: # x = -np.pi/2 # y = -z + math.atan2(-R[0,1], -R[0,2]) # return x, y, z

insightface/python-package/insightface/thirdparty/face3d/mesh_numpy/transform.py/0

# Face Recognition Datasets ## Training Datasets (Updating) ### CASIA-Webface (10K ids/0.5M images) [1] [Baidu](https://pan.baidu.com/s/1AfHdPsxJZBD8kBJeIhmq1w) [GDrive](https://drive.google.com/file/d/1KxNCrXzln0lal3N4JiYl9cFOIhT78y1l/view?usp=sharing) ### CelebA (10K ids/0.2M images) [2] ### UMDFace (8K ids/0.37M images) [3] [Baidu](https://pan.baidu.com/s/1aGutJwNWpV-lA0f_7eNsGQ) [GDrive](https://drive.google.com/file/d/1azhEHoJjVmifuzBVKJwl-sDbLZ-Wzp4O/view?usp=sharing) ### VGG2 (9K ids/3.31M images) [4] [Baidu](https://pan.baidu.com/s/1c3KeLzy) [GDrive](https://drive.google.com/file/d/1dyVQ7X3d28eAcjV3s3o0MT-HyODp_v3R/view?usp=sharing) ### MS1M-IBUG (85K ids/3.8M images) [5,6] [Baidu](https://pan.baidu.com/s/1nxmSCch) ### MS1M-ArcFace (85K ids/5.8M images) [5,7] [Baidu](https://pan.baidu.com/s/1S6LJZGdqcZRle1vlcMzHOQ) [GDrive](https://drive.google.com/file/d/1SXS4-Am3bsKSK615qbYdbA_FMVh3sAvR/view?usp=sharing) ### MS1M-RetinaFace [Baidu](https://pan.baidu.com/s/1RBnaW88PC6cKqtYwgfVX8Q) (code:8eb3) [GDrive](https://drive.google.com/file/d/1JgmzL9OLTqDAZE86pBgETtSQL4USKTFy/view?usp=sharing) ### Asian-Celeb (94K ids/2.8M images)[8] [Baidu](https://pan.baidu.com/s/12wSgofDy1flFf6lOyAxJRg) ### Glint360K (360K ids/17M images)[17] [Baidu](https://pan.baidu.com/s/1GsYqTTt7_Dn8BfxxsLFN0w) (code:o3az) magnet uri: `magnet:?xt=urn:btih:E5F46EE502B9E76DA8CC3A0E4F7C17E4000C7B1E&dn=glint360k` ### Glint-Mini (91K ids/5.2M images)[17] [Baidu](https://pan.baidu.com/s/10IzEyP-Z9dWFcxxj9jdJpQ) (code:10m5) ### DeepGlint (181K ids/6.75M images) [8] [baidu](https://pan.baidu.com/s/1yApUbklBgRgOyOV4o3J8Eg) ### WebFace260M [18] [Link](https://www.face-benchmark.org/download.html) ### IMDB-Face (59K ids/1.7M images) [9] ### Celeb500k (500K ids/50M images) [10] ### MegaFace(train) (672K ids/4.7M images) [11] [Baidu](https://pan.baidu.com/s/1uy366DjUiGc3AvhuamRLyw) (code:5f8m) [GDrive](https://drive.google.com/file/d/1O4FxijSXoEIe6fLfOocqF4VFMh5B4d89/view?usp=sharing) ### DigiFace-1M (110K ids/1.22M images) [19] [Website](https://microsoft.github.io/DigiFace1M/) [Github](https://github.com/microsoft/DigiFace1M) ## Validation Datasets ### CFP-FP (500 ids/7K images/7K pairs)[12] ### AgeDB-30 (570 ids/12,240 images/6K pairs)[13,6] ### LFW (5749 ids/13233 images/6K pairs)[14] ### CALFW (5749 ids/13233 images/6K pairs)[15] ### CPLFW (5749 ids/13233 images/6K pairs)[16] ## Image Test Datasets ### MegaFace testsuite: [GDrive](https://drive.google.com/file/d/1KBwp0U9oZgZj7SYDXRxUnnH7Lwvd9XMy/view?usp=sharing) ### IJB (IJB-B, IJB-C) testsuite: [GDrive](https://drive.google.com/file/d/1aC4zf2Bn0xCVH_ZtEuQipR2JvRb1bf8o/view?usp=sharing) ### TrillionPairs ### NIST ## Video Test Datasets ### YTF ### IQIYI ## Reference [1] Dong Yi, Zhen Lei, Shengcai Liao, Stan Z. Li. Learning Face Representation from Scratch. arXiv:1411.7923, 2014. [2] Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang. Deep Learning Face Attributes in the Wild, ICCV, 2015. [3] Bansal Ankan, Nanduri Anirudh, Castillo Carlos D, Ranjan Rajeev, Chellappa, Rama. UMDFaces: An Annotated Face Dataset for Training Deep Networks, arXiv:1611.01484v2, 2016. [4] Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi, Andrew Zisserman. VGGFace2: A dataset for recognising faces across pose and age. FG, 2018. [5] Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, Jianfeng Gao. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. ECCV, 2016. [6] Jiankang Deng, Yuxiang Zhou, Stefanos Zafeiriou. Marginal loss for deep face recognition, CVPRW, 2017. [7] Jiankang Deng, Jia Guo, Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition, arXiv:1801.07698, 2018. [8] [http://trillionpairs.deepglint.com/](http://trillionpairs.deepglint.com/) [9] Wang Fei, Chen Liren, Li Cheng, Huang Shiyao, Chen Yanjie, Qian Chen, Loy, Chen Change. The Devil of Face Recognition is in the Noise, ECCV, 2018. [10] Cao Jiajiong, Li Yingming, Zhang Zhongfei, Celeb-500K: A Large Training Dataset for Face Recognition, ICIP, 2018. [11] Nech Aaron, Kemelmacher-Shlizerman Ira, Level Playing Field For Million Scale Face Recognition, CVPR, 2017. [12] Sengupta Soumyadip, Chen Jun-Cheng, Castillo Carlos, Patel Vishal M, Chellappa Rama, Jacobs David W, Frontal to profile face verification in the wild, WACV, 2016. [13] Moschoglou, Stylianos and Papaioannou, Athanasios and Sagonas, Christos and Deng, Jiankang and Kotsia, Irene and Zafeiriou, Stefanos, Agedb: the first manually collected, in-the-wild age database, CVPRW, 2017. [14] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, 2007. [15] Zheng Tianyue, Deng Weihong, Hu Jiani, Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments, arXiv:1708.08197, 2017. [16] Zheng, Tianyue, and Weihong Deng. Cross-Pose LFW: A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments, 2018. [17] An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and Zhang, Debing and Fu Ying. Partial FC: Training 10 Million Identities on a Single Machine, arxiv:2010.05222, 2020. [18] Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou. WebFace260M: A Benchmark Unveiling the Power of Million-scale Deep Face Recognition [19] Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen. DigiFace-1M: 1 Million Digital Face Images for Face Recognition. WACV 2023

insightface/recognition/_datasets_/README.md/0

import cv2 import numpy as np from skimage import transform as trans src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007], [51.157, 89.050], [57.025, 89.702]], dtype=np.float32) #<--left src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111], [45.177, 86.190], [64.246, 86.758]], dtype=np.float32) #---frontal src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493], [42.463, 87.010], [69.537, 87.010]], dtype=np.float32) #-->right src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111], [48.167, 86.758], [67.236, 86.190]], dtype=np.float32) #-->right profile src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007], [55.388, 89.702], [61.257, 89.050]], dtype=np.float32) src = np.array([src1, src2, src3, src4, src5]) src_map = {112: src, 224: src * 2} arcface_src = np.array( [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32) arcface_src = np.expand_dims(arcface_src, axis=0) # In[66]: # lmk is prediction; src is template def estimate_norm(lmk, image_size=112, mode='arcface'): assert lmk.shape == (5, 2) tform = trans.SimilarityTransform() lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1) min_M = [] min_index = [] min_error = float('inf') if mode == 'arcface': assert image_size == 112 src = arcface_src else: src = src_map[image_size] for i in np.arange(src.shape[0]): tform.estimate(lmk, src[i]) M = tform.params[0:2, :] results = np.dot(M, lmk_tran.T) results = results.T error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1))) # print(error) if error < min_error: min_error = error min_M = M min_index = i return min_M, min_index def norm_crop(img, landmark, image_size=112, mode='arcface'): M, pose_index = estimate_norm(landmark, image_size, mode) warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) return warped

insightface/recognition/arcface_mxnet/common/face_align.py/0

import os import sys import struct import argparse from mxnet import recordio import oneflow.core.record.record_pb2 as of_record def parse_arguement(argv): parser = argparse.ArgumentParser() parser.add_argument( "--data_dir", type=str, default="insightface/datasets/faces_emore", help="Root directory to mxnet dataset.", ) parser.add_argument( "--output_filepath", type=str, default="./output", help="Path to output OFRecord.", ) return parser.parse_args(argv) def load_train_data(data_dir): path_imgrec = os.path.join(data_dir, "train.rec") path_imgidx = path_imgrec[0:-4] + ".idx" print( "Loading recordio {}\n\ Corresponding record idx is {}".format( path_imgrec, path_imgidx ) ) imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, "r", key_type=int) # Read header0 to get some info. identity_key_start = 0 identity_key_end = 0 imgidx_list = [] id2range = {} rec0 = imgrec.read_idx(0) header0, img_str = recordio.unpack(rec0) if header0.flag > 0: identity_key_start = int(header0.label[0]) identity_key_end = int(header0.label[1]) imgidx_list = range(1, identity_key_start) # Read identity id range for identity in range(identity_key_start, identity_key_end): rec = imgrec.read_idx(identity) header, s = recordio.unpack(rec) a, b = int(header.label[0]), int(header.label[1]) id2range[identity] = (a, b) else: imgidx_list = imgrec.keys return imgrec, imgidx_list def convert_to_ofrecord(img_data): """ Convert python dictionary formath data of one image to of.Example proto. Args: img_data: Python dict. Returns: example: The converted of.Exampl """ def _int32_feature(value): """Wrapper for inserting int32 features into Example proto.""" if not isinstance(value, list): value = [value] return of_record.Feature(int32_list=of_record.Int32List(value=value)) def _float_feature(value): """Wrapper for inserting float features into Example proto.""" if not isinstance(value, list): value = [value] return of_record.Feature(float_list=of_record.FloatList(value=value)) def _double_feature(value): """Wrapper for inserting float features into Example proto.""" if not isinstance(value, list): value = [value] return of_record.Feature(double_list=of_record.DoubleList(value=value)) def _bytes_feature(value): """Wrapper for inserting bytes features into Example proto.""" # if isinstance(value, six.string_types): # value = six.binary_type(value, encoding='utf-8') return of_record.Feature(bytes_list=of_record.BytesList(value=[value])) example = of_record.OFRecord( feature={ "label": _int32_feature(img_data["label"]), "encoded": _bytes_feature(img_data["pixel_data"]), } ) return example def main(args): # Convert recordio to ofrecord imgrec, imgidx_list = load_train_data(data_dir=args.data_dir) output_dir = os.path.join(args.output_filepath, "train") if not os.path.exists(output_dir): os.makedirs(output_dir) output_file = os.path.join(output_dir, "part-0") with open(output_file, "wb") as f: for idx in imgidx_list: if idx % 10000 == 0: print("Converting images: {} of {}".format(idx, len(imgidx_list))) img_data = {} rec = imgrec.read_idx(idx) header, s = recordio.unpack(rec) img_data["label"] = int(header.label[0]) img_data["pixel_data"] = s example = convert_to_ofrecord(img_data) print("shape", len(img_data["pixel_data"])) size = example.ByteSize() f.write(struct.pack("q", size)) f.write(example.SerializeToString()) if __name__ == "__main__": main(parse_arguement(sys.argv[1:]))

insightface/recognition/arcface_oneflow/tools/mx_recordio_2_ofrecord.py/0

# Copyright (c) 2021 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 time import os import numpy as np import sklearn import paddle import logging from typing import List from utils.verification import evaluate from datasets import load_bin @paddle.no_grad() def test(data_set, backbone, batch_size, fp16=False, nfolds=10): print('testing verification..') data_list = data_set[0] issame_list = data_set[1] embeddings_list = [] time_consumed = 0.0 for i in range(len(data_list)): data = data_list[i] embeddings = None ba = 0 while ba < data.shape[0]: bb = min(ba + batch_size, data.shape[0]) count = bb - ba _data = data[bb - batch_size:bb] # 将numpy转Tensor img = paddle.to_tensor( _data, dtype='float16' if fp16 else 'float32') net_out: paddle.Tensor = backbone(img) _embeddings = net_out.detach().cpu().numpy() if embeddings is None: embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] ba = bb embeddings_list.append(embeddings) _xnorm = 0.0 _xnorm_cnt = 0 for embed in embeddings_list: for i in range(embed.shape[0]): _em = embed[i] _norm = np.linalg.norm(_em) _xnorm += _norm _xnorm_cnt += 1 _xnorm /= _xnorm_cnt embeddings = embeddings_list[0].copy() try: embeddings = sklearn.preprocessing.normalize(embeddings) except: print(embeddings) acc1 = 0.0 std1 = 0.0 embeddings = embeddings_list[0] + embeddings_list[1] embeddings = sklearn.preprocessing.normalize(embeddings) _, _, accuracy, val, val_std, far = evaluate( embeddings, issame_list, nrof_folds=nfolds) acc2, std2 = np.mean(accuracy), np.std(accuracy) return acc1, std1, acc2, std2, _xnorm, embeddings_list class CallBackVerification(object): def __init__(self, frequent, rank, batch_size, val_targets, rec_prefix, fp16=False, image_size=(112, 112)): self.frequent: int = frequent self.rank: int = rank self.batch_size: int = batch_size self.fp16 = fp16 self.highest_acc_list: List[float] = [0.0] * len(val_targets) self.ver_list: List[object] = [] self.ver_name_list: List[str] = [] if self.rank == 0: self.init_dataset( val_targets=val_targets, data_dir=rec_prefix, image_size=image_size) def ver_test(self, backbone: paddle.nn.Layer, global_step: int): for i in range(len(self.ver_list)): test_start = time.time() acc1, std1, acc2, std2, xnorm, embeddings_list = test( self.ver_list[i], backbone, self.batch_size, fp16=self.fp16, nfolds=10) logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm)) logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2)) if acc2 > self.highest_acc_list[i]: self.highest_acc_list[i] = acc2 logging.info('[%s][%d]Accuracy-Highest: %1.5f' % ( self.ver_name_list[i], global_step, self.highest_acc_list[i])) test_end = time.time() logging.info("test time: {:.4f}".format(test_end - test_start)) def init_dataset(self, val_targets, data_dir, image_size): for name in val_targets: path = os.path.join(data_dir, name + ".bin") if os.path.exists(path): data_set = load_bin(path, image_size) self.ver_list.append(data_set) self.ver_name_list.append(name) def __call__(self, num_update, backbone: paddle.nn.Layer): if self.rank == 0 and num_update > 0 and num_update % self.frequent == 0: backbone.eval() with paddle.no_grad(): self.ver_test(backbone, num_update) backbone.train()

insightface/recognition/arcface_paddle/dynamic/utils/verification.py/0

from .iresnet import iresnet18, iresnet34, iresnet50, iresnet100, iresnet200 from .mobilefacenet import get_mbf def get_model(name, **kwargs): # resnet if name == "r18": return iresnet18(False, **kwargs) elif name == "r34": return iresnet34(False, **kwargs) elif name == "r50": return iresnet50(False, **kwargs) elif name == "r100": return iresnet100(False, **kwargs) elif name == "r200": return iresnet200(False, **kwargs) elif name == "r2060": from .iresnet2060 import iresnet2060 return iresnet2060(False, **kwargs) elif name == "mbf": fp16 = kwargs.get("fp16", False) num_features = kwargs.get("num_features", 512) return get_mbf(fp16=fp16, num_features=num_features) elif name == "mbf_large": from .mobilefacenet import get_mbf_large fp16 = kwargs.get("fp16", False) num_features = kwargs.get("num_features", 512) return get_mbf_large(fp16=fp16, num_features=num_features) elif name == "vit_t": num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=256, depth=12, num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1) elif name == "vit_t_dp005_mask0": # For WebFace42M num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=256, depth=12, num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.0) elif name == "vit_s": num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=12, num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1) elif name == "vit_s_dp005_mask_0": # For WebFace42M num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=12, num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.0) elif name == "vit_b": # this is a feature num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=24, num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1, using_checkpoint=True) elif name == "vit_b_dp005_mask_005": # For WebFace42M # this is a feature num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=24, num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.05, using_checkpoint=True) elif name == "vit_l_dp005_mask_005": # For WebFace42M # this is a feature num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=768, depth=24, num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.05, using_checkpoint=True) elif name == "vit_h": # For WebFace42M num_features = kwargs.get("num_features", 512) from .vit import VisionTransformer return VisionTransformer( img_size=112, patch_size=9, num_classes=num_features, embed_dim=1024, depth=48, num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0, using_checkpoint=True) else: raise ValueError()

insightface/recognition/arcface_torch/backbones/__init__.py/0

from torch.optim.lr_scheduler import _LRScheduler from torch.optim import SGD import torch import warnings class PolynomialLRWarmup(_LRScheduler): def __init__(self, optimizer, warmup_iters, total_iters=5, power=1.0, last_epoch=-1, verbose=False): super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose) self.total_iters = total_iters self.power = power self.warmup_iters = warmup_iters def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning) if self.last_epoch == 0 or self.last_epoch > self.total_iters: return [group["lr"] for group in self.optimizer.param_groups] if self.last_epoch <= self.warmup_iters: return [base_lr * self.last_epoch / self.warmup_iters for base_lr in self.base_lrs] else: l = self.last_epoch w = self.warmup_iters t = self.total_iters decay_factor = ((1.0 - (l - w) / (t - w)) / (1.0 - (l - 1 - w) / (t - w))) ** self.power return [group["lr"] * decay_factor for group in self.optimizer.param_groups] def _get_closed_form_lr(self): if self.last_epoch <= self.warmup_iters: return [ base_lr * self.last_epoch / self.warmup_iters for base_lr in self.base_lrs] else: return [ ( base_lr * (1.0 - (min(self.total_iters, self.last_epoch) - self.warmup_iters) / (self.total_iters - self.warmup_iters)) ** self.power ) for base_lr in self.base_lrs ] if __name__ == "__main__": class TestModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(32, 32) def forward(self, x): return self.linear(x) test_module = TestModule() test_module_pfc = TestModule() lr_pfc_weight = 1 / 3 base_lr = 10 total_steps = 1000 sgd = SGD([ {"params": test_module.parameters(), "lr": base_lr}, {"params": test_module_pfc.parameters(), "lr": base_lr * lr_pfc_weight} ], base_lr) scheduler = PolynomialLRWarmup(sgd, total_steps//10, total_steps, power=2) x = [] y = [] y_pfc = [] for i in range(total_steps): scheduler.step() lr = scheduler.get_last_lr()[0] lr_pfc = scheduler.get_last_lr()[1] x.append(i) y.append(lr) y_pfc.append(lr_pfc) import matplotlib.pyplot as plt fontsize=15 plt.figure(figsize=(6, 6)) plt.plot(x, y, linestyle='-', linewidth=2, ) plt.plot(x, y_pfc, linestyle='-', linewidth=2, ) plt.xlabel('Iterations') # x_label plt.ylabel("Lr") # y_label plt.savefig("tmp.png", dpi=600, bbox_inches='tight')

insightface/recognition/arcface_torch/lr_scheduler.py/0

from PIL import Image import os import torch.utils.data as data import torchvision.transforms as transforms class Real_Dataset_112(data.Dataset): def __init__(self, args): super(Real_Dataset_112, self).__init__() self.img_root = args.img_root_R self.img_list, self.num_classes = self.list_reader(args.train_list_R) self.input_mode = args.input_mode self.transform = transforms.Compose([ # transforms.RandomCrop(112), transforms.ToTensor() ]) def __getitem__(self, index): img_name, label = self.img_list[index] img = self.get_img_from_path(img_name) return {'img': img, 'label': int(label)} def __len__(self): return len(self.img_list) def get_img_from_path(self, img_name): img_path = os.path.join(self.img_root, img_name) if self.input_mode == 'grey': img = Image.open(img_path).convert('L') elif self.input_mode == 'red': img = Image.open(img_path) img = img.split()[0] img = self.transform(img) return img def list_reader(self, list_file): img_list = [] with open(list_file, 'r') as f: lines = f.readlines() pid_container = set() for line in lines: pid = int(line.strip().split(' ')[1]) pid_container.add(pid) pid2label = {pid:label for label, pid in enumerate(pid_container)} for line in lines: img_name, pid = line.strip().split(' ') if not os.path.exists(os.path.join(self.img_root, img_name)): continue label = pid2label[int(pid)] img_list.append((img_name, label)) return img_list, len(pid_container) class Real_Dataset_112_paired(data.Dataset): def __init__(self, args): super(Real_Dataset_112_paired, self).__init__() self.img_root = args.img_root_R self.img_list, self.num_classes = self.list_reader(args.train_list_R) self.input_mode = args.input_mode self.transform = transforms.Compose([ # transforms.RandomCrop(112), transforms.ToTensor() ]) self.vir_list = [(a,b,c) for (a,b,c) in self.img_list if c==0] self.nir_list = [(a,b,c) for (a,b,c) in self.img_list if c==1] self.vis_labels = np.array([p[1] for p in self.vir_list]) self.nir_labels = np.array([p[1] for p in self.nir_list]) self.visIndex = None self.nirIndex = None def __getitem__(self, index): vis_img_name, vis_label, vis_domain = self.vir_list[self.visIndex[index]] nir_img_name, nir_label, nir_domain = self.nir_list[self.nirIndex[index]] assert vis_domain == 0 and nir_domain == 1 vis_img = self.get_img_from_path(vis_img_name) nir_img = self.get_img_from_path(nir_img_name) return vis_img, nir_img, vis_label, nir_label def __len__(self): return len(self.img_list) def get_img_from_path(self, img_name): img_path = os.path.join(self.img_root, img_name) if self.input_mode == 'grey': img = Image.open(img_path).convert('L') elif self.input_mode == 'red': img = Image.open(img_path) img = img.split()[0] img = self.transform(img) return img def list_reader(self, list_file): img_list = [] with open(list_file, 'r') as f: lines = f.readlines() pid_container = set() for line in lines: pid = int(line.strip().split(' ')[1]) pid_container.add(pid) pid2label = {pid:label for label, pid in enumerate(pid_container)} for line in lines: img_name, pid = line.strip().split(' ') label = pid2label[int(pid)] domain = 0 if 'VIS' in img_name else 1 img_list.append((img_name, label, domain)) return img_list, len(pid_container) class Mix_Dataset_112(data.Dataset): def __init__(self, args): super(Mix_Dataset_112, self).__init__() self.img_root_R = args.img_root_R self.img_root_F = args.img_root_F self.img_list, self.num_classes = self.list_reader(args.train_list_R, args.train_list_F) self.input_mode = args.input_mode self.transform = transforms.Compose([ # transforms.RandomCrop(112), transforms.ToTensor() ]) def __getitem__(self, index): img_path, label = self.img_list[index] img = self.get_img_from_path(img_path) return {'img': img, 'label': int(label)} def __len__(self): return len(self.img_list) def get_img_from_path(self, img_path): if self.input_mode == 'grey': img = Image.open(img_path).convert('L') elif self.input_mode == 'red': img = Image.open(img_path) img = img.split()[0] img = self.transform(img) return img def list_reader(self, list_file_real, list_file_fake): with open(list_file_real, 'r') as f: lines_real = f.readlines() with open(list_file_fake, 'r') as f: lines_fake = f.readlines() fake_label_start = max([int(l.strip().split(' ')[-1]) for l in lines_real]) + 1 lines_fake = ["{} {}".format(l.strip().split(' ')[0], int(l.strip().split(' ')[1]) + fake_label_start) for l in lines_fake] lines = lines_real + lines_fake pid_container = set() for line in lines: pid = int(line.strip().split(' ')[1]) pid_container.add(pid) pid2label = {pid:label for label, pid in enumerate(pid_container)} img_list_R = [] for line in lines_real: img_name, pid = line.strip().split(' ') label = pid2label[int(pid)] img_list_R.append((os.path.join(self.img_root_R + img_name), label)) img_list_F = [] for line in lines_fake: img_name, pid = line.strip().split(' ') label = pid2label[int(pid)] img_list_F.append((os.path.join(self.img_root_F + img_name), label)) img_list = img_list_R + img_list_F return img_list, len(pid_container) class Mix_Dataset_112_paired(data.Dataset): def __init__(self, args): super(Mix_Dataset_112_paired, self).__init__() self.img_root_R = args.img_root_R self.img_root_F = args.img_root_F self.img_list, self.num_classes = self.list_reader(args.train_list_R, args.train_list_F) self.input_mode = args.input_mode self.transform = transforms.Compose([ # transforms.RandomCrop(112), transforms.ToTensor() ]) self.vir_list = [(a,b,c) for (a,b,c) in self.img_list if c==0] self.nir_list = [(a,b,c) for (a,b,c) in self.img_list if c==1] self.vis_labels = np.array([p[1] for p in self.vir_list]) self.nir_labels = np.array([p[1] for p in self.nir_list]) self.visIndex = None self.nirIndex = None def __getitem__(self, index): vis_img_name, vis_label, vis_domain = self.vir_list[self.visIndex[index]] nir_img_name, nir_label, nir_domain = self.nir_list[self.nirIndex[index]] assert vis_domain == 0 and nir_domain == 1 vis_img = self.get_img_from_path(vis_img_name) nir_img = self.get_img_from_path(nir_img_name) return vis_img, nir_img, vis_label, nir_label def __len__(self): return len(self.img_list) def get_img_from_path(self, img_path): if self.input_mode == 'grey': img = Image.open(img_path).convert('L') elif self.input_mode == 'red': img = Image.open(img_path) img = img.split()[0] img = self.transform(img) return img def list_reader(self, list_file_real, list_file_fake): with open(list_file_real, 'r') as f: lines_real = f.readlines() with open(list_file_fake, 'r') as f: lines_fake = f.readlines() fake_label_start = max([int(l.strip().split(' ')[-1]) for l in lines_real]) + 1 lines_fake = ["{} {}".format(l.strip().split(' ')[0], int(l.strip().split(' ')[1]) + fake_label_start) for l in lines_fake] lines = lines_real + lines_fake pid_container = set() for line in lines: pid = int(line.strip().split(' ')[1]) pid_container.add(pid) pid2label = {pid:label for label, pid in enumerate(pid_container)} img_list_R = [] for line in lines_real: img_name, pid = line.strip().split(' ') label = pid2label[int(pid)] domain = 0 if 'VIS' in img_name else 1 img_list_R.append((os.path.join(self.img_root_R + img_name), label, domain)) img_list_F = [] for line in lines_fake: img_name, pid = line.strip().split(' ') label = pid2label[int(pid)] # if label in [8192,1984,2110,6344,8566,8589,9362]: # only with single image pair # print(img_name) domain = 0 if 'VIS' in img_name else 1 img_list_F.append((os.path.join(self.img_root_F + img_name), label, domain)) img_list = img_list_R + img_list_F return img_list, len(pid_container) from torch.utils.data.sampler import Sampler import numpy as np def GenIdx(train_vis_label, train_nir_label): def get_idx_from_label(train_label): pos = [] unique_train_label = np.unique(train_label) for ul in unique_train_label: tmp = np.argwhere(train_label == ul).squeeze().tolist() if isinstance(tmp,int): tmp = [tmp] pos.append(tmp) return pos vis_pos = get_idx_from_label(train_vis_label) nir_pos = get_idx_from_label(train_nir_label) return vis_pos, nir_pos class IdentitySampler(Sampler): """Sample person identities evenly in each batch. Args: train_color_label, train_thermal_label: labels of two modalities color_pos, thermal_pos: positions of each identity batchSize: batch size """ def __init__(self, train_color_label, train_thermal_label, color_pos, thermal_pos, batchSize, num_img_per_id = 4): uni_label = np.unique(train_color_label) self.n_classes = len(uni_label) sample_color = np.arange(batchSize) sample_thermal = np.arange(batchSize) N = np.maximum(len(train_color_label), len(train_thermal_label)) num_id_per_batch = batchSize / num_img_per_id for j in range(N//batchSize+1): batch_idx = np.random.choice(uni_label, int(num_id_per_batch), replace=False) for s, i in enumerate(range(0, batchSize, num_img_per_id)): sample_flag = True if len(color_pos[batch_idx[s]]) < num_img_per_id or len(thermal_pos[batch_idx[s]]) < num_img_per_id else False sample_color[i:i+num_img_per_id] = np.random.choice(color_pos[batch_idx[s]], num_img_per_id, replace=sample_flag) sample_thermal[i:i+num_img_per_id] = np.random.choice(thermal_pos[batch_idx[s]], num_img_per_id, replace=sample_flag) if j ==0: index1= sample_color index2= sample_thermal else: index1 = np.hstack((index1, sample_color)) index2 = np.hstack((index2, sample_thermal)) self.visIndex = index1 self.nirIndex = index2 self.N = N def __iter__(self): return iter(np.arange(len(self.visIndex))) def __len__(self): return self.N

insightface/recognition/idmmd/dataset_mix.py/0

import os import numpy as np import torch import torch.nn.functional as F def ort_loss(x, y): loss = torch.abs((x * y).sum(dim=1)).sum() loss = loss / float(x.size(0)) return loss def ang_loss(x, y): loss = (x * y).sum(dim=1).sum() loss = loss / float(x.size(0)) return loss def MMD_Loss(fc_nir, fc_vis): mean_fc_nir = torch.mean(fc_nir, 0) mean_fc_vis = torch.mean(fc_vis, 0) loss_mmd = F.mse_loss(mean_fc_nir, mean_fc_vis) return loss_mmd def rgb2gray(img): r, g, b = torch.split(img, 1, dim=1) return torch.mul(r, 0.299) + torch.mul(g, 0.587) + torch.mul(b, 0.114) def save_checkpoint(model, epoch, name="", dataset=''): if not os.path.exists("model/{}/".format(dataset)): os.makedirs("model/{}/".format(dataset)) model_path = "model/{}/".format(dataset) + name + "_e{}.pth.tar".format(epoch) state = {"epoch": epoch, "state_dict": model.state_dict()} torch.save(state, model_path) print("checkpoint saved to {}".format(model_path)) def load_model(model, pretrained): weights = torch.load(pretrained) pretrained_dict = weights["state_dict"] model_dict = model.state_dict() # print("to here") # print(model_dict.keys()) # print('\n') # print(pretrained_dict.keys()) # import pdb;pdb.set_trace() if 'LightCNN' in pretrained: tmp = [k for k in pretrained_dict] if "module." in tmp[0]: pretrained_dict = {k.replace('module.',''): v for k, v in pretrained_dict.items() if k.replace('module.','') in model_dict} else: pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and k!='module.weight'} print("len of params to be loaded: ",len(pretrained_dict)) model.load_state_dict(pretrained_dict, strict=False) return weights['epoch'] def load_model_train_lightcnn(model, pretrained): weights = torch.load(pretrained) pretrained_dict = weights["state_dict"] model_dict = model.state_dict() # print("to here") # print(model_dict.keys()) # print('\n') # print(pretrained_dict.keys()) # import pdb;pdb.set_trace() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and 'module.weight' not in k} print("len of params to be loaded: ",len(pretrained_dict)) model.load_state_dict(pretrained_dict, strict=False) return weights['epoch'] def set_requires_grad(nets, requires_grad=False): if not isinstance(nets, list): nets = [nets] for net in nets: if net is not None: for param in net.parameters(): param.requires_grad = requires_grad # assign adain_params to AdaIN layers def assign_adain_params(adain_params, model): for m in model.modules(): if m.__class__.__name__ == "AdaptiveInstanceNorm2d": mean = adain_params[:, :m.num_features] std = adain_params[:, m.num_features:2*m.num_features] m.bias = mean.contiguous().view(-1) m.weight = std.contiguous().view(-1) if adain_params.size(1) > 2*m.num_features: adain_params = adain_params[:, 2*m.num_features:] def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.unsqueeze(0).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / batch_size)) return res def adjust_learning_rate(lr, step, optimizer, epoch): scale = 0.457305051927326 lr = lr * (scale ** (epoch // step)) print('lr: {}'.format(lr)) if (epoch != 0) & (epoch % step == 0): print('Change lr') for param_group in optimizer.param_groups: param_group['lr'] = param_group['lr'] * scale class AverageMeter(object): def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count

insightface/recognition/idmmd/utils.py/0

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import random import logging import sys import numbers import math import datetime import numpy as np import cv2 import mxnet as mx from mxnet import ndarray as nd from mxnet import io from mxnet import recordio logger = logging.getLogger() class FaceImageIter(io.DataIter): def __init__(self, batch_size, data_shape, path_imgrec=None, shuffle=False, aug_list=None, mean=None, rand_mirror=False, cutoff=0, color_jittering=0, images_filter=0, data_name='data', label_name='softmax_label', context=0, context_num=1, **kwargs): super(FaceImageIter, self).__init__() assert path_imgrec self.context = context self.context_num = context_num if path_imgrec: logging.info('loading recordio %s...', path_imgrec) path_imgidx = path_imgrec[0:-4] + ".idx" self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') s = self.imgrec.read_idx(0) header, _ = recordio.unpack(s) if header.flag > 0: self.header0 = (int(header.label[0]), int(header.label[1])) self.imgidx = [] self.id2range = {} self.seq_identity = range(int(header.label[0]), int(header.label[1])) for identity in self.seq_identity: s = self.imgrec.read_idx(identity) header, _ = recordio.unpack(s) a, b = int(header.label[0]), int(header.label[1]) count = b - a if count < images_filter: continue self.id2range[identity] = (a, b) self.imgidx += range(a, b) self_data_lenth = len(self.imgidx) else: self.imgidx = list(self.imgrec.keys) if shuffle: self.seq = self.imgidx self.oseq = self.imgidx else: self.seq = None self.mean = mean self.nd_mean = None self.epoch = 0 if self.mean: self.mean = np.array(self.mean, dtype=np.float32).reshape(1, 1, 3) self.nd_mean = mx.nd.array(self.mean).reshape((1, 1, 3)) self.check_data_shape(data_shape) self.provide_data = [(data_name, (batch_size, ) + data_shape)] self.batch_size = batch_size self.data_shape = data_shape self.shuffle = shuffle self.image_size = '%d,%d' % (data_shape[1], data_shape[2]) self.rand_mirror = rand_mirror self.cutoff = cutoff self.color_jittering = color_jittering self.CJA = mx.image.ColorJitterAug(0.125, 0.125, 0.125) self.provide_label = [(label_name, (batch_size, ))] self.cur = 0 self.nbatch = 0 self.is_init = False self.num_samples_per_gpu = int( math.floor(len(self.seq) * 1.0 / self.context_num)) def reset(self): """Resets the iterator to the beginning of the data.""" self.epoch += 1 self.cur = 0 if self.shuffle: random.seed(self.epoch) random.shuffle(self.seq) if self.seq is None and self.imgrec is not None: self.imgrec.reset() def num_samples(self): return len(self.seq) def next_sample(self): if self.seq is not None: while True: if self.cur >= self.num_samples_per_gpu: raise StopIteration idx = self.seq[self.num_samples_per_gpu * self.context + self.cur] self.cur += 1 if self.imgrec is not None: s = self.imgrec.read_idx(idx) header, img = recordio.unpack(s) label = header.label if not isinstance(label, numbers.Number): label = label[0] return int(label), img, None, None else: label, fname, bbox, landmark = self.imglist[idx] return label, self.read_image(fname), bbox, landmark else: s = self.imgrec.read() if s is None: raise StopIteration header, img = recordio.unpack(s) return header.label, img, None, None def brightness_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) src *= alpha return src def contrast_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) coef = nd.array([[[0.299, 0.587, 0.114]]]) gray = src * coef gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray) src *= alpha src += gray return src def saturation_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) coef = nd.array([[[0.299, 0.587, 0.114]]]) gray = src * coef gray = nd.sum(gray, axis=2, keepdims=True) gray *= (1.0 - alpha) src *= alpha src += gray return src def color_aug(self, img, x): return self.CJA(img) def mirror_aug(self, img): _rd = random.randint(0, 1) if _rd == 1: for c in range(img.shape[2]): img[:, :, c] = np.fliplr(img[:, :, c]) return img def compress_aug(self, img): from PIL import Image from io import BytesIO buf = BytesIO() img = Image.fromarray(img.asnumpy(), 'RGB') q = random.randint(2, 20) img.save(buf, format='JPEG', quality=q) buf = buf.getvalue() img = Image.open(BytesIO(buf)) return nd.array(np.asarray(img, 'float32')) def next(self): if not self.is_init: self.reset() self.is_init = True """Returns the next batch of data.""" # print('in next', self.cur, self.labelcur) self.nbatch += 1 batch_size = self.batch_size c, h, w = self.data_shape batch_data = nd.empty((batch_size, c, h, w)) if self.provide_label is not None: batch_label = nd.empty(self.provide_label[0][1]) i = 0 try: while i < batch_size: label, s, bbox, landmark = self.next_sample() _data = self.imdecode(s) if _data.shape[0] != self.data_shape[1]: _data = mx.image.resize_short(_data, self.data_shape[1]) if self.rand_mirror: _rd = random.randint(0, 1) if _rd == 1: _data = mx.ndarray.flip(data=_data, axis=1) if self.color_jittering > 0: if self.color_jittering > 1: _rd = random.randint(0, 1) if _rd == 1: _data = self.compress_aug(_data) # print('do color aug') _data = _data.astype('float32', copy=False) # print(_data.__class__) _data = self.color_aug(_data, 0.125) if self.nd_mean is not None: _data = _data.astype('float32', copy=False) _data -= self.nd_mean _data *= 0.0078125 if self.cutoff > 0: _rd = random.randint(0, 1) if _rd == 1: # print('do cutoff aug', self.cutoff) centerh = random.randint(0, _data.shape[0] - 1) centerw = random.randint(0, _data.shape[1] - 1) half = self.cutoff // 2 starth = max(0, centerh - half) endh = min(_data.shape[0], centerh + half) startw = max(0, centerw - half) endw = min(_data.shape[1], centerw + half) # print(starth, endh, startw, endw, _data.shape) _data[starth:endh, startw:endw, :] = 128 data = [_data] try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue # print('aa',data[0].shape) # data = self.augmentation_transform(data) # print('bb',data[0].shape) for datum in data: assert i < batch_size, 'Batch size must be multiples of augmenter output length' # print(datum.shape) batch_data[i][:] = self.postprocess_data(datum) batch_label[i][:] = label i += 1 except StopIteration: if i < batch_size: raise StopIteration return io.DataBatch([batch_data], [batch_label], batch_size - i) def check_data_shape(self, data_shape): """Checks if the input data shape is valid""" if not len(data_shape) == 3: raise ValueError( 'data_shape should have length 3, with dimensions CxHxW') if not data_shape[0] == 3: raise ValueError( 'This iterator expects inputs to have 3 channels.') def check_valid_image(self, data): """Checks if the input data is valid""" if len(data[0].shape) == 0: raise RuntimeError('Data shape is wrong') def imdecode(self, s): """Decodes a string or byte string to an NDArray. See mx.img.imdecode for more details.""" img = mx.image.imdecode(s) # mx.ndarray return img def read_image(self, fname): """Reads an input image `fname` and returns the decoded raw bytes. Example usage: ---------- >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. """ with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read() return img def augmentation_transform(self, data): """Transforms input data with specified augmentation.""" for aug in self.auglist: data = [ret for src in data for ret in aug(src)] return data def postprocess_data(self, datum): """Final postprocessing step before image is loaded into the batch.""" return nd.transpose(datum, axes=(2, 0, 1)) class FaceImageIterList(io.DataIter): def __init__(self, iter_list): assert len(iter_list) > 0 self.provide_data = iter_list[0].provide_data self.provide_label = iter_list[0].provide_label self.iter_list = iter_list self.cur_iter = None def reset(self): self.cur_iter.reset() def next(self): self.cur_iter = random.choice(self.iter_list) while True: try: ret = self.cur_iter.next() except StopIteration: self.cur_iter.reset() continue return ret # dummy class DummyIter(mx.io.DataIter): def __init__(self, batch_size, data_shape, batches=1000, mode='', dtype='float32'): super(DummyIter, self).__init__(batch_size) self.data_shape = (batch_size, ) + data_shape self.label_shape = (batch_size, ) self.provide_data = [('data', self.data_shape)] self.provide_label = [('softmax_label', self.label_shape)] # self.provide_label = [('label', self.label_shape)] # if mode == 'perseus': # self.provide_label = [] self.batch = mx.io.DataBatch( data=[mx.nd.zeros(self.data_shape, dtype=dtype)], label=[mx.nd.zeros(self.label_shape, dtype=dtype)]) self._batches = 0 self.batches = batches def next(self): if self._batches < self.batches: self._batches += 1 return self.batch else: self._batches = 0 raise StopIteration

insightface/recognition/partial_fc/mxnet/image_iter.py/0

import argparse import logging import os import sys import time import horovod.mxnet as hvd import mxnet as mx import default from callbacks import CallBackModelSave, CallBackLogging, CallBackCenterSave, CallBackVertification from default import config from image_iter import FaceImageIter, DummyIter from memory_module import SampleDistributeModule from memory_bank import MemoryBank from memory_scheduler import get_scheduler from memory_softmax import MarginLoss from optimizer import MemoryBankSGDOptimizer from symbol import resnet sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol')) os.environ['MXNET_BACKWARD_DO_MIRROR'] = '0' os.environ['MXNET_UPDATE_ON_KVSTORE'] = "0" os.environ['MXNET_EXEC_ENABLE_ADDTO'] = "1" os.environ['MXNET_USE_TENSORRT'] = "0" os.environ['MXNET_GPU_WORKER_NTHREADS'] = "2" os.environ['MXNET_GPU_COPY_NTHREADS'] = "1" os.environ['MXNET_OPTIMIZER_AGGREGATION_SIZE'] = "54" os.environ['HOROVOD_CYCLE_TIME'] = "0.1" os.environ['HOROVOD_FUSION_THRESHOLD'] = "67108864" os.environ['HOROVOD_NUM_NCCL_STREAMS'] = "2" os.environ['MXNET_HOROVOD_NUM_GROUPS'] = "16" os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD'] = "999" os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD'] = "25" def parse_args(): parser = argparse.ArgumentParser(description='Train parall face network') # general parser.add_argument('--dataset', default='emore', help='dataset config') parser.add_argument('--network', default='r100', help='network config') parser.add_argument('--loss', default='cosface', help='loss config') args, rest = parser.parse_known_args() default.generate_config(args.loss, args.dataset, args.network) parser.add_argument('--models-root', default="./test", help='root directory to save model.') args = parser.parse_args() return args def set_logger(logger, rank, models_root): formatter = logging.Formatter("rank-id:" + str(rank) + ":%(asctime)s-%(message)s") file_handler = logging.FileHandler( os.path.join(models_root, "%d_hist.log" % rank)) stream_handler = logging.StreamHandler(sys.stdout) file_handler.setFormatter(formatter) stream_handler.setFormatter(formatter) logger.addHandler(file_handler) logger.addHandler(stream_handler) logger.info('rank_id: %d' % rank) def get_symbol_embedding(): embedding = eval(config.net_name).get_symbol() all_label = mx.symbol.Variable('softmax_label') all_label = mx.symbol.BlockGrad(all_label) out_list = [embedding, all_label] out = mx.symbol.Group(out_list) return out, embedding def train_net(): args = parse_args() hvd.init() # Size is the number of total GPU, rank is the unique process(GPU) ID from 0 to size, # local_rank is the unique process(GPU) ID within this server rank = hvd.rank() local_rank = hvd.local_rank() size = hvd.size() prefix = os.path.join(args.models_root, 'model') prefix_dir = os.path.dirname(prefix) if not os.path.exists(prefix_dir) and not local_rank: os.makedirs(prefix_dir) else: time.sleep(2) logger = logging.getLogger() logger.setLevel(logging.INFO) set_logger(logger, rank, prefix_dir) data_shape = (3, config.image_size, config.image_size) # We equally store the class centers (softmax linear transformation matrix) on all GPUs in order. num_local = (config.num_classes + size - 1) // size num_sample = int(num_local * config.sample_ratio) memory_bank = MemoryBank( num_sample=num_sample, num_local=num_local, rank=rank, local_rank=local_rank, embedding_size=config.embedding_size, prefix=prefix_dir, gpu=True) if config.debug: train_iter = DummyIter(config.batch_size, data_shape, 1000 * 10000) else: train_iter = FaceImageIter( batch_size=config.batch_size, data_shape=data_shape, path_imgrec=config.rec, shuffle=True, rand_mirror=True, context=rank, context_num=size) train_data_iter = mx.io.PrefetchingIter(train_iter) esym, save_symbol = get_symbol_embedding() margins = (config.loss_m1, config.loss_m2, config.loss_m3) fc7_model = MarginLoss(margins, config.loss_s, config.embedding_size) # optimizer # backbone lr_scheduler & optimizer backbone_lr_scheduler, memory_bank_lr_scheduler = get_scheduler() backbone_kwargs = { 'learning_rate': config.backbone_lr, 'momentum': 0.9, 'wd': 5e-4, 'rescale_grad': 1.0 / (config.batch_size * size) * size, 'multi_precision': config.fp16, 'lr_scheduler': backbone_lr_scheduler, } # memory_bank lr_scheduler & optimizer memory_bank_optimizer = MemoryBankSGDOptimizer( lr_scheduler=memory_bank_lr_scheduler, rescale_grad=1.0 / config.batch_size / size, ) # train_module = SampleDistributeModule( symbol=esym, fc7_model=fc7_model, memory_bank=memory_bank, memory_optimizer=memory_bank_optimizer) # if not config.debug and local_rank == 0: cb_vert = CallBackVertification(esym, train_module) cb_speed = CallBackLogging(rank, size, prefix_dir) cb_save = CallBackModelSave(save_symbol, train_module, prefix, rank) cb_center_save = CallBackCenterSave(memory_bank) def call_back_fn(params): cb_speed(params) if not config.debug and local_rank == 0: cb_vert(params) cb_center_save(params) cb_save(params) train_module.fit( train_data_iter, optimizer_params=backbone_kwargs, initializer=mx.init.Normal(0.1), batch_end_callback=call_back_fn) if __name__ == '__main__': train_net()

insightface/recognition/partial_fc/mxnet/train_memory.py/0

import sys import os import mxnet as mx import mxnet.ndarray as nd import mxnet.gluon as gluon import mxnet.gluon.nn as nn import mxnet.autograd as ag import symbol_utils sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Act(): if config.net_act == 'prelu': return nn.PReLU() else: return nn.Activation(config.net_act) def ConvBlock(channels, kernel_size, strides, **kwargs): out = nn.HybridSequential(**kwargs) with out.name_scope(): out.add( nn.Conv2D(channels, kernel_size, strides=strides, padding=1, use_bias=False), nn.BatchNorm(scale=True), Act() #nn.Activation('relu') ) return out def Conv1x1(channels, is_linear=False, **kwargs): out = nn.HybridSequential(**kwargs) with out.name_scope(): out.add(nn.Conv2D(channels, 1, padding=0, use_bias=False), nn.BatchNorm(scale=True)) if not is_linear: #out.add(nn.Activation('relu')) out.add(Act()) return out def DWise(channels, strides, kernel_size=3, **kwargs): out = nn.HybridSequential(**kwargs) with out.name_scope(): out.add( nn.Conv2D(channels, kernel_size, strides=strides, padding=kernel_size // 2, groups=channels, use_bias=False), nn.BatchNorm(scale=True), Act() #nn.Activation('relu') ) return out class SepCONV(nn.HybridBlock): def __init__(self, inp, output, kernel_size, depth_multiplier=1, with_bn=True, **kwargs): super(SepCONV, self).__init__(**kwargs) with self.name_scope(): self.net = nn.HybridSequential() cn = int(inp * depth_multiplier) if output is None: self.net.add( nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1, 1), padding=kernel_size // 2, use_bias=not with_bn)) else: self.net.add( nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1, 1), padding=kernel_size // 2, use_bias=False), nn.BatchNorm(), Act(), #nn.Activation('relu'), nn.Conv2D(in_channels=cn, channels=output, kernel_size=(1, 1), strides=(1, 1), use_bias=not with_bn)) self.with_bn = with_bn self.act = Act() #self.act = nn.Activation('relu') if with_bn: self.bn = nn.BatchNorm() def hybrid_forward(self, F, x): x = self.net(x) if self.with_bn: x = self.bn(x) if self.act is not None: x = self.act(x) return x class ExpandedConv(nn.HybridBlock): def __init__(self, inp, oup, t, strides, kernel=3, same_shape=True, **kwargs): super(ExpandedConv, self).__init__(**kwargs) self.same_shape = same_shape self.strides = strides with self.name_scope(): self.bottleneck = nn.HybridSequential() self.bottleneck.add( Conv1x1(inp * t, prefix="expand_"), DWise(inp * t, self.strides, kernel, prefix="dwise_"), Conv1x1(oup, is_linear=True, prefix="linear_")) def hybrid_forward(self, F, x): out = self.bottleneck(x) if self.strides == 1 and self.same_shape: out = F.elemwise_add(out, x) return out def ExpandedConvSequence(t, k, inp, oup, repeats, first_strides, **kwargs): seq = nn.HybridSequential(**kwargs) with seq.name_scope(): seq.add(ExpandedConv(inp, oup, t, first_strides, k, same_shape=False)) curr_inp = oup for i in range(1, repeats): seq.add(ExpandedConv(curr_inp, oup, t, 1)) curr_inp = oup return seq class MNasNet(nn.HybridBlock): def __init__(self, m=1.0, **kwargs): super(MNasNet, self).__init__(**kwargs) self.first_oup = int(32 * m) self.second_oup = int(16 * m) #self.second_oup = int(32*m) self.interverted_residual_setting = [ # t, c, n, s, k [3, int(24 * m), 3, 2, 3, "stage2_"], # -> 56x56 [3, int(40 * m), 3, 2, 5, "stage3_"], # -> 28x28 [6, int(80 * m), 3, 2, 5, "stage4_1_"], # -> 14x14 [6, int(96 * m), 2, 1, 3, "stage4_2_"], # -> 14x14 [6, int(192 * m), 4, 2, 5, "stage5_1_"], # -> 7x7 [6, int(320 * m), 1, 1, 3, "stage5_2_"], # -> 7x7 ] self.last_channels = int(1024 * m) with self.name_scope(): self.features = nn.HybridSequential() self.features.add( ConvBlock(self.first_oup, 3, 1, prefix="stage1_conv0_")) self.features.add( SepCONV(self.first_oup, self.second_oup, 3, prefix="stage1_sepconv0_")) inp = self.second_oup for i, (t, c, n, s, k, prefix) in enumerate(self.interverted_residual_setting): oup = c self.features.add( ExpandedConvSequence(t, k, inp, oup, n, s, prefix=prefix)) inp = oup self.features.add(Conv1x1(self.last_channels, prefix="stage5_3_")) #self.features.add(nn.GlobalAvgPool2D()) #self.features.add(nn.Flatten()) #self.output = nn.Dense(num_classes) def hybrid_forward(self, F, x): x = self.features(x) #x = self.output(x) return x def num_output_channel(self): return self.last_channels def get_symbol(): net = MNasNet(config.net_multiplier) data = mx.sym.Variable(name='data') data = data - 127.5 data = data * 0.0078125 body = net(data) fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output, input_channel=net.num_output_channel()) return fc1

insightface/recognition/subcenter_arcface/symbol/fmnasnet.py/0

import numbers import os import queue as Queue import threading import mxnet as mx import numpy as np import torch from torch.utils.data import DataLoader, Dataset from torchvision import transforms class BackgroundGenerator(threading.Thread): def __init__(self, generator, local_rank, max_prefetch=6): super(BackgroundGenerator, self).__init__() self.queue = Queue.Queue(max_prefetch) self.generator = generator self.local_rank = local_rank self.daemon = True self.start() def run(self): torch.cuda.set_device(self.local_rank) for item in self.generator: self.queue.put(item) self.queue.put(None) def next(self): next_item = self.queue.get() if next_item is None: raise StopIteration return next_item def __next__(self): return self.next() def __iter__(self): return self class DataLoaderX(DataLoader): def __init__(self, local_rank, **kwargs): super(DataLoaderX, self).__init__(**kwargs) self.stream = torch.cuda.Stream(local_rank) self.local_rank = local_rank def __iter__(self): self.iter = super(DataLoaderX, self).__iter__() self.iter = BackgroundGenerator(self.iter, self.local_rank) self.preload() return self def preload(self): self.batch = next(self.iter, None) if self.batch is None: return None with torch.cuda.stream(self.stream): for k in range(len(self.batch)): self.batch[k] = self.batch[k].to(device=self.local_rank, non_blocking=True) def __next__(self): torch.cuda.current_stream().wait_stream(self.stream) batch = self.batch if batch is None: raise StopIteration self.preload() return batch class MXFaceDataset(Dataset): def __init__(self, root_dir, local_rank): super(MXFaceDataset, self).__init__() self.transform = transforms.Compose( [transforms.ToPILImage(), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) self.root_dir = root_dir self.local_rank = local_rank path_imgrec = os.path.join(root_dir, 'train.rec') path_imgidx = os.path.join(root_dir, 'train.idx') self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') s = self.imgrec.read_idx(0) header, _ = mx.recordio.unpack(s) #print('ddd1') if header.flag > 0: if len(header.label)==2: self.header0 = (int(header.label[0]), int(header.label[1])) self.imgidx = np.array(range(1, int(header.label[0]))) else: self.imgidx = np.array(list(self.imgrec.keys)) else: self.imgidx = np.array(list(self.imgrec.keys)) #print('ddd2') def __getitem__(self, index): idx = self.imgidx[index] s = self.imgrec.read_idx(idx) header, img = mx.recordio.unpack(s) label = header.label if not isinstance(label, numbers.Number): label = label[0] sample = mx.image.imdecode(img).asnumpy() label = torch.tensor(label, dtype=torch.long) if self.transform is not None: sample = self.transform(sample) return sample, label def __len__(self): return len(self.imgidx)

insightface/recognition/vpl/dataset.py/0

import torch @torch.no_grad() def concat_all_gather(tensor): """ Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. """ tensors_gather = [torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())] torch.distributed.all_gather(tensors_gather, tensor, async_op=False) output = torch.cat(tensors_gather, dim=0) return output @torch.no_grad() def batch_shuffle_ddp(x, rank, world_size): """ Batch shuffle, for making use of BatchNorm. *** Only support DistributedDataParallel (DDP) model. *** """ # gather from all gpus batch_size_this = x.shape[0] x_gather = concat_all_gather(x) batch_size_all = x_gather.shape[0] # random shuffle index idx_shuffle = torch.randperm(batch_size_all).cuda() # broadcast to all gpus torch.distributed.broadcast(idx_shuffle, src=0) # index for restoring idx_unshuffle = torch.argsort(idx_shuffle) # shuffled index for this gpu idx_this = idx_shuffle.view(world_size, -1)[rank] return x_gather[idx_this], idx_unshuffle @torch.no_grad() def batch_unshuffle_ddp(x, idx_unshuffle, rank, world_size): """ Undo batch shuffle. *** Only support DistributedDataParallel (DDP) model. *** """ # gather from all gpus batch_size_this = x.shape[0] x_gather = concat_all_gather(x) batch_size_all = x_gather.shape[0] # restored index for this gpu idx_this = idx_unshuffle.view(world_size, -1)[rank] return x_gather[idx_this]

insightface/recognition/vpl/utils/utils_dist.py/0

set -ex GPU=0 python preprocess/get_aux_dataset.py -g $GPU -i '../raw_data/0' -o 0 -d 'Test' --yaw 17 --pitch 0 python preprocess/preprocess_cameras.py -i 0 -d 'Test'

insightface/reconstruction/PBIDR/code/script/data_process.sh/0

from argparse import ArgumentParser import os import os.path as osp import torch import torch.nn as nn from torch.nn import functional as F from torch.utils.data import DataLoader import pytorch_lightning as pl from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.callbacks import LearningRateMonitor from pytorch_lightning.loggers import TensorBoardLogger import timm from datasets.dataset_gaze import GazeDataset, DataLoaderX from models import GazeModel def cli_main(): pl.seed_everything(727) # ------------ # args # ------------ parser = ArgumentParser() parser.add_argument('--backbone', default='resnet101d', type=str) parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--epoch', default=16, type=int) parser.add_argument('--root', default='data/gaze_refine', type=str) parser.add_argument('--num-gpus', default=8, type=int) parser.add_argument('--tf32', action='store_true') parser = pl.Trainer.add_argparse_args(parser) args = parser.parse_args() if not args.tf32: torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cudnn.allow_tf32 = False else: torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.backends.cudnn.benchmark = True # ------------ # data # ------------ train_set = GazeDataset(root_dir=args.root, is_train=True) val_set = GazeDataset(root_dir=args.root, is_train=False) print('train data size:', len(train_set)) train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=2, pin_memory=True) val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False) # ------------ # model # ------------ model = GazeModel(backbone=args.backbone, epoch=args.epoch) ckpt_path = 'work_dirs/gaze' if not os.path.exists(ckpt_path): os.makedirs(ckpt_path) # ------------ # training # ------------ checkpoint_callback = ModelCheckpoint( monitor='val_loss', dirpath=ckpt_path, filename='{epoch:02d}-{val_loss:.6f}', save_top_k=5, mode='min', ) lr_monitor = LearningRateMonitor(logging_interval='step') trainer = pl.Trainer( gpus = args.num_gpus, accelerator="gpu", strategy="ddp", benchmark=True, logger=TensorBoardLogger(osp.join(ckpt_path, 'logs')), callbacks=[checkpoint_callback, lr_monitor], check_val_every_n_epoch=1, #progress_bar_refresh_rate=1, max_epochs=args.epoch, ) trainer.fit(model, train_loader, val_loader) if __name__ == '__main__': cli_main()

insightface/reconstruction/gaze/trainer_gaze.py/0

import torch from torch.optim.lr_scheduler import _LRScheduler class PolyScheduler(_LRScheduler): def __init__(self, optimizer, base_lr, max_steps, warmup_steps, last_epoch=-1): self.base_lr = base_lr self.warmup_lr_init = 0.0001 self.max_steps: int = max_steps self.warmup_steps: int = warmup_steps self.power = 2 super(PolyScheduler, self).__init__(optimizer, last_epoch, False) def get_warmup_lr(self): alpha = float(self.last_epoch) / float(self.warmup_steps) #_lr = max(self.base_lr * alpha, self.warmup_lr_init) _lr = self.base_lr * alpha return [_lr for _ in self.optimizer.param_groups] def get_lr(self): if self.last_epoch == -1: return [self.warmup_lr_init for _ in self.optimizer.param_groups] if self.last_epoch < self.warmup_steps: return self.get_warmup_lr() else: alpha = pow( 1 - float(self.last_epoch - self.warmup_steps) / float(self.max_steps - self.warmup_steps), self.power, ) return [self.base_lr * alpha for _ in self.optimizer.param_groups] class StepScheduler(_LRScheduler): def __init__(self, optimizer, base_lr, lr_steps, warmup_steps, last_epoch=-1): self.base_lr = base_lr self.warmup_lr_init = 0.0001 self.lr_steps = lr_steps self.warmup_steps: int = warmup_steps super(StepScheduler, self).__init__(optimizer, last_epoch, False) def get_warmup_lr(self): alpha = float(self.last_epoch) / float(self.warmup_steps) #_lr = max(self.base_lr * alpha, self.warmup_lr_init) _lr = self.base_lr * alpha return [_lr for _ in self.optimizer.param_groups] def get_lr(self): if self.last_epoch == -1: return [self.warmup_lr_init for _ in self.optimizer.param_groups] if self.last_epoch < self.warmup_steps: return self.get_warmup_lr() else: alpha = 0.1 ** len([m for m in self.lr_steps if m <= self.last_epoch]) return [self.base_lr * alpha for _ in self.optimizer.param_groups] def get_scheduler(opt, cfg): if cfg.lr_func is not None: scheduler = torch.optim.lr_scheduler.LambdaLR( optimizer=opt, lr_lambda=cfg.lr_func) else: #total_batch_size = cfg.batch_size * cfg.world_size #warmup_steps = cfg.num_images // total_batch_size * cfg.warmup_epochs #total_steps = cfg.num_images // total_batch_size * cfg.num_epochs if cfg.lr_steps is None: scheduler = PolyScheduler( optimizer=opt, base_lr=cfg.lr, max_steps=cfg.total_steps, warmup_steps=cfg.warmup_steps, ) else: scheduler = StepScheduler( optimizer=opt, base_lr=cfg.lr, lr_steps=cfg.lr_steps, warmup_steps=cfg.warmup_steps, ) return scheduler

insightface/reconstruction/jmlr/lr_scheduler.py/0

# Copyright (c) 2020, Baris Gecer. All rights reserved. # # This work is made available under the CC BY-NC-SA 4.0. # To view a copy of this license, see LICENSE import math import tensorflow as tf import numpy as np import external.stylegan2.dnnlib.tflib as tflib from functools import partial def create_stub(name, batch_size): return tf.constant(0, dtype='float32', shape=(batch_size, 0)) def create_variable_for_generator(name, batch_size, tiled_dlatent, model_scale=18, tile_size = 1): if tiled_dlatent: low_dim_dlatent = tf.get_variable('learnable_dlatents', shape=(batch_size, tile_size, 512), dtype='float32', initializer=tf.initializers.random_normal()) return tf.tile(low_dim_dlatent, [1, model_scale // tile_size, 1]) else: return tf.get_variable('learnable_dlatents', shape=(batch_size, model_scale, 512), dtype='float32', initializer=tf.initializers.random_normal()) class Generator: def __init__(self, model, batch_size, custom_input=None, clipping_threshold=2, tiled_dlatent=False, model_res=1024, randomize_noise=False): self.batch_size = batch_size self.tiled_dlatent=tiled_dlatent self.model_scale = int(2*(math.log(model_res,2)-1)) # For example, 1024 -> 18 if tiled_dlatent: self.initial_dlatents = np.zeros((self.batch_size, 512)) model.components.synthesis.run(np.zeros((self.batch_size, self.model_scale, 512)), randomize_noise=randomize_noise, minibatch_size=self.batch_size, custom_inputs=[partial(create_variable_for_generator, batch_size=batch_size, tiled_dlatent=True), partial(create_stub, batch_size=batch_size)], structure='fixed') else: self.initial_dlatents = np.zeros((self.batch_size, self.model_scale, 512)) if custom_input is not None: model.components.synthesis.run(self.initial_dlatents, randomize_noise=randomize_noise, minibatch_size=self.batch_size, custom_inputs=[partial(custom_input.eval(), batch_size=batch_size), partial(create_stub, batch_size=batch_size)], structure='fixed') else: model.components.synthesis.run(self.initial_dlatents, randomize_noise=randomize_noise, minibatch_size=self.batch_size, custom_inputs=[partial(create_variable_for_generator, batch_size=batch_size, tiled_dlatent=False, model_scale=self.model_scale), partial(create_stub, batch_size=batch_size)], structure='fixed') self.dlatent_avg_def = model.get_var('dlatent_avg') self.reset_dlatent_avg() self.sess = tf.get_default_session() self.graph = tf.get_default_graph() self.dlatent_variable = next(v for v in tf.global_variables() if 'learnable_dlatents' in v.name) self._assign_dlatent_ph = tf.placeholder(tf.float32, name="assign_dlatent_ph") self._assign_dlantent = tf.assign(self.dlatent_variable, self._assign_dlatent_ph) self.set_dlatents(self.initial_dlatents) def get_tensor(name): try: return self.graph.get_tensor_by_name(name) except KeyError: return None self.generator_output = get_tensor('G_synthesis_1/_Run/concat:0') if self.generator_output is None: self.generator_output = get_tensor('G_synthesis_1/_Run/concat/concat:0') if self.generator_output is None: self.generator_output = get_tensor('G_synthesis_1/_Run/concat_1/concat:0') # If we loaded only Gs and didn't load G or D, then scope "G_synthesis_1" won't exist in the graph. if self.generator_output is None: self.generator_output = get_tensor('G_synthesis/_Run/concat:0') if self.generator_output is None: self.generator_output = get_tensor('G_synthesis/_Run/concat/concat:0') if self.generator_output is None: self.generator_output = get_tensor('G_synthesis/_Run/concat_1/concat:0') if self.generator_output is None: for op in self.graph.get_operations(): print(op) raise Exception("Couldn't find G_synthesis_1/_Run/concat tensor output") self.generated_image = tflib.convert_images_to_uint8(self.generator_output, nchw_to_nhwc=True, uint8_cast=False) self.generated_image_uint8 = tf.saturate_cast(self.generated_image, tf.uint8) # Implement stochastic clipping similar to what is described in https://arxiv.org/abs/1702.04782 # (Slightly different in that the latent space is normal gaussian here and was uniform in [-1, 1] in that paper, # so we clip any vector components outside of [-2, 2]. It seems fine, but I haven't done an ablation check.) clipping_mask = tf.math.logical_or(self.dlatent_variable > clipping_threshold, self.dlatent_variable < -clipping_threshold) clipped_values = tf.where(clipping_mask, tf.random_normal(shape=self.dlatent_variable.shape), self.dlatent_variable) self.stochastic_clip_op = tf.assign(self.dlatent_variable, clipped_values) def reset_dlatents(self): self.set_dlatents(self.initial_dlatents) def set_dlatents(self, dlatents): if self.tiled_dlatent: if (dlatents.shape != (self.batch_size, 512)) and (dlatents.shape[1] != 512): dlatents = np.mean(dlatents, axis=1) if (dlatents.shape != (self.batch_size, 512)): dlatents = np.vstack([dlatents, np.zeros((self.batch_size-dlatents.shape[0], 512))]) assert (dlatents.shape == (self.batch_size, 512)) else: if (dlatents.shape[1] > self.model_scale): dlatents = dlatents[:,:self.model_scale,:] if (isinstance(dlatents.shape[0], int)): if (dlatents.shape != (self.batch_size, self.model_scale, 512)): dlatents = np.vstack([dlatents, np.zeros((self.batch_size-dlatents.shape[0], self.model_scale, 512))]) assert (dlatents.shape == (self.batch_size, self.model_scale, 512)) self.sess.run([self._assign_dlantent], {self._assign_dlatent_ph: dlatents}) return else: self._assign_dlantent = tf.assign(self.dlatent_variable, dlatents) return self.sess.run([self._assign_dlantent], {self._assign_dlatent_ph: dlatents}) def stochastic_clip_dlatents(self): self.sess.run(self.stochastic_clip_op) def get_dlatents(self): return self.sess.run(self.dlatent_variable) def get_dlatent_avg(self): return self.dlatent_avg def set_dlatent_avg(self, dlatent_avg): self.dlatent_avg = dlatent_avg def reset_dlatent_avg(self): self.dlatent_avg = self.dlatent_avg_def def generate_images(self, dlatents=None): if dlatents is not None: self.set_dlatents(dlatents) return self.sess.run(self.generated_image_uint8)

insightface/reconstruction/ostec/core/generator_model.py/0

import torch from . import graph import torch.nn as nn import torch.nn.functional as F from torch.nn.parameter import Parameter class GraphConvolution(nn.Module): def __init__(self, in_features, out_features, bias=False): super(GraphConvolution, self).__init__() self.in_features = in_features self.out_features = out_features self.weight = Parameter(torch.FloatTensor(in_features, out_features)) if bias: self.bias = Parameter(torch.FloatTensor(out_features)) else: self.register_parameter("bias", None) self.reset_parameters() def reset_parameters(self): # stdv = 1./math.sqrt(self.weight(1)) # self.weight.data.uniform_(-stdv,stdv) torch.nn.init.xavier_uniform_(self.weight) # if self.bias is not None: # self.bias.data.uniform_(-stdv,stdv) def forward(self, input, adj=None, relu=False): support = torch.matmul(input, self.weight) # print(support.size(),adj.size()) if adj is not None: output = torch.matmul(adj, support) else: output = support # print(output.size()) if self.bias is not None: return output + self.bias else: if relu: return F.relu(output) else: return output def __repr__(self): return ( self.__class__.__name__ + " (" + str(self.in_features) + " -> " + str(self.out_features) + ")" ) class Featuremaps_to_Graph(nn.Module): def __init__(self, input_channels, hidden_layers, nodes=7): super(Featuremaps_to_Graph, self).__init__() self.pre_fea = Parameter(torch.FloatTensor(input_channels, nodes)) self.weight = Parameter(torch.FloatTensor(input_channels, hidden_layers)) self.reset_parameters() def forward(self, input): n, c, h, w = input.size() # print('fea input',input.size()) input1 = input.view(n, c, h * w) input1 = input1.transpose(1, 2) # n x hw x c # print('fea input1', input1.size()) ############## Feature maps to node ################ fea_node = torch.matmul(input1, self.pre_fea) # n x hw x n_classes weight_node = torch.matmul(input1, self.weight) # n x hw x hidden_layer # softmax fea_node fea_node = F.softmax(fea_node, dim=-1) # print(fea_node.size(),weight_node.size()) graph_node = F.relu(torch.matmul(fea_node.transpose(1, 2), weight_node)) return graph_node # n x n_class x hidden_layer def reset_parameters(self): for ww in self.parameters(): torch.nn.init.xavier_uniform_(ww) # if self.bias is not None: # self.bias.data.uniform_(-stdv,stdv) class Featuremaps_to_Graph_transfer(nn.Module): def __init__(self, input_channels, hidden_layers, nodes=7, source_nodes=20): super(Featuremaps_to_Graph_transfer, self).__init__() self.pre_fea = Parameter(torch.FloatTensor(input_channels, nodes)) self.weight = Parameter(torch.FloatTensor(input_channels, hidden_layers)) self.pre_fea_transfer = nn.Sequential( *[ nn.Linear(source_nodes, source_nodes), nn.LeakyReLU(True), nn.Linear(source_nodes, nodes), nn.LeakyReLU(True), ] ) self.reset_parameters() def forward(self, input, source_pre_fea): self.pre_fea.data = self.pre_fea_learn(source_pre_fea) n, c, h, w = input.size() # print('fea input',input.size()) input1 = input.view(n, c, h * w) input1 = input1.transpose(1, 2) # n x hw x c # print('fea input1', input1.size()) ############## Feature maps to node ################ fea_node = torch.matmul(input1, self.pre_fea) # n x hw x n_classes weight_node = torch.matmul(input1, self.weight) # n x hw x hidden_layer # softmax fea_node fea_node = F.softmax(fea_node, dim=1) # print(fea_node.size(),weight_node.size()) graph_node = F.relu(torch.matmul(fea_node.transpose(1, 2), weight_node)) return graph_node # n x n_class x hidden_layer def pre_fea_learn(self, input): pre_fea = self.pre_fea_transfer.forward(input.unsqueeze(0)).squeeze(0) return self.pre_fea.data + pre_fea class Graph_to_Featuremaps(nn.Module): # this is a special version def __init__(self, input_channels, output_channels, hidden_layers, nodes=7): super(Graph_to_Featuremaps, self).__init__() self.node_fea = Parameter(torch.FloatTensor(input_channels + hidden_layers, 1)) self.weight = Parameter(torch.FloatTensor(hidden_layers, output_channels)) self.reset_parameters() def reset_parameters(self): for ww in self.parameters(): torch.nn.init.xavier_uniform_(ww) def forward(self, input, res_feature): """ :param input: 1 x batch x nodes x hidden_layer :param res_feature: batch x channels x h x w :return: """ batchi, channeli, hi, wi = res_feature.size() # print(res_feature.size()) # print(input.size()) try: _, batch, nodes, hidden = input.size() except: # print(input.size()) input = input.unsqueeze(0) _, batch, nodes, hidden = input.size() assert batch == batchi input1 = input.transpose(0, 1).expand(batch, hi * wi, nodes, hidden) res_feature_after_view = res_feature.view(batch, channeli, hi * wi).transpose( 1, 2 ) res_feature_after_view1 = res_feature_after_view.unsqueeze(2).expand( batch, hi * wi, nodes, channeli ) new_fea = torch.cat((res_feature_after_view1, input1), dim=3) # print(self.node_fea.size(),new_fea.size()) new_node = torch.matmul(new_fea, self.node_fea) # batch x hw x nodes x 1 new_weight = torch.matmul(input, self.weight) # batch x node x channel new_node = new_node.view(batch, hi * wi, nodes) # 0721 new_node = F.softmax(new_node, dim=-1) # feature_out = torch.matmul(new_node, new_weight) # print(feature_out.size()) feature_out = feature_out.transpose(2, 3).contiguous().view(res_feature.size()) return F.relu(feature_out) class Graph_to_Featuremaps_savemem(nn.Module): # this is a special version for saving gpu memory. The process is same as Graph_to_Featuremaps. def __init__(self, input_channels, output_channels, hidden_layers, nodes=7): super(Graph_to_Featuremaps_savemem, self).__init__() self.node_fea_for_res = Parameter(torch.FloatTensor(input_channels, 1)) self.node_fea_for_hidden = Parameter(torch.FloatTensor(hidden_layers, 1)) self.weight = Parameter(torch.FloatTensor(hidden_layers, output_channels)) self.reset_parameters() def reset_parameters(self): for ww in self.parameters(): torch.nn.init.xavier_uniform_(ww) def forward(self, input, res_feature): """ :param input: 1 x batch x nodes x hidden_layer :param res_feature: batch x channels x h x w :return: """ batchi, channeli, hi, wi = res_feature.size() # print(res_feature.size()) # print(input.size()) try: _, batch, nodes, hidden = input.size() except: # print(input.size()) input = input.unsqueeze(0) _, batch, nodes, hidden = input.size() assert batch == batchi input1 = input.transpose(0, 1).expand(batch, hi * wi, nodes, hidden) res_feature_after_view = res_feature.view(batch, channeli, hi * wi).transpose( 1, 2 ) res_feature_after_view1 = res_feature_after_view.unsqueeze(2).expand( batch, hi * wi, nodes, channeli ) # new_fea = torch.cat((res_feature_after_view1,input1),dim=3) ## sim new_node1 = torch.matmul(res_feature_after_view1, self.node_fea_for_res) new_node2 = torch.matmul(input1, self.node_fea_for_hidden) new_node = new_node1 + new_node2 ## sim end # print(self.node_fea.size(),new_fea.size()) # new_node = torch.matmul(new_fea, self.node_fea) # batch x hw x nodes x 1 new_weight = torch.matmul(input, self.weight) # batch x node x channel new_node = new_node.view(batch, hi * wi, nodes) # 0721 new_node = F.softmax(new_node, dim=-1) # feature_out = torch.matmul(new_node, new_weight) # print(feature_out.size()) feature_out = feature_out.transpose(2, 3).contiguous().view(res_feature.size()) return F.relu(feature_out) class Graph_trans(nn.Module): def __init__( self, in_features, out_features, begin_nodes=7, end_nodes=2, bias=False, adj=None, ): super(Graph_trans, self).__init__() self.in_features = in_features self.out_features = out_features self.weight = Parameter(torch.FloatTensor(in_features, out_features)) if adj is not None: h, w = adj.size() assert (h == end_nodes) and (w == begin_nodes) self.adj = torch.autograd.Variable(adj, requires_grad=False) else: self.adj = Parameter(torch.FloatTensor(end_nodes, begin_nodes)) if bias: self.bias = Parameter(torch.FloatTensor(out_features)) else: self.register_parameter("bias", None) # self.reset_parameters() def reset_parameters(self): # stdv = 1./math.sqrt(self.weight(1)) # self.weight.data.uniform_(-stdv,stdv) torch.nn.init.xavier_uniform_(self.weight) # if self.bias is not None: # self.bias.data.uniform_(-stdv,stdv) def forward(self, input, relu=False, adj_return=False, adj=None): support = torch.matmul(input, self.weight) # print(support.size(),self.adj.size()) if adj is None: adj = self.adj adj1 = self.norm_trans_adj(adj) output = torch.matmul(adj1, support) if adj_return: output1 = F.normalize(output, p=2, dim=-1) self.adj_mat = torch.matmul(output1, output1.transpose(-2, -1)) if self.bias is not None: return output + self.bias else: if relu: return F.relu(output) else: return output def get_adj_mat(self): adj = graph.normalize_adj_torch(F.relu(self.adj_mat)) return adj def get_encode_adj(self): return self.adj def norm_trans_adj(self, adj): # maybe can use softmax adj = F.relu(adj) r = F.softmax(adj, dim=-1) # print(adj.size()) # row_sum = adj.sum(-1).unsqueeze(-1) # d_mat = row_sum.expand(adj.size()) # r = torch.div(row_sum,d_mat) # r[torch.isnan(r)] = 0 return r if __name__ == "__main__": graph = torch.randn((7, 128)) en = GraphConvolution(128, 128) a = en.forward(graph) print(a) # a = en.forward(graph,pred) # print(a.size())

insightface/reconstruction/ostec/external/graphonomy/FaceHairMask/gcn.py/0

# Copyright (c) 2019, NVIDIA Corporation. All rights reserved. # # This work is made available under the Nvidia Source Code License-NC. # To view a copy of this license, visit # https://nvlabs.github.io/stylegan2/license.html from . import run_context from . import submit

insightface/reconstruction/ostec/external/stylegan2/dnnlib/submission/__init__.py/0

# Copyright (c) 2019, NVIDIA Corporation. All rights reserved. # # This work is made available under the Nvidia Source Code License-NC. # To view a copy of this license, visit # https://nvlabs.github.io/stylegan2/license.html """Miscellaneous utility classes and functions.""" import ctypes import fnmatch import importlib import inspect import numpy as np import os import shutil import sys import types import io import pickle import re import requests import html import hashlib import glob import uuid from distutils.util import strtobool from typing import Any, List, Tuple, Union # Util classes # ------------------------------------------------------------------------------------------ class EasyDict(dict): """Convenience class that behaves like a dict but allows access with the attribute syntax.""" def __getattr__(self, name: str) -> Any: try: return self[name] except KeyError: raise AttributeError(name) def __setattr__(self, name: str, value: Any) -> None: self[name] = value def __delattr__(self, name: str) -> None: del self[name] class Logger(object): """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): self.file = None if file_name is not None: self.file = open(file_name, file_mode) self.should_flush = should_flush self.stdout = sys.stdout self.stderr = sys.stderr sys.stdout = self sys.stderr = self def __enter__(self) -> "Logger": return self def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: self.close() def write(self, text: str) -> None: """Write text to stdout (and a file) and optionally flush.""" if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash return if self.file is not None: self.file.write(text) self.stdout.write(text) if self.should_flush: self.flush() def flush(self) -> None: """Flush written text to both stdout and a file, if open.""" if self.file is not None: self.file.flush() self.stdout.flush() def close(self) -> None: """Flush, close possible files, and remove stdout/stderr mirroring.""" self.flush() # if using multiple loggers, prevent closing in wrong order if sys.stdout is self: sys.stdout = self.stdout if sys.stderr is self: sys.stderr = self.stderr if self.file is not None: self.file.close() # Small util functions # ------------------------------------------------------------------------------------------ def format_time(seconds: Union[int, float]) -> str: """Convert the seconds to human readable string with days, hours, minutes and seconds.""" s = int(np.rint(seconds)) if s < 60: return "{0}s".format(s) elif s < 60 * 60: return "{0}m {1:02}s".format(s // 60, s % 60) elif s < 24 * 60 * 60: return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) else: return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) def ask_yes_no(question: str) -> bool: """Ask the user the question until the user inputs a valid answer.""" while True: try: print("{0} [y/n]".format(question)) return strtobool(input().lower()) except ValueError: pass def tuple_product(t: Tuple) -> Any: """Calculate the product of the tuple elements.""" result = 1 for v in t: result *= v return result _str_to_ctype = { "uint8": ctypes.c_ubyte, "uint16": ctypes.c_uint16, "uint32": ctypes.c_uint32, "uint64": ctypes.c_uint64, "int8": ctypes.c_byte, "int16": ctypes.c_int16, "int32": ctypes.c_int32, "int64": ctypes.c_int64, "float32": ctypes.c_float, "float64": ctypes.c_double } def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" type_str = None if isinstance(type_obj, str): type_str = type_obj elif hasattr(type_obj, "__name__"): type_str = type_obj.__name__ elif hasattr(type_obj, "name"): type_str = type_obj.name else: raise RuntimeError("Cannot infer type name from input") assert type_str in _str_to_ctype.keys() my_dtype = np.dtype(type_str) my_ctype = _str_to_ctype[type_str] assert my_dtype.itemsize == ctypes.sizeof(my_ctype) return my_dtype, my_ctype def is_pickleable(obj: Any) -> bool: try: with io.BytesIO() as stream: pickle.dump(obj, stream) return True except: return False # Functionality to import modules/objects by name, and call functions by name # ------------------------------------------------------------------------------------------ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: """Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part removed).""" # allow convenience shorthands, substitute them by full names obj_name = re.sub("^np.", "numpy.", obj_name) obj_name = re.sub("^tf.", "tensorflow.", obj_name) # list alternatives for (module_name, local_obj_name) parts = obj_name.split(".") name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] # try each alternative in turn for module_name, local_obj_name in name_pairs: try: module = importlib.import_module(module_name) # may raise ImportError get_obj_from_module(module, local_obj_name) # may raise AttributeError return module, local_obj_name except: pass # maybe some of the modules themselves contain errors? for module_name, _local_obj_name in name_pairs: try: importlib.import_module(module_name) # may raise ImportError except ImportError: if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): raise # maybe the requested attribute is missing? for module_name, local_obj_name in name_pairs: try: module = importlib.import_module(module_name) # may raise ImportError get_obj_from_module(module, local_obj_name) # may raise AttributeError except ImportError: pass # we are out of luck, but we have no idea why raise ImportError(obj_name) def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: """Traverses the object name and returns the last (rightmost) python object.""" if obj_name == '': return module obj = module for part in obj_name.split("."): obj = getattr(obj, part) return obj def get_obj_by_name(name: str) -> Any: """Finds the python object with the given name.""" module, obj_name = get_module_from_obj_name(name) return get_obj_from_module(module, obj_name) def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: """Finds the python object with the given name and calls it as a function.""" assert func_name is not None func_obj = get_obj_by_name(func_name) assert callable(func_obj) return func_obj(*args, **kwargs) def get_module_dir_by_obj_name(obj_name: str) -> str: """Get the directory path of the module containing the given object name.""" module, _ = get_module_from_obj_name(obj_name) return os.path.dirname(inspect.getfile(module)) def is_top_level_function(obj: Any) -> bool: """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ def get_top_level_function_name(obj: Any) -> str: """Return the fully-qualified name of a top-level function.""" assert is_top_level_function(obj) return obj.__module__ + "." + obj.__name__ # File system helpers # ------------------------------------------------------------------------------------------ def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: """List all files recursively in a given directory while ignoring given file and directory names. Returns list of tuples containing both absolute and relative paths.""" assert os.path.isdir(dir_path) base_name = os.path.basename(os.path.normpath(dir_path)) if ignores is None: ignores = [] result = [] for root, dirs, files in os.walk(dir_path, topdown=True): for ignore_ in ignores: dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] # dirs need to be edited in-place for d in dirs_to_remove: dirs.remove(d) files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] absolute_paths = [os.path.join(root, f) for f in files] relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] if add_base_to_relative: relative_paths = [os.path.join(base_name, p) for p in relative_paths] assert len(absolute_paths) == len(relative_paths) result += zip(absolute_paths, relative_paths) return result def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: """Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories.""" for file in files: target_dir_name = os.path.dirname(file[1]) # will create all intermediate-level directories if not os.path.exists(target_dir_name): os.makedirs(target_dir_name) shutil.copyfile(file[0], file[1]) # URL helpers # ------------------------------------------------------------------------------------------ def is_url(obj: Any, allow_file_urls: bool = False) -> bool: """Determine whether the given object is a valid URL string.""" if not isinstance(obj, str) or not "://" in obj: return False if allow_file_urls and obj.startswith('file:///'): return True try: res = requests.compat.urlparse(obj) if not res.scheme or not res.netloc or not "." in res.netloc: return False res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) if not res.scheme or not res.netloc or not "." in res.netloc: return False except: return False return True def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True) -> Any: """Download the given URL and return a binary-mode file object to access the data.""" assert is_url(url, allow_file_urls=True) assert num_attempts >= 1 # Handle file URLs. if url.startswith('file:///'): return open(url[len('file:///'):], "rb") # Lookup from cache. url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() if cache_dir is not None: cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) if len(cache_files) == 1: return open(cache_files[0], "rb") # Download. url_name = None url_data = None with requests.Session() as session: if verbose: print("Downloading %s ..." % url, end="", flush=True) for attempts_left in reversed(range(num_attempts)): try: with session.get(url) as res: res.raise_for_status() if len(res.content) == 0: raise IOError("No data received") if len(res.content) < 8192: content_str = res.content.decode("utf-8") if "download_warning" in res.headers.get("Set-Cookie", ""): links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] if len(links) == 1: url = requests.compat.urljoin(url, links[0]) raise IOError("Google Drive virus checker nag") if "Google Drive - Quota exceeded" in content_str: raise IOError("Google Drive download quota exceeded -- please try again later") match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) url_name = match[1] if match else url url_data = res.content if verbose: print(" done") break except: if not attempts_left: if verbose: print(" failed") raise if verbose: print(".", end="", flush=True) # Save to cache. if cache_dir is not None: safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) os.makedirs(cache_dir, exist_ok=True) with open(temp_file, "wb") as f: f.write(url_data) os.replace(temp_file, cache_file) # atomic # Return data as file object. return io.BytesIO(url_data)

insightface/reconstruction/ostec/external/stylegan2/dnnlib/util.py/0

# Copyright (c) 2019, NVIDIA Corporation. All rights reserved. # # This work is made available under the Nvidia Source Code License-NC. # To view a copy of this license, visit # https://nvlabs.github.io/stylegan2/license.html import argparse import numpy as np import PIL.Image import dnnlib import dnnlib.tflib as tflib import re import sys import pretrained_networks #---------------------------------------------------------------------------- def generate_images(network_pkl, seeds, truncation_psi): print('Loading networks from "%s"...' % network_pkl) _G, _D, Gs = pretrained_networks.load_networks(network_pkl) noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')] Gs_kwargs = dnnlib.EasyDict() Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) Gs_kwargs.randomize_noise = False if truncation_psi is not None: Gs_kwargs.truncation_psi = truncation_psi for seed_idx, seed in enumerate(seeds): print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds))) rnd = np.random.RandomState(seed) z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component] tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width] images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel] PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed)) #---------------------------------------------------------------------------- def style_mixing_example(network_pkl, row_seeds, col_seeds, truncation_psi, col_styles, minibatch_size=4): print('Loading networks from "%s"...' % network_pkl) _G, _D, Gs = pretrained_networks.load_networks(network_pkl) w_avg = Gs.get_var('dlatent_avg') # [component] Gs_syn_kwargs = dnnlib.EasyDict() Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) Gs_syn_kwargs.randomize_noise = False Gs_syn_kwargs.minibatch_size = minibatch_size print('Generating W vectors...') all_seeds = list(set(row_seeds + col_seeds)) all_z = np.stack([np.random.RandomState(seed).randn(*Gs.input_shape[1:]) for seed in all_seeds]) # [minibatch, component] all_w = Gs.components.mapping.run(all_z, None) # [minibatch, layer, component] w1 = np.load('latent_representations/im_right_01.npy') w2 = np.load('latent_representations/04753.000002.02_C_01.npy') all_w = np.stack([w1, w2],0) all_w = w_avg + (all_w - w_avg) * truncation_psi # [minibatch, layer, component] w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))} # [layer, component] print('Generating images...') all_images = Gs.components.synthesis.run(all_w, **Gs_syn_kwargs) # [minibatch, height, width, channel] image_dict = {(seed, seed): image for seed, image in zip(all_seeds, list(all_images))} print('Generating style-mixed images...') for row_seed in row_seeds: for col_seed in col_seeds: w = w_dict[row_seed].copy() w[col_styles] = w_dict[col_seed][col_styles] image = Gs.components.synthesis.run(w[np.newaxis], **Gs_syn_kwargs)[0] image_dict[(row_seed, col_seed)] = image print('Saving images...') for (row_seed, col_seed), image in image_dict.items(): PIL.Image.fromarray(image, 'RGB').save(dnnlib.make_run_dir_path('%d-%d.png' % (row_seed, col_seed))) print('Saving image grid...') _N, _C, H, W = Gs.output_shape canvas = PIL.Image.new('RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black') for row_idx, row_seed in enumerate([None] + row_seeds): for col_idx, col_seed in enumerate([None] + col_seeds): if row_seed is None and col_seed is None: continue key = (row_seed, col_seed) if row_seed is None: key = (col_seed, col_seed) if col_seed is None: key = (row_seed, row_seed) canvas.paste(PIL.Image.fromarray(image_dict[key], 'RGB'), (W * col_idx, H * row_idx)) canvas.save(dnnlib.make_run_dir_path('grid.png')) #---------------------------------------------------------------------------- def _parse_num_range(s): '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.''' range_re = re.compile(r'^(\d+)-(\d+)$') m = range_re.match(s) if m: return range(int(m.group(1)), int(m.group(2))+1) vals = s.split(',') return [int(x) for x in vals] #---------------------------------------------------------------------------- _examples = '''examples: # Generate ffhq uncurated images (matches paper Figure 12) python %(prog)s generate-images --network=gdrive:networks/stylegan2-ffhq-config-f.pkl --seeds=6600-6625 --truncation-psi=0.5 # Generate ffhq curated images (matches paper Figure 11) python %(prog)s generate-images --network=gdrive:networks/stylegan2-ffhq-config-f.pkl --seeds=66,230,389,1518 --truncation-psi=1.0 # Generate uncurated car images (matches paper Figure 12) python %(prog)s generate-images --network=gdrive:networks/stylegan2-car-config-f.pkl --seeds=6000-6025 --truncation-psi=0.5 # Generate style mixing example (matches style mixing video clip) python %(prog)s style-mixing-example --network=gdrive:networks/stylegan2-ffhq-config-f.pkl --row-seeds=85,100,75,458,1500 --col-seeds=55,821,1789,293 --truncation-psi=1.0 ''' #---------------------------------------------------------------------------- def main(): parser = argparse.ArgumentParser( description='''StyleGAN2 generator. Run 'python %(prog)s <subcommand> --help' for subcommand help.''', epilog=_examples, formatter_class=argparse.RawDescriptionHelpFormatter ) subparsers = parser.add_subparsers(help='Sub-commands', dest='command') parser_generate_images = subparsers.add_parser('generate-images', help='Generate images') parser_generate_images.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True) parser_generate_images.add_argument('--seeds', type=_parse_num_range, help='List of random seeds', required=True) parser_generate_images.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5) parser_generate_images.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR') parser_style_mixing_example = subparsers.add_parser('style-mixing-example', help='Generate style mixing video') parser_style_mixing_example.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True) parser_style_mixing_example.add_argument('--row-seeds', type=_parse_num_range, help='Random seeds to use for image rows', required=True) parser_style_mixing_example.add_argument('--col-seeds', type=_parse_num_range, help='Random seeds to use for image columns', required=True) parser_style_mixing_example.add_argument('--col-styles', type=_parse_num_range, help='Style layer range (default: %(default)s)', default='0-6') parser_style_mixing_example.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5) parser_style_mixing_example.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR') args = parser.parse_args() kwargs = vars(args) subcmd = kwargs.pop('command') if subcmd is None: print ('Error: missing subcommand. Re-run with --help for usage.') sys.exit(1) sc = dnnlib.SubmitConfig() sc.num_gpus = 1 sc.submit_target = dnnlib.SubmitTarget.LOCAL sc.local.do_not_copy_source_files = True sc.run_dir_root = kwargs.pop('result_dir') sc.run_desc = subcmd func_name_map = { 'generate-images': 'run_generator.generate_images', 'style-mixing-example': 'run_generator.style_mixing_example' } dnnlib.submit_run(sc, func_name_map[subcmd], **kwargs) #---------------------------------------------------------------------------- if __name__ == "__main__": main() #----------------------------------------------------------------------------

insightface/reconstruction/ostec/external/stylegan2/run_generator.py/0

# Copyright (c) 2020, Baris Gecer. All rights reserved. # # This work is made available under the CC BY-NC-SA 4.0. # To view a copy of this license, see LICENSE from menpo3d.rasterize import ( rasterize_barycentric_coordinate_images, rasterize_mesh_from_barycentric_coordinate_images) from menpo.transform import Rotation, Translation, Scale from menpo3d.camera import PerspectiveProjection, PerspectiveCamera import numpy as np def rotation_z(theta, degrees=True): if degrees: # convert to radians theta = theta * np.pi / 180.0 return Rotation(np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]), skip_checks=True) def rotation_y(theta, degrees=True): if degrees: # convert to radians theta = theta * np.pi / 180.0 return Rotation(np.array([[np.cos(theta), 0, np.sin(theta)], [0, 1, 0], [-np.sin(theta), 0, np.cos(theta)]]), skip_checks=True) def rotation_x(theta, degrees=True): if degrees: theta = np.deg2rad(theta) return Rotation(np.array([[ 1, 0, 0], [ 0, np.cos(theta), -np.sin(theta)], [ 0, np.sin(theta), np.cos(theta)]]), skip_checks=True) def perspective_camera_for_template(img_shape, focal_length_mult=2, pose_angle_deg=[0,0,0], cam_dist = 7): f = np.array(img_shape).max() * focal_length_mult rot_z = rotation_z(180 + pose_angle_deg[2]) rot_y = rotation_y(180 + pose_angle_deg[1]) rot_x = rotation_x(pose_angle_deg[0]) rotation = rot_z.compose_before(rot_y).compose_before(rot_x) translation = Translation([0, 0, +cam_dist]) projection = PerspectiveProjection(f, img_shape) return PerspectiveCamera(rotation, translation, projection) def align_mesh_to_template(source, target, scale_corrective=1.2): scale = Scale((target.norm() / source.norm()) * scale_corrective, n_dims=target.n_dims) translation = Translation(target.centre() - source.centre()) return translation.compose_before(scale) def rasterize_image( mesh, img_shape, pose_angle_deg=[0,0,0], cam_dist = 7, ): camera = perspective_camera_for_template(img_shape, pose_angle_deg=pose_angle_deg, cam_dist= cam_dist) # Pre-process - align the mesh roughly with the template # aligned_mesh = align_mesh_to_template(mesh, template).apply(mesh) mesh_in_img = camera.apply(mesh) bcs = rasterize_barycentric_coordinate_images(mesh_in_img, img_shape) img = rasterize_mesh_from_barycentric_coordinate_images(mesh_in_img, *bcs) # shape_img = rasterize_shape_image_from_barycentric_coordinate_images(mesh, *bcs) img.pixels = np.clip(img.pixels,0.0,1.0) return img, mesh_in_img.points

insightface/reconstruction/ostec/utils/image_rasterization.py/0

import cv2 import numpy as np from skimage import transform as trans src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007], [51.157, 89.050], [57.025, 89.702]], dtype=np.float32) #<--left src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111], [45.177, 86.190], [64.246, 86.758]], dtype=np.float32) #---frontal src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493], [42.463, 87.010], [69.537, 87.010]], dtype=np.float32) #-->right src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111], [48.167, 86.758], [67.236, 86.190]], dtype=np.float32) #-->right profile src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007], [55.388, 89.702], [61.257, 89.050]], dtype=np.float32) src = np.array([src1, src2, src3, src4, src5]) src_map = {112: src, 224: src * 2} arcface_src = np.array( [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32) arcface_src = np.expand_dims(arcface_src, axis=0) # In[66]: # lmk is prediction; src is template def estimate_norm(lmk, image_size=112, mode='arcface'): assert lmk.shape == (5, 2) tform = trans.SimilarityTransform() lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1) min_M = [] min_index = [] min_error = float('inf') if mode == 'arcface': if image_size == 112: src = arcface_src else: src = float(image_size) / 112 * arcface_src else: src = src_map[image_size] for i in np.arange(src.shape[0]): tform.estimate(lmk, src[i]) M = tform.params[0:2, :] results = np.dot(M, lmk_tran.T) results = results.T error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1))) # print(error) if error < min_error: min_error = error min_M = M min_index = i return min_M, min_index def norm_crop(img, landmark, image_size=112, mode='arcface'): M, pose_index = estimate_norm(landmark, image_size, mode) warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) return warped def square_crop(im, S): if im.shape[0] > im.shape[1]: height = S width = int(float(im.shape[1]) / im.shape[0] * S) scale = float(S) / im.shape[0] else: width = S height = int(float(im.shape[0]) / im.shape[1] * S) scale = float(S) / im.shape[1] resized_im = cv2.resize(im, (width, height)) det_im = np.zeros((S, S, 3), dtype=np.uint8) det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im return det_im, scale def transform(data, center, output_size, scale, rotation): scale_ratio = scale rot = float(rotation) * np.pi / 180.0 #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio) t1 = trans.SimilarityTransform(scale=scale_ratio) cx = center[0] * scale_ratio cy = center[1] * scale_ratio t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy)) t3 = trans.SimilarityTransform(rotation=rot) t4 = trans.SimilarityTransform(translation=(output_size / 2, output_size / 2)) t = t1 + t2 + t3 + t4 M = t.params[0:2] cropped = cv2.warpAffine(data, M, (output_size, output_size), borderValue=0.0) return cropped, M def trans_points2d(pts, M): new_pts = np.zeros(shape=pts.shape, dtype=np.float32) for i in range(pts.shape[0]): pt = pts[i] new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) new_pt = np.dot(M, new_pt) #print('new_pt', new_pt.shape, new_pt) new_pts[i] = new_pt[0:2] return new_pts def trans_points3d(pts, M): scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1]) #print(scale) new_pts = np.zeros(shape=pts.shape, dtype=np.float32) for i in range(pts.shape[0]): pt = pts[i] new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) new_pt = np.dot(M, new_pt) #print('new_pt', new_pt.shape, new_pt) new_pts[i][0:2] = new_pt[0:2] new_pts[i][2] = pts[i][2] * scale return new_pts def trans_points(pts, M): if pts.shape[1] == 2: return trans_points2d(pts, M) else: return trans_points3d(pts, M)

insightface/web-demos/src_recognition/face_align.py/0

package com.example.nativedemo.entity; import com.example.nativedemo.enmus.MessageType; import lombok.Data; @Data public class MessageDto { private Long id; private Long userId; private String userName; private String message; private MessageType messageType; private String createUser; private String updateUser; }

mybatis-native-demo/src/main/java/com/example/nativedemo/entity/MessageDto.java/0

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mybatis-native-demo/target/classes/META-INF/native-image/org.postgresql/postgresql/42.6.0/reflect-config.json/0

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"name": "com.ecwid.consul.ConsulException", "condition": { "typeReachable": "com.ecwid.consul.v1.ConsulClient" }, "allDeclaredConstructors": true } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/com.ecwid.consul/consul-api/1.4.5/reflect-config.json/0

[ { "latest": true, "metadata-version": "3.21.12", "module": "com.google.protobuf:protobuf-java-util", "tested-versions": [ "3.21.12" ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/com.google.protobuf/protobuf-java-util/index.json/0

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"typeReachable": "com.hazelcast.client.impl.statistics.ClientStatisticsService" }, "name": "java.util.function.Supplier", "queryAllDeclaredMethods": true }, { "condition": { "typeReachable": "com.hazelcast.core.Hazelcast" }, "name": "java.util.function.Supplier", "queryAllDeclaredMethods": true }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceFactory" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.Node" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.internal.metrics.impl.MetricsCollectionCycle" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.internal.metrics.impl.MetricsRegistryImpl" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.internal.metrics.impl.MetricsService" }, "name": "java.util.function.Supplier", "queryAllDeclaredMethods": true }, { "condition": { "typeReachable": "com.hazelcast.internal.networking.nio.NioNetworking" }, "name": "java.util.function.Supplier", "allDeclaredFields": true }, { "condition": { "typeReachable": "com.hazelcast.spi.impl.NodeEngineImpl" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.spi.impl.operationservice.impl.InboundResponseHandlerSupplier" }, "name": "java.util.function.Supplier", "allDeclaredFields": true }, { "condition": { "typeReachable": "com.hazelcast.spi.impl.operationservice.impl.OperationServiceImpl" }, "name": "java.util.function.Supplier" }, { "condition": { "typeReachable": "com.hazelcast.core.Hazelcast" }, "name": "java.util.logging.LogManager", "methods": [ { "name": "getLoggingMXBean", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.core.Hazelcast" }, "name": "java.util.logging.LoggingMXBean", "queryAllPublicMethods": true }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.ClassLoaderUtil" }, "name": "javax.cache.Caching" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.ClassLoaderUtil" }, "name": "javax.cache.configuration.CompleteConfiguration" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.IOUtil$ClassLoaderAwareObjectInputStream" }, "name": "javax.cache.configuration.FactoryBuilder$SingletonFactory" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.IOUtil$ClassLoaderAwareObjectInputStream" }, "name": "javax.cache.expiry.AccessedExpiryPolicy" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.IOUtil$ClassLoaderAwareObjectInputStream" }, "name": "javax.cache.expiry.Duration" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.IOUtil$ClassLoaderAwareObjectInputStream" }, "name": "javax.cache.expiry.EternalExpiryPolicy" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.ClassLoaderUtil" }, "name": "javax.cache.integration.CacheLoaderException" }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.ClassLoaderUtil" }, "name": "javax.cache.integration.CacheWriterException" }, { "condition": { "typeReachable": "com.hazelcast.spi.impl.operationservice.impl.OperationRunnerImpl" }, "name": "javax.cache.management.CacheMXBean", "queryAllPublicMethods": true }, { "condition": { "typeReachable": "com.hazelcast.spi.impl.operationservice.impl.OperationRunnerImpl" }, "name": "javax.cache.management.CacheStatisticsMXBean", "queryAllPublicMethods": true }, { "condition": { "typeReachable": "com.hazelcast.internal.nio.ClassLoaderUtil" }, "name": "javax.cache.processor.EntryProcessorException" }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "javax.security.auth.x500.X500Principal", "fields": [ { "name": "thisX500Name" } ], "methods": [ { "name": "<init>", "parameterTypes": [ "sun.security.x509.X500Name" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.memory.impl.UnsafeUtil$1" }, "name": "sun.misc.Unsafe", "fields": [ { "name": "theUnsafe" } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.networking.nio.SelectorOptimizer" }, "name": "sun.nio.ch.SelectorImpl", "fields": [ { "name": "selectedKeys" } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.pkcs12.PKCS12KeyStore", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.pkcs12.PKCS12KeyStore$DualFormatPKCS12", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.DSA$SHA224withDSA", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.DSA$SHA256withDSA", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.JavaKeyStore$DualFormatJKS", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.JavaKeyStore$JKS", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.NativePRNG", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.SHA", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.SHA2$SHA224", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.SHA2$SHA256", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.SHA5$SHA384", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.SHA5$SHA512", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.X509Factory", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.provider.certpath.PKIXCertPathValidator", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.rsa.PSSParameters", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.rsa.RSAKeyFactory$Legacy", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.rsa.RSAPSSSignature", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.rsa.RSASignature$SHA224withRSA", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.rsa.RSASignature$SHA256withRSA", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.ssl.KeyManagerFactoryImpl$SunX509", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.ssl.SSLContextImpl$DefaultSSLContext", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.ssl.TrustManagerFactoryImpl$PKIXFactory", "methods": [ { "name": "<init>", "parameterTypes": [] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.util.ObjectIdentifier" }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.AuthorityInfoAccessExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.AuthorityKeyIdentifierExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.BasicConstraintsExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.CRLDistributionPointsExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.CertificateExtensions" }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.CertificatePoliciesExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.ExtendedKeyUsageExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.IssuerAlternativeNameExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.KeyUsageExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.NetscapeCertTypeExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.PrivateKeyUsageExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.SubjectAlternativeNameExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.internal.util.phonehome.PhoneHome" }, "name": "sun.security.x509.SubjectKeyIdentifierExtension", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.Boolean", "java.lang.Object" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientExecutorServiceProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientDurableExecutorServiceProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "com.hazelcast.config.DurableExecutorConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.config.DurableExecutorConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.txn.xa.XAResourceProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "com.hazelcast.config.ScheduledExecutorConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.config.ScheduledExecutorConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientReliableTopicProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "com.hazelcast.config.ReliableTopicConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.config.ReliableTopicConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.config.ClientReliableTopicConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.client.config.ClientReliableTopicConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientFlakeIdGeneratorProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "com.hazelcast.config.FlakeIdGeneratorConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.config.FlakeIdGeneratorConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.config.ClientFlakeIdGeneratorConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.client.config.ClientFlakeIdGeneratorConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientCardinalityEstimatorProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "com.hazelcast.config.CardinalityEstimatorConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.config.CardinalityEstimatorConfig" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientScheduledExecutorProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.client.impl.clientside.HazelcastClientInstanceImpl" }, "name": "com.hazelcast.client.impl.proxy.ClientPNCounterProxy", "methods": [ { "name": "<init>", "parameterTypes": [ "java.lang.String", "java.lang.String", "com.hazelcast.client.impl.spi.ClientContext" ] } ] }, { "condition": { "typeReachable": "com.hazelcast.instance.impl.HazelcastInstanceImpl" }, "name": "com.hazelcast.config.PNCounterConfig", "methods": [ { "name": "<init>", "parameterTypes": [] }, { "name": "<init>", "parameterTypes": [ "com.hazelcast.config.PNCounterConfig" ] } ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/com.hazelcast/hazelcast/5.2.1/reflect-config.json/0

[ { "condition": { "typeReachable": "com.zaxxer.hikari.pool.ProxyConnection$ClosedConnection" }, "interfaces": [ "java.sql.Connection" ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/com.zaxxer/HikariCP/5.0.1/proxy-config.json/0

{ "bundles": [], "resources": { "includes": [ { "condition": { "typeReachable": "io.jsonwebtoken.impl.lang.Services" }, "pattern": "\\QMETA-INF/services/io.jsonwebtoken.CompressionCodec\\E" }, { "condition": { "typeReachable": "io.jsonwebtoken.impl.DefaultJwtParserBuilder" }, "pattern": "\\QMETA-INF/services/io.jsonwebtoken.io.Deserializer\\E" }, { "condition": { "typeReachable": "io.jsonwebtoken.impl.lang.LegacyServices" }, "pattern": "\\QMETA-INF/services/io.jsonwebtoken.io.Serializer\\E" } ] } }

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/io.jsonwebtoken/jjwt-jackson/0.11.5/resource-config.json/0

{ "lambdaCapturingTypes": [], "types": [ { "condition": { "typeReachable": "io.netty.channel.epoll.EpollEventLoop" }, "name": "java.lang.String" }, { "condition": { "typeReachable": "io.netty.handler.codec.marshalling.MarshallingEncoder" }, "name": "java.lang.String" }, { "condition": { "typeReachable": "io.netty.handler.codec.marshalling.MarshallingEncoder" }, "name": "java.lang.String", "customTargetConstructorClass": "java.lang.Object" }, { "condition": { "typeReachable": "io.netty.handler.codec.marshalling.MarshallingEncoder" }, "name": "java.lang.String", "customTargetConstructorClass": "java.lang.String" }, { "condition": { "typeReachable": "io.netty.util.concurrent.FastThreadLocalRunnable" }, "name": "java.lang.String" } ] }

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/io.netty/netty-common/4.1.80.Final/serialization-config.json/0

[ "reflect-config.json" ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/io.opentelemetry/opentelemetry-exporter-zipkin/1.19.0/index.json/0

[ "resource-config.json", "serialization-config.json" ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/javax.cache/cache-api/1.1.1/index.json/0

[ { "latest": true, "metadata-version": "5.18.1", "module": "org.apache.activemq:activemq-broker", "tested-versions": [ "5.18.1" ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.apache.activemq/activemq-broker/index.json/0

[ { "name": "org.apache.http.impl.auth.BasicScheme", "condition": { "typeReachable": "org.apache.http.impl.client.BasicAuthCache" }, "methods": [ { "name": "<init>", "parameterTypes": [] } ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.apache.httpcomponents/httpclient/4.5.14/reflect-config.json/0

[ { "condition": { "typeReachable": "jakarta.mail.Session" }, "allDeclaredConstructors": true, "name": "com.sun.mail.smtp.SMTPTransport" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.image_gif" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.image_jpeg" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.message_rfc822" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.multipart_mixed" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.text_html" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.text_plain" }, { "condition": { "typeReachable": "jakarta.activation.MailcapCommandMap" }, "allDeclaredConstructors": true, "name": "com.sun.mail.handlers.text_xml" } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.eclipse.angus/jakarta.mail/1.0.0/reflect-config.json/0

[ { "name": "org.eclipse.paho.client.mqttv3.logging.JSR47Logger", "condition": { "typeReachable": "org.eclipse.paho.client.mqttv3.logging.LoggerFactory" }, "methods": [ { "name": "<init>", "parameterTypes": [ ] } ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.eclipse.paho/org.eclipse.paho.client.mqttv3/1.2.5/reflect-config.json/0

[ { "latest": true, "metadata-version": "2.1.12", "module": "org.hdrhistogram:HdrHistogram", "tested-versions": [ "2.1.12" ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.hdrhistogram/HdrHistogram/index.json/0

{ "resources": { "includes": [ { "pattern": "\\QMETA-INF/services/org.hibernate.boot.model.TypeContributor\\E", "condition": { "typeReachable": "org.hibernate.reactive.provider.impl.ReactiveEntityManagerFactoryBuilder" } }, { "pattern": "\\QMETA-INF/services/org.hibernate.engine.jdbc.dialect.spi.DialectResolver\\E", "condition": { "typeReachable": "org.hibernate.reactive.provider.service.NoJdbcEnvironmentInitiator$DialectBuilder" } }, { "pattern": "\\QMETA-INF/services/org.hibernate.integrator.spi.Integrator\\E", "condition": { "typeReachable": "org.hibernate.reactive.provider.impl.ReactiveEntityManagerFactoryBuilder" } }, { "pattern": "\\QMETA-INF/services/org.hibernate.service.spi.SessionFactoryServiceContributor\\E", "condition": { "typeReachable": "org.hibernate.reactive.session.impl.ReactiveSessionFactoryImpl" } } ] } }

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.hibernate.reactive/hibernate-reactive-core/2.0.0.Final/resource-config.json/0

[ { "latest": true, "metadata-version": "2.0.0.Final", "module": "org.jboss.spec.javax.servlet:jboss-servlet-api_4.0_spec", "tested-versions": [ "2.0.0.Final" ] } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.jboss.spec.javax.servlet/jboss-servlet-api_4.0_spec/index.json/0

[ "reflect-config.json" ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.jooq/jooq/3.18.2/index.json/0

{ "resources": { "includes": [ { "pattern": "\\Qproject.properties\\E", "condition": { "typeReachable": "org.mariadb.r2dbc.message.client.HandshakeResponse" } } ] }, "bundles": [ ] }

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.mariadb/r2dbc-mariadb/1.1.3/resource-config.json/0

[ { "name": "org.quartz.JobDataMap", "condition": { "typeReachable": "org.quartz.impl.jdbcjobstore.JobStoreSupport" } }, { "name": "org.quartz.utils.DirtyFlagMap", "condition": { "typeReachable": "org.quartz.impl.jdbcjobstore.JobStoreSupport" } }, { "name": "org.quartz.utils.StringKeyDirtyFlagMap", "condition": { "typeReachable": "org.quartz.impl.jdbcjobstore.JobStoreSupport" } }, { "name": "java.util.HashMap", "condition": { "typeReachable": "org.quartz.impl.jdbcjobstore.JobStoreSupport" } }, { "name": "java.lang.String", "condition": { "typeReachable": "org.quartz.impl.jdbcjobstore.JobStoreSupport" } } ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.quartz-scheduler/quartz/2.3.2/serialization-config.json/0

[ "reflect-config.json", "resource-config.json" ]

mybatis-native-demo/target/graalvm-reachability-metadata/5c2bbae17873953cfd284011e1cedc9ce1396f45/org.thymeleaf/thymeleaf/3.1.0.RC1/index.json/0

[ { "interfaces": [ "com.example.nativedemo.mapper.MessagesMapper" ] }, { "interfaces": [ "com.example.nativedemo.mapper.SysUserMapper" ] }, { "interfaces": [ "org.apache.ibatis.executor.statement.StatementHandler" ] }, { "interfaces": [ "org.apache.ibatis.executor.Executor" ] }, { "interfaces": [ "org.apache.ibatis.executor.resultset.ResultSetHandler" ] }, { "interfaces": [ "org.apache.ibatis.executor.parameter.ParameterHandler" ] } ]

mybatis-native-demo/target/spring-aot/main/resources/META-INF/native-image/com.example/mybatis-native-demo/proxy-config.json/0

package com.example.nativedemo.dao; import org.springframework.beans.factory.aot.AutowiredFieldValueResolver; import org.springframework.beans.factory.support.RegisteredBean; /** * Autowiring for {@link MessagesDao}. */ public class MessagesDao__Autowiring { /** * Apply the autowiring. */ public static MessagesDao apply(RegisteredBean registeredBean, MessagesDao instance) { AutowiredFieldValueResolver.forRequiredField("baseMapper").resolveAndSet(registeredBean, instance); return instance; } }

mybatis-native-demo/target/spring-aot/main/sources/com/example/nativedemo/dao/MessagesDao__Autowiring.java/0

package org.springframework.boot.autoconfigure.jdbc; import org.springframework.beans.factory.aot.BeanInstanceSupplier; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; import org.springframework.jdbc.core.JdbcTemplate; import org.springframework.jdbc.core.namedparam.NamedParameterJdbcTemplate; /** * Bean definitions for {@link NamedParameterJdbcTemplateConfiguration}. */ public class NamedParameterJdbcTemplateConfiguration__BeanDefinitions { /** * Get the bean definition for 'namedParameterJdbcTemplateConfiguration'. */ public static BeanDefinition getNamedParameterJdbcTemplateConfigurationBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(NamedParameterJdbcTemplateConfiguration.class); beanDefinition.setInstanceSupplier(NamedParameterJdbcTemplateConfiguration::new); return beanDefinition; } /** * Get the bean instance supplier for 'namedParameterJdbcTemplate'. */ private static BeanInstanceSupplier<NamedParameterJdbcTemplate> getNamedParameterJdbcTemplateInstanceSupplier( ) { return BeanInstanceSupplier.<NamedParameterJdbcTemplate>forFactoryMethod(NamedParameterJdbcTemplateConfiguration.class, "namedParameterJdbcTemplate", JdbcTemplate.class) .withGenerator((registeredBean, args) -> registeredBean.getBeanFactory().getBean(NamedParameterJdbcTemplateConfiguration.class).namedParameterJdbcTemplate(args.get(0))); } /** * Get the bean definition for 'namedParameterJdbcTemplate'. */ public static BeanDefinition getNamedParameterJdbcTemplateBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(NamedParameterJdbcTemplate.class); beanDefinition.setPrimary(true); beanDefinition.setDependsOn("dataSourceScriptDatabaseInitializer"); beanDefinition.setInstanceSupplier(getNamedParameterJdbcTemplateInstanceSupplier()); return beanDefinition; } }

mybatis-native-demo/target/spring-aot/main/sources/org/springframework/boot/autoconfigure/jdbc/NamedParameterJdbcTemplateConfiguration__BeanDefinitions.java/0

package org.springframework.boot.autoconfigure.web; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; /** * Bean definitions for {@link ServerProperties}. */ public class ServerProperties__BeanDefinitions { /** * Get the bean definition for 'serverProperties'. */ public static BeanDefinition getServerPropertiesBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(ServerProperties.class); beanDefinition.setInstanceSupplier(ServerProperties::new); return beanDefinition; } }

mybatis-native-demo/target/spring-aot/main/sources/org/springframework/boot/autoconfigure/web/ServerProperties__BeanDefinitions.java/0

package org.springframework.boot.context.properties; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; /** * Bean definitions for {@link BoundConfigurationProperties}. */ public class BoundConfigurationProperties__BeanDefinitions { /** * Get the bean definition for 'boundConfigurationProperties'. */ public static BeanDefinition getBoundConfigurationPropertiesBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(BoundConfigurationProperties.class); beanDefinition.setRole(BeanDefinition.ROLE_INFRASTRUCTURE); beanDefinition.setInstanceSupplier(BoundConfigurationProperties::new); return beanDefinition; } }

mybatis-native-demo/target/spring-aot/main/sources/org/springframework/boot/context/properties/BoundConfigurationProperties__BeanDefinitions.java/0

package org.springframework.cloud.client.loadbalancer; import org.springframework.beans.factory.aot.BeanInstanceSupplier; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; import org.springframework.cloud.commons.config.DefaultsBindHandlerAdvisor; /** * Bean definitions for {@link LoadBalancerDefaultMappingsProviderAutoConfiguration}. */ public class LoadBalancerDefaultMappingsProviderAutoConfiguration__BeanDefinitions { /** * Get the bean definition for 'loadBalancerDefaultMappingsProviderAutoConfiguration'. */ public static BeanDefinition getLoadBalancerDefaultMappingsProviderAutoConfigurationBeanDefinition( ) { RootBeanDefinition beanDefinition = new RootBeanDefinition(LoadBalancerDefaultMappingsProviderAutoConfiguration.class); beanDefinition.setInstanceSupplier(LoadBalancerDefaultMappingsProviderAutoConfiguration::new); return beanDefinition; } /** * Get the bean instance supplier for 'loadBalancerClientsDefaultsMappingsProvider'. */ private static BeanInstanceSupplier<DefaultsBindHandlerAdvisor.MappingsProvider> getLoadBalancerClientsDefaultsMappingsProviderInstanceSupplier( ) { return BeanInstanceSupplier.<DefaultsBindHandlerAdvisor.MappingsProvider>forFactoryMethod(LoadBalancerDefaultMappingsProviderAutoConfiguration.class, "loadBalancerClientsDefaultsMappingsProvider") .withGenerator((registeredBean) -> registeredBean.getBeanFactory().getBean(LoadBalancerDefaultMappingsProviderAutoConfiguration.class).loadBalancerClientsDefaultsMappingsProvider()); } /** * Get the bean definition for 'loadBalancerClientsDefaultsMappingsProvider'. */ public static BeanDefinition getLoadBalancerClientsDefaultsMappingsProviderBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(DefaultsBindHandlerAdvisor.MappingsProvider.class); beanDefinition.setInstanceSupplier(getLoadBalancerClientsDefaultsMappingsProviderInstanceSupplier()); return beanDefinition; } }

mybatis-native-demo/target/spring-aot/main/sources/org/springframework/cloud/client/loadbalancer/LoadBalancerDefaultMappingsProviderAutoConfiguration__BeanDefinitions.java/0

package org.springframework.transaction.annotation; import org.springframework.beans.factory.aot.BeanInstanceSupplier; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.InstanceSupplier; import org.springframework.beans.factory.support.RootBeanDefinition; import org.springframework.transaction.interceptor.BeanFactoryTransactionAttributeSourceAdvisor; import org.springframework.transaction.interceptor.TransactionAttributeSource; import org.springframework.transaction.interceptor.TransactionInterceptor; /** * Bean definitions for {@link ProxyTransactionManagementConfiguration}. */ public class ProxyTransactionManagementConfiguration__BeanDefinitions { /** * Get the bean definition for 'proxyTransactionManagementConfiguration'. */ public static BeanDefinition getProxyTransactionManagementConfigurationBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(ProxyTransactionManagementConfiguration.class); beanDefinition.setRole(BeanDefinition.ROLE_INFRASTRUCTURE); InstanceSupplier<ProxyTransactionManagementConfiguration> instanceSupplier = InstanceSupplier.using(ProxyTransactionManagementConfiguration::new); instanceSupplier = instanceSupplier.andThen(ProxyTransactionManagementConfiguration__Autowiring::apply); beanDefinition.setInstanceSupplier(instanceSupplier); return beanDefinition; } /** * Get the bean instance supplier for 'org.springframework.transaction.config.internalTransactionAdvisor'. */ private static BeanInstanceSupplier<BeanFactoryTransactionAttributeSourceAdvisor> getInternalTransactionAdvisorInstanceSupplier( ) { return BeanInstanceSupplier.<BeanFactoryTransactionAttributeSourceAdvisor>forFactoryMethod(ProxyTransactionManagementConfiguration.class, "transactionAdvisor", TransactionAttributeSource.class, TransactionInterceptor.class) .withGenerator((registeredBean, args) -> registeredBean.getBeanFactory().getBean(ProxyTransactionManagementConfiguration.class).transactionAdvisor(args.get(0), args.get(1))); } /** * Get the bean definition for 'internalTransactionAdvisor'. */ public static BeanDefinition getInternalTransactionAdvisorBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(BeanFactoryTransactionAttributeSourceAdvisor.class); beanDefinition.setRole(BeanDefinition.ROLE_INFRASTRUCTURE); beanDefinition.setInstanceSupplier(getInternalTransactionAdvisorInstanceSupplier()); return beanDefinition; } /** * Get the bean instance supplier for 'transactionAttributeSource'. */ private static BeanInstanceSupplier<TransactionAttributeSource> getTransactionAttributeSourceInstanceSupplier( ) { return BeanInstanceSupplier.<TransactionAttributeSource>forFactoryMethod(ProxyTransactionManagementConfiguration.class, "transactionAttributeSource") .withGenerator((registeredBean) -> registeredBean.getBeanFactory().getBean(ProxyTransactionManagementConfiguration.class).transactionAttributeSource()); } /** * Get the bean definition for 'transactionAttributeSource'. */ public static BeanDefinition getTransactionAttributeSourceBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(TransactionAttributeSource.class); beanDefinition.setRole(BeanDefinition.ROLE_INFRASTRUCTURE); beanDefinition.setInstanceSupplier(getTransactionAttributeSourceInstanceSupplier()); return beanDefinition; } /** * Get the bean instance supplier for 'transactionInterceptor'. */ private static BeanInstanceSupplier<TransactionInterceptor> getTransactionInterceptorInstanceSupplier( ) { return BeanInstanceSupplier.<TransactionInterceptor>forFactoryMethod(ProxyTransactionManagementConfiguration.class, "transactionInterceptor", TransactionAttributeSource.class) .withGenerator((registeredBean, args) -> registeredBean.getBeanFactory().getBean(ProxyTransactionManagementConfiguration.class).transactionInterceptor(args.get(0))); } /** * Get the bean definition for 'transactionInterceptor'. */ public static BeanDefinition getTransactionInterceptorBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(TransactionInterceptor.class); beanDefinition.setRole(BeanDefinition.ROLE_INFRASTRUCTURE); beanDefinition.setInstanceSupplier(getTransactionInterceptorInstanceSupplier()); return beanDefinition; } }

mybatis-native-demo/target/spring-aot/main/sources/org/springframework/transaction/annotation/ProxyTransactionManagementConfiguration__BeanDefinitions.java/0

NACOS_VERSION=v2.3.1

nacos-docker/example/.env/0

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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:11:05,619 INFO DISTRO:|0|0|0| 2024-06-22 00:12:05,620 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:12:05,620 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:12:05,620 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:13:05,620 INFO DISTRO:|0|0|0| 2024-06-22 00:14:05,620 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:14:05,620 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:16:05,621 INFO DISTRO:|0|0|0| 2024-06-22 00:17:05,621 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:17:05,621 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 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pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:18:05,621 INFO DISTRO:|0|0|0| 2024-06-22 00:19:05,622 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 00:19:05,622 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 00:19:05,622 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:19:05,622 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 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pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:21:05,622 INFO DISTRO:|0|0|0| 2024-06-22 00:22:05,623 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:22:05,623 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:22:05,623 INFO DISTRO:|0|0|0| 2024-06-22 00:23:05,623 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:23:05,623 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:23:05,623 INFO DISTRO:|0|0|0| 2024-06-22 00:24:05,623 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:24:05,623 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:24:05,623 INFO DISTRO:|0|0|0| 2024-06-22 00:25:05,623 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:25:05,624 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:25:05,624 INFO DISTRO:|0|0|0| 2024-06-22 00:26:05,624 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:26:05,624 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:26:05,624 INFO DISTRO:|0|0|0| 2024-06-22 00:27:05,624 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:27:05,624 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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00:31:05,625 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:34:05,626 INFO DISTRO:|0|0|0| 2024-06-22 00:35:05,626 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:35:05,626 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:35:05,626 INFO DISTRO:|0|0|0| 2024-06-22 00:36:05,627 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:36:05,627 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:36:05,627 INFO DISTRO:|0|0|0| 2024-06-22 00:37:05,627 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:37:05,627 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:37:05,627 INFO DISTRO:|0|0|0| 2024-06-22 00:38:05,627 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:38:05,627 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:38:05,627 INFO DISTRO:|0|0|0| 2024-06-22 00:39:05,627 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 00:39:05,628 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 00:39:05,628 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:39:05,628 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:39:05,628 INFO DISTRO:|0|0|0| 2024-06-22 00:40:05,628 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:40:05,628 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:40:05,628 INFO DISTRO:|0|0|0| 2024-06-22 00:41:05,628 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:41:05,628 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:41:05,628 INFO DISTRO:|0|0|0| 2024-06-22 00:42:05,628 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:42:05,628 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:42:05,628 INFO DISTRO:|0|0|0| 2024-06-22 00:43:05,629 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:43:05,629 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:43:05,629 INFO DISTRO:|0|0|0| 2024-06-22 00:44:05,629 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:44:05,629 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:50:05,631 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:56:05,633 INFO DISTRO:|0|0|0| 2024-06-22 00:57:05,633 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:57:05,634 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 00:58:05,634 INFO DISTRO:|0|0|0| 2024-06-22 00:59:05,634 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 00:59:05,634 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 00:59:05,634 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 00:59:05,634 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:02:05,635 INFO DISTRO:|0|0|0| 2024-06-22 01:03:05,635 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:03:05,636 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, 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pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:08:05,637 INFO DISTRO:|0|0|0| 2024-06-22 01:09:05,638 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 01:09:05,638 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 01:09:05,638 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:09:05,638 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:09:05,638 INFO DISTRO:|0|0|0| 2024-06-22 01:10:05,638 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:10:05,638 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending 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pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:11:05,638 INFO DISTRO:|0|0|0| 2024-06-22 01:12:05,638 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:12:05,639 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:12:05,639 INFO DISTRO:|0|0|0| 2024-06-22 01:13:05,639 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:13:05,639 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:13:05,639 INFO DISTRO:|0|0|0| 2024-06-22 01:14:05,639 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:14:05,639 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:14:05,639 INFO DISTRO:|0|0|0| 2024-06-22 01:15:05,640 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:15:05,640 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:15:05,640 INFO DISTRO:|0|0|0| 2024-06-22 01:16:05,640 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:16:05,640 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:16:05,640 INFO DISTRO:|0|0|0| 2024-06-22 01:17:05,640 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:17:05,640 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:18:05,641 INFO DISTRO:|0|0|0| 2024-06-22 01:19:05,641 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 01:19:05,641 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 01:19:05,641 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:19:05,641 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:20:05,641 INFO DISTRO:|0|0|0| 2024-06-22 01:21:05,642 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:21:05,642 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:26:05,643 INFO DISTRO:|0|0|0| 2024-06-22 01:27:05,643 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:27:05,644 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 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pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:28:05,644 INFO DISTRO:|0|0|0| 2024-06-22 01:29:05,644 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 01:29:05,644 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 01:29:05,644 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:29:05,644 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:30:05,644 INFO DISTRO:|0|0|0| 2024-06-22 01:31:05,644 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:31:05,645 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:31:05,645 INFO DISTRO:|0|0|0| 2024-06-22 01:32:05,645 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:32:05,645 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:33:05,645 INFO DISTRO:|0|0|0| 2024-06-22 01:34:05,645 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:34:05,645 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:40:05,647 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:46:05,649 INFO DISTRO:|0|0|0| 2024-06-22 01:47:05,649 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:47:05,649 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:48:05,650 INFO DISTRO:|0|0|0| 2024-06-22 01:49:05,650 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 01:49:05,650 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 01:49:05,650 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:49:05,650 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:52:05,652 INFO DISTRO:|0|0|0| 2024-06-22 01:53:05,652 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:53:05,652 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:53:05,652 INFO DISTRO:|0|0|0| 2024-06-22 01:54:05,652 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:54:05,652 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 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naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 01:57:05,653 INFO DISTRO:|0|0|0| 2024-06-22 01:58:05,654 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 01:58:05,654 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, 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tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:03:05,657 INFO DISTRO:|0|0|0| 2024-06-22 02:04:05,657 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:04:05,657 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:04:05,657 INFO DISTRO:|0|0|0| 2024-06-22 02:05:05,657 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:05:05,657 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:05:05,658 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:06:05,658 INFO DISTRO:|0|0|0| 2024-06-22 02:07:05,658 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:07:05,658 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:10:05,659 INFO DISTRO:|0|0|0| 2024-06-22 02:11:05,659 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:11:05,660 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:13:05,660 INFO DISTRO:|0|0|0| 2024-06-22 02:14:05,660 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:14:05,661 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:14:05,661 INFO DISTRO:|0|0|0| 2024-06-22 02:15:05,662 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:15:05,662 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending 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02:24:05,670 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 02:29:05,672 INFO DISTRO:|0|0|0| 2024-06-22 02:30:05,672 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 02:30:05,672 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:00:05,685 INFO DISTRO:|0|0|0| 2024-06-22 03:01:05,686 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:01:05,686 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:04:05,688 INFO DISTRO:|0|0|0| 2024-06-22 03:05:05,688 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:05:05,688 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending 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pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:06:05,689 INFO DISTRO:|0|0|0| 2024-06-22 03:07:05,689 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:07:05,689 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:07:05,689 INFO DISTRO:|0|0|0| 2024-06-22 03:08:05,689 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:08:05,689 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:08:05,689 INFO 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naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:09:05,690 INFO DISTRO:|0|0|0| 2024-06-22 03:10:05,690 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:10:05,690 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:20:05,696 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:26:05,699 INFO DISTRO:|0|0|0| 2024-06-22 03:27:05,700 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:27:05,700 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:32:05,702 INFO DISTRO:|0|0|0| 2024-06-22 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pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:36:05,704 INFO DISTRO:|0|0|0| 2024-06-22 03:37:05,704 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:37:05,704 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 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pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:38:05,704 INFO DISTRO:|0|0|0| 2024-06-22 03:39:05,704 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 03:39:05,705 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 03:39:05,705 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:39:05,705 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 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pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:41:05,706 INFO DISTRO:|0|0|0| 2024-06-22 03:42:05,706 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:42:05,707 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:42:05,707 INFO DISTRO:|0|0|0| 2024-06-22 03:43:05,707 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:43:05,707 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:43:05,707 INFO DISTRO:|0|0|0| 2024-06-22 03:44:05,707 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:44:05,707 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:44:05,708 INFO DISTRO:|0|0|0| 2024-06-22 03:45:05,708 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:45:05,709 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:45:05,709 INFO DISTRO:|0|0|0| 2024-06-22 03:46:05,709 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:46:05,709 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:46:05,710 INFO DISTRO:|0|0|0| 2024-06-22 03:47:05,710 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:47:05,710 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:47:05,711 INFO DISTRO:|0|0|0| 2024-06-22 03:48:05,711 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:48:05,711 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:48:05,712 INFO DISTRO:|0|0|0| 2024-06-22 03:49:05,712 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 03:49:05,713 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 03:49:05,713 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:49:05,713 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:50:05,714 INFO DISTRO:|0|0|0| 2024-06-22 03:51:05,714 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:51:05,714 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:51:05,714 INFO DISTRO:|0|0|0| 2024-06-22 03:52:05,714 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:52:05,715 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:52:05,715 INFO DISTRO:|0|0|0| 2024-06-22 03:53:05,715 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:53:05,715 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:54:05,715 INFO DISTRO:|0|0|0| 2024-06-22 03:55:05,715 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:55:05,716 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:55:05,716 INFO DISTRO:|0|0|0| 2024-06-22 03:56:05,716 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:56:05,716 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:56:05,716 INFO DISTRO:|0|0|0| 2024-06-22 03:57:05,716 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:57:05,716 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:57:05,716 INFO DISTRO:|0|0|0| 2024-06-22 03:58:05,716 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:58:05,717 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:58:05,717 INFO DISTRO:|0|0|0| 2024-06-22 03:59:05,717 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 03:59:05,717 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 03:59:05,717 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 03:59:05,717 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 03:59:05,717 INFO DISTRO:|0|0|0| 2024-06-22 04:00:05,717 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:00:05,717 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:00:05,717 INFO DISTRO:|0|0|0| 2024-06-22 04:01:05,717 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:01:05,718 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, 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naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:02:05,718 INFO DISTRO:|0|0|0| 2024-06-22 04:03:05,718 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:03:05,718 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:03:05,718 INFO DISTRO:|0|0|0| 2024-06-22 04:04:05,719 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:04:05,719 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:10:05,722 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:16:05,724 INFO DISTRO:|0|0|0| 2024-06-22 04:17:05,724 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:17:05,724 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:18:05,724 INFO DISTRO:|0|0|0| 2024-06-22 04:19:05,725 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 04:19:05,725 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 04:19:05,725 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:19:05,725 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 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04:23:05,726 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:23:05,726 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, 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DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 04:29:05,728 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:29:05,728 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:34:05,730 INFO DISTRO:|0|0|0| 2024-06-22 04:35:05,730 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:35:05,730 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:35:05,730 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:36:05,731 INFO DISTRO:|0|0|0| 2024-06-22 04:37:05,731 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:37:05,731 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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04:41:05,733 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:47:05,735 INFO DISTRO:|0|0|0| 2024-06-22 04:48:05,735 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:48:05,735 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:48:05,735 INFO DISTRO:|0|0|0| 2024-06-22 04:49:05,735 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 04:49:05,735 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 04:49:05,735 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:49:05,735 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 04:53:05,737 INFO DISTRO:|0|0|0| 2024-06-22 04:54:05,737 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 04:54:05,737 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:00:05,739 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 05:06:05,741 INFO DISTRO:|0|0|0| 2024-06-22 05:07:05,741 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:07:05,742 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 05:12:05,743 INFO DISTRO:|0|0|0| 2024-06-22 05:13:05,744 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:13:05,744 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 05:24:05,748 INFO DISTRO:|0|0|0| 2024-06-22 05:25:05,748 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:25:05,748 INFO Task worker status: naming_0%32, 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 05:26:05,748 INFO DISTRO:|0|0|0| 2024-06-22 05:27:05,749 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:27:05,749 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 05:30:05,750 INFO DISTRO:|0|0|0| 2024-06-22 05:31:05,750 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:31:05,750 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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05:44:05,755 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 05:50:05,757 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 06:09:05,763 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:09:05,763 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:20:05,767 INFO DISTRO:|0|0|0| 2024-06-22 06:21:05,768 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:21:05,768 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:24:05,769 INFO DISTRO:|0|0|0| 2024-06-22 06:25:05,769 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:25:05,769 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:27:05,770 INFO DISTRO:|0|0|0| 2024-06-22 06:28:05,770 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:28:05,770 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:28:05,770 INFO DISTRO:|0|0|0| 2024-06-22 06:29:05,770 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 06:29:05,770 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 06:29:05,770 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:29:05,770 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:29:05,770 INFO DISTRO:|0|0|0| 2024-06-22 06:30:05,771 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:30:05,771 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:30:05,771 INFO DISTRO:|0|0|0| 2024-06-22 06:31:05,771 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:31:05,771 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, 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naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:32:05,771 INFO DISTRO:|0|0|0| 2024-06-22 06:33:05,772 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:33:05,772 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:33:05,772 INFO DISTRO:|0|0|0| 2024-06-22 06:34:05,772 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:34:05,772 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:34:05,772 INFO DISTRO:|0|0|0| 2024-06-22 06:35:05,772 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:35:05,772 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:35:05,773 INFO DISTRO:|0|0|0| 2024-06-22 06:36:05,773 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:36:05,773 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:36:05,773 INFO DISTRO:|0|0|0| 2024-06-22 06:37:05,773 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:37:05,773 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:37:05,773 INFO DISTRO:|0|0|0| 2024-06-22 06:38:05,773 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:38:05,774 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:38:05,774 INFO DISTRO:|0|0|0| 2024-06-22 06:39:05,774 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 06:39:05,774 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 06:39:05,774 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:39:05,774 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:39:05,774 INFO DISTRO:|0|0|0| 2024-06-22 06:40:05,774 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:40:05,774 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:41:05,775 INFO DISTRO:|0|0|0| 2024-06-22 06:42:05,775 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:42:05,775 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:42:05,775 INFO DISTRO:|0|0|0| 2024-06-22 06:43:05,775 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:43:05,775 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:46:05,776 INFO DISTRO:|0|0|0| 2024-06-22 06:47:05,777 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:47:05,777 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:52:05,779 INFO DISTRO:|0|0|0| 2024-06-22 06:53:05,779 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:53:05,779 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, 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pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:56:05,780 INFO DISTRO:|0|0|0| 2024-06-22 06:57:05,780 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:57:05,780 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 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pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 06:58:05,781 INFO DISTRO:|0|0|0| 2024-06-22 06:59:05,781 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 06:59:05,781 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 06:59:05,781 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 06:59:05,781 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 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pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:01:05,782 INFO DISTRO:|0|0|0| 2024-06-22 07:02:05,782 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:02:05,782 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:02:05,782 INFO DISTRO:|0|0|0| 2024-06-22 07:03:05,782 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:03:05,782 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:03:05,782 INFO DISTRO:|0|0|0| 2024-06-22 07:04:05,783 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:04:05,783 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:04:05,783 INFO DISTRO:|0|0|0| 2024-06-22 07:05:05,783 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:05:05,783 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:05:05,783 INFO 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:10:05,785 INFO DISTRO:|0|0|0| 2024-06-22 07:11:05,785 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:11:05,785 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:16:05,787 INFO DISTRO:|0|0|0| 2024-06-22 07:17:05,787 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:17:05,787 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:17:05,787 INFO DISTRO:|0|0|0| 2024-06-22 07:18:05,787 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:18:05,787 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:18:05,787 INFO DISTRO:|0|0|0| 2024-06-22 07:19:05,788 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 07:19:05,788 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 07:19:05,788 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:19:05,788 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:20:05,788 INFO DISTRO:|0|0|0| 2024-06-22 07:21:05,788 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:21:05,789 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, 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07:24:05,790 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:30:05,792 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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07:37:05,794 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:54:05,800 INFO DISTRO:|0|0|0| 2024-06-22 07:55:05,800 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:55:05,800 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:55:05,800 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 07:56:05,801 INFO DISTRO:|0|0|0| 2024-06-22 07:57:05,801 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 07:57:05,801 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 08:08:05,805 INFO 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08:14:05,806 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 08:20:05,808 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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08:27:05,810 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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08:33:05,812 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 08:33:05,812 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 08:44:05,815 INFO DISTRO:|0|0|0| 2024-06-22 08:45:05,815 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 08:45:05,815 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 08:45:05,815 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 08:46:05,815 INFO DISTRO:|0|0|0| 2024-06-22 08:47:05,815 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 08:47:05,816 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 08:50:05,816 INFO DISTRO:|0|0|0| 2024-06-22 08:51:05,817 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 08:51:05,817 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:03:05,820 INFO DISTRO:|0|0|0| 2024-06-22 09:04:05,820 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:04:05,820 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:09:05,822 INFO DISTRO:|0|0|0| 2024-06-22 09:10:05,822 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:10:05,822 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:29:05,828 INFO DISTRO:|0|0|0| 2024-06-22 09:30:05,828 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:30:05,828 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending 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pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:35:05,830 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:36:05,830 INFO DISTRO:|0|0|0| 2024-06-22 09:37:05,830 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:37:05,830 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:37:05,830 INFO DISTRO:|0|0|0| 2024-06-22 09:38:05,830 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:38:05,830 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:40:05,831 INFO DISTRO:|0|0|0| 2024-06-22 09:41:05,831 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:41:05,831 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:42:05,831 INFO DISTRO:|0|0|0| 2024-06-22 09:43:05,832 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:43:05,832 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, 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pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:46:05,833 INFO DISTRO:|0|0|0| 2024-06-22 09:47:05,833 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:47:05,833 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:47:05,833 INFO DISTRO:|0|0|0| 2024-06-22 09:48:05,833 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:48:05,833 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:48:05,833 INFO DISTRO:|0|0|0| 2024-06-22 09:49:05,833 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 09:49:05,833 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 09:49:05,833 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:49:05,833 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:49:05,833 INFO DISTRO:|0|0|0| 2024-06-22 09:50:05,834 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:50:05,834 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:50:05,834 INFO DISTRO:|0|0|0| 2024-06-22 09:51:05,834 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:51:05,834 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:51:05,834 INFO DISTRO:|0|0|0| 2024-06-22 09:52:05,834 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:52:05,834 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:53:05,835 INFO DISTRO:|0|0|0| 2024-06-22 09:54:05,835 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:54:05,835 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:54:05,835 INFO DISTRO:|0|0|0| 2024-06-22 09:55:05,835 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:55:05,835 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:55:05,835 INFO DISTRO:|0|0|0| 2024-06-22 09:56:05,835 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:56:05,835 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:56:05,836 INFO DISTRO:|0|0|0| 2024-06-22 09:57:05,836 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:57:05,836 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:57:05,836 INFO DISTRO:|0|0|0| 2024-06-22 09:58:05,836 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:58:05,836 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:58:05,836 INFO DISTRO:|0|0|0| 2024-06-22 09:59:05,836 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 09:59:05,836 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 09:59:05,836 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 09:59:05,836 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 09:59:05,836 INFO DISTRO:|0|0|0| 2024-06-22 10:00:05,837 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:00:05,837 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:01:05,837 INFO DISTRO:|0|0|0| 2024-06-22 10:02:05,837 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:02:05,837 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:02:05,837 INFO DISTRO:|0|0|0| 2024-06-22 10:03:05,838 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:03:05,838 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:03:05,838 INFO DISTRO:|0|0|0| 2024-06-22 10:04:05,838 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:04:05,838 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:04:05,838 INFO DISTRO:|0|0|0| 2024-06-22 10:05:05,838 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:05:05,838 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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10:07:05,839 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:24:05,844 INFO DISTRO:|0|0|0| 2024-06-22 10:25:05,844 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:25:05,844 INFO Task worker status: naming_0%32, 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10:31:05,846 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:37:05,848 INFO DISTRO:|0|0|0| 2024-06-22 10:38:05,848 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:38:05,848 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 10:38:05,848 INFO 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 10:50:05,854 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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10:57:05,856 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 11:14:05,864 INFO DISTRO:|0|0|0| 2024-06-22 11:15:05,864 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 11:15:05,865 INFO Task worker status: naming_0%32, 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 11:16:05,865 INFO DISTRO:|0|0|0| 2024-06-22 11:17:05,865 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 11:17:05,865 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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11:21:05,869 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 11:28:05,872 INFO DISTRO:|0|0|0| 2024-06-22 11:29:05,872 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 11:29:05,872 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 11:29:05,872 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 11:29:05,872 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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11:34:05,874 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 11:54:05,884 INFO DISTRO:|0|0|0| 2024-06-22 11:55:05,884 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 11:55:05,884 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending 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tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:03:05,887 INFO DISTRO:|0|0|0| 2024-06-22 12:04:05,887 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:04:05,887 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:04:05,887 INFO DISTRO:|0|0|0| 2024-06-22 12:05:05,887 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:05:05,888 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:05:05,888 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:06:05,888 INFO DISTRO:|0|0|0| 2024-06-22 12:07:05,888 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:07:05,888 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:08:05,889 INFO DISTRO:|0|0|0| 2024-06-22 12:09:05,889 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 12:09:05,889 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 12:09:05,889 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:09:05,889 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:10:05,890 INFO DISTRO:|0|0|0| 2024-06-22 12:11:05,890 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:11:05,891 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:18:05,893 INFO DISTRO:|0|0|0| 2024-06-22 12:19:05,893 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 12:19:05,893 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 12:19:05,893 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:19:05,893 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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12:24:05,895 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:29:05,897 INFO DISTRO:|0|0|0| 2024-06-22 12:30:05,897 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:30:05,897 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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12:37:05,899 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:42:05,901 INFO DISTRO:|0|0|0| 2024-06-22 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DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 12:49:05,907 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:49:05,907 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 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tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:50:05,907 INFO DISTRO:|0|0|0| 2024-06-22 12:51:05,907 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:51:05,907 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:51:05,907 INFO DISTRO:|0|0|0| 2024-06-22 12:52:05,908 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:52:05,908 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:52:05,908 INFO DISTRO:|0|0|0| 2024-06-22 12:53:05,908 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:53:05,908 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:53:05,908 INFO DISTRO:|0|0|0| 2024-06-22 12:54:05,908 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:54:05,908 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:54:05,908 INFO DISTRO:|0|0|0| 2024-06-22 12:55:05,909 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:55:05,909 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:55:05,909 INFO DISTRO:|0|0|0| 2024-06-22 12:56:05,909 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:56:05,909 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:56:05,909 INFO DISTRO:|0|0|0| 2024-06-22 12:57:05,909 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:57:05,909 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:57:05,909 INFO DISTRO:|0|0|0| 2024-06-22 12:58:05,909 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:58:05,910 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:58:05,910 INFO DISTRO:|0|0|0| 2024-06-22 12:59:05,910 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 12:59:05,910 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 12:59:05,910 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 12:59:05,910 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 12:59:05,910 INFO DISTRO:|0|0|0| 2024-06-22 13:00:05,910 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:00:05,910 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:00:05,910 INFO DISTRO:|0|0|0| 2024-06-22 13:01:05,911 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:01:05,911 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:01:05,911 INFO DISTRO:|0|0|0| 2024-06-22 13:02:05,911 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:02:05,911 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:02:05,911 INFO DISTRO:|0|0|0| 2024-06-22 13:03:05,911 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:03:05,911 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:03:05,911 INFO DISTRO:|0|0|0| 2024-06-22 13:04:05,912 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:04:05,912 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:04:05,912 INFO DISTRO:|0|0|0| 2024-06-22 13:05:05,912 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:05:05,912 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:05:05,912 INFO DISTRO:|0|0|0| 2024-06-22 13:06:05,912 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:06:05,912 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:06:05,912 INFO DISTRO:|0|0|0| 2024-06-22 13:07:05,913 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:07:05,913 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:07:05,913 INFO DISTRO:|0|0|0| 2024-06-22 13:08:05,913 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:08:05,913 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:08:05,913 INFO 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naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:09:05,914 INFO DISTRO:|0|0|0| 2024-06-22 13:10:05,914 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:10:05,915 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:13:05,917 INFO DISTRO:|0|0|0| 2024-06-22 13:14:05,917 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:14:05,917 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:19:05,922 INFO DISTRO:|0|0|0| 2024-06-22 13:20:05,922 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:20:05,922 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:50:05,937 INFO DISTRO:|0|0|0| 2024-06-22 13:51:05,937 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:51:05,937 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:57:05,939 INFO DISTRO:|0|0|0| 2024-06-22 13:58:05,939 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:58:05,939 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:58:05,939 INFO DISTRO:|0|0|0| 2024-06-22 13:59:05,940 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 13:59:05,940 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 13:59:05,940 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 13:59:05,940 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 13:59:05,940 INFO DISTRO:|0|0|0| 2024-06-22 14:00:05,940 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 14:00:05,940 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 14:00:05,940 INFO DISTRO:|0|0|0| 2024-06-22 14:01:05,940 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 14:01:05,941 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 14:10:05,944 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 14:34:05,951 INFO DISTRO:|0|0|0| 2024-06-22 14:35:05,952 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 14:35:05,952 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 14:35:05,952 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 14:36:05,952 INFO DISTRO:|0|0|0| 2024-06-22 14:37:05,952 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 14:37:05,952 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 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14:54:05,958 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:00:05,960 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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15:07:05,962 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:12:05,963 INFO DISTRO:|0|0|0| 2024-06-22 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pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:16:05,964 INFO DISTRO:|0|0|0| 2024-06-22 15:17:05,965 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:17:05,965 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 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DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 15:19:05,965 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:19:05,965 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:19:05,965 INFO DISTRO:|0|0|0| 2024-06-22 15:20:05,965 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:20:05,966 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending 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pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:21:05,966 INFO DISTRO:|0|0|0| 2024-06-22 15:22:05,966 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:22:05,966 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:22:05,966 INFO DISTRO:|0|0|0| 2024-06-22 15:23:05,967 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:23:05,967 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:23:05,967 INFO DISTRO:|0|0|0| 2024-06-22 15:24:05,967 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:24:05,967 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:24:05,967 INFO DISTRO:|0|0|0| 2024-06-22 15:25:05,967 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:25:05,967 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:25:05,967 INFO 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pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:26:05,967 INFO DISTRO:|0|0|0| 2024-06-22 15:27:05,968 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:27:05,968 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:27:05,968 INFO DISTRO:|0|0|0| 2024-06-22 15:28:05,968 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:28:05,968 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:28:05,968 INFO DISTRO:|0|0|0| 2024-06-22 15:29:05,968 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 15:29:05,968 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 15:29:05,968 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:29:05,968 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:30:05,969 INFO DISTRO:|0|0|0| 2024-06-22 15:31:05,969 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:31:05,969 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:31:05,969 INFO DISTRO:|0|0|0| 2024-06-22 15:32:05,969 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:32:05,969 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending 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pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:36:05,971 INFO DISTRO:|0|0|0| 2024-06-22 15:37:05,971 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:37:05,971 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:37:05,971 INFO DISTRO:|0|0|0| 2024-06-22 15:38:05,971 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:38:05,971 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:38:05,971 INFO DISTRO:|0|0|0| 2024-06-22 15:39:05,971 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 15:39:05,971 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 15:39:05,971 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:39:05,972 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:40:05,972 INFO DISTRO:|0|0|0| 2024-06-22 15:41:05,972 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:41:05,972 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:43:05,973 INFO DISTRO:|0|0|0| 2024-06-22 15:44:05,973 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:44:05,973 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:44:05,973 INFO DISTRO:|0|0|0| 2024-06-22 15:45:05,973 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:45:05,973 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:47:05,974 INFO DISTRO:|0|0|0| 2024-06-22 15:48:05,974 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:48:05,974 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:48:05,974 INFO DISTRO:|0|0|0| 2024-06-22 15:49:05,975 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 15:49:05,975 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 15:49:05,975 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:49:05,975 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 15:49:05,975 INFO DISTRO:|0|0|0| 2024-06-22 15:50:05,975 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 15:50:05,975 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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15:57:05,977 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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16:03:05,979 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 16:03:05,979 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, 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16:21:05,985 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 16:24:05,987 INFO DISTRO:|0|0|0| 2024-06-22 16:25:05,987 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 16:25:05,987 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending 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16:34:05,990 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 16:40:05,992 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 17:10:06,002 INFO DISTRO:|0|0|0| 2024-06-22 17:11:06,002 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 17:11:06,002 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 17:17:06,004 INFO DISTRO:|0|0|0| 2024-06-22 17:18:06,004 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 17:18:06,005 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 17:18:06,005 INFO DISTRO:|0|0|0| 2024-06-22 17:19:06,005 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 17:19:06,005 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 17:19:06,005 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 17:19:06,005 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 17:19:06,005 INFO DISTRO:|0|0|0| 2024-06-22 17:20:06,005 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 17:20:06,005 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending 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INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 17:30:06,009 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 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17:37:06,011 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 17:54:06,017 INFO DISTRO:|0|0|0| 2024-06-22 17:55:06,017 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 17:55:06,017 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 17:55:06,017 INFO 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18:01:06,019 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 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naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:04:06,020 INFO DISTRO:|0|0|0| 2024-06-22 18:05:06,020 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:05:06,021 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:05:06,021 INFO DISTRO:|0|0|0| 2024-06-22 18:06:06,021 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:06:06,021 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:06:06,021 INFO DISTRO:|0|0|0| 2024-06-22 18:07:06,021 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:07:06,021 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:07:06,021 INFO DISTRO:|0|0|0| 2024-06-22 18:08:06,021 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:08:06,022 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:08:06,022 INFO DISTRO:|0|0|0| 2024-06-22 18:09:06,022 INFO PERFORMANCE:|serviceCount|ipCount|subscribeCount|maxPushCost|avgPushCost|totalPushCount|failPushCount 2024-06-22 18:09:06,022 INFO DISTRO:|V1SyncDone|V1SyncFail|V2SyncDone|V2SyncFail|V2VerifyFail| 2024-06-22 18:09:06,022 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:09:06,022 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 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tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:10:06,022 INFO DISTRO:|0|0|0| 2024-06-22 18:11:06,022 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:11:06,023 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:11:06,023 INFO DISTRO:|0|0|0| 2024-06-22 18:12:06,023 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:12:06,023 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 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pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:13:06,023 INFO DISTRO:|0|0|0| 2024-06-22 18:14:06,023 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:14:06,024 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:14:06,024 INFO DISTRO:|0|0|0| 2024-06-22 18:15:06,024 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:15:06,024 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:15:06,024 INFO DISTRO:|0|0|0| 2024-06-22 18:16:06,024 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:16:06,024 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:16:06,024 INFO DISTRO:|0|0|0| 2024-06-22 18:17:06,024 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:17:06,025 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:17:06,025 INFO DISTRO:|0|0|0| 2024-06-22 18:18:06,025 INFO PERFORMANCE:|0|0|0|-1|-1|0|0 2024-06-22 18:18:06,025 INFO Task worker status: naming_0%32, pending tasks: 0 naming_1%32, pending tasks: 0 naming_2%32, pending tasks: 0 naming_3%32, pending tasks: 0 naming_4%32, pending tasks: 0 naming_5%32, pending tasks: 0 naming_6%32, pending tasks: 0 naming_7%32, pending tasks: 0 naming_8%32, pending tasks: 0 naming_9%32, pending tasks: 0 naming_10%32, pending tasks: 0 naming_11%32, pending tasks: 0 naming_12%32, pending tasks: 0 naming_13%32, pending tasks: 0 naming_14%32, pending tasks: 0 naming_15%32, pending tasks: 0 naming_16%32, pending tasks: 0 naming_17%32, pending tasks: 0 naming_18%32, pending tasks: 0 naming_19%32, pending tasks: 0 naming_20%32, pending tasks: 0 naming_21%32, pending tasks: 0 naming_22%32, pending tasks: 0 naming_23%32, pending tasks: 0 naming_24%32, pending tasks: 0 naming_25%32, pending tasks: 0 naming_26%32, pending tasks: 0 naming_27%32, pending tasks: 0 naming_28%32, pending tasks: 0 naming_29%32, pending tasks: 0 naming_30%32, pending tasks: 0 naming_31%32, pending tasks: 0 2024-06-22 18:18:06,025 INFO DISTRO:|0|0|0|

nacos-docker/example/standalone-logs/naming-performance.log/0

2024-06-11 08:42:50,416 INFO Connection transportReady,connectionId = 1718066570416_10.20.28.245_6026 2024-06-11 08:42:50,992 INFO Connection transportReady,connectionId = 1718066570992_10.20.28.245_6027 2024-06-11 08:42:51,612 INFO Connection transportTerminated,connectionId = 1718066570416_10.20.28.245_6026 2024-06-11 08:42:51,614 INFO [1718066570416_10.20.28.245_6026]client disconnected,clear config listen context 2024-06-11 08:43:31,550 INFO Connection transportTerminated,connectionId = 1718066570992_10.20.28.245_6027 2024-06-11 08:43:31,550 INFO [1718066570992_10.20.28.245_6027]client disconnected,clear config listen context

nacos-docker/example/standalone-logs/remote-digest.log.2024-06-11.0/0

<?xml version="1.0" encoding="UTF-8"?> <dataSource name="postgres@localhost"> <database-model serializer="dbm" dbms="POSTGRES" family-id="POSTGRES" format-version="4.52"> <root id="1"/> <database id="2" parent="1" name="postgres"> <Comment>default administrative connection database</Comment> <Current>1</Current> <Grants>11||10|C|G 11||-9223372036854775808|U|G 11||10|U|G 2200||6171|C|G 2200||-9223372036854775808|U|G 2200||6171|U|G 13209||10|C|G 13209||-9223372036854775808|U|G 13209||10|U|G</Grants> <IntrospectionStateNumber>747</IntrospectionStateNumber> <ObjectId>5</ObjectId> <OwnerName>postgres</OwnerName> </database> <database id="3" parent="1" name="vector"> <ObjectId>16388</ObjectId> <OwnerName>postgres</OwnerName> </database> <role id="4" parent="1" name="pg_checkpoint"> <ObjectId>4544</ObjectId> </role> <role id="5" parent="1" name="pg_database_owner"> <ObjectId>6171</ObjectId> </role> <role id="6" parent="1" name="pg_execute_server_program"> <ObjectId>4571</ObjectId> </role> <role id="7" parent="1" name="pg_monitor"> <ObjectId>3373</ObjectId> <RoleGrants>3374 3375 3377</RoleGrants> </role> <role id="8" parent="1" name="pg_read_all_data"> <ObjectId>6181</ObjectId> </role> <role id="9" parent="1" name="pg_read_all_settings"> <ObjectId>3374</ObjectId> </role> <role id="10" parent="1" name="pg_read_all_stats"> <ObjectId>3375</ObjectId> </role> <role id="11" parent="1" name="pg_read_server_files"> <ObjectId>4569</ObjectId> </role> <role id="12" parent="1" name="pg_signal_backend"> <ObjectId>4200</ObjectId> </role> <role id="13" parent="1" name="pg_stat_scan_tables"> <ObjectId>3377</ObjectId> </role> <role id="14" parent="1" name="pg_write_all_data"> <ObjectId>6182</ObjectId> </role> <role id="15" parent="1" name="pg_write_server_files"> <ObjectId>4570</ObjectId> </role> <role id="16" parent="1" name="postgres"> <BypassRls>1</BypassRls> <CanLogin>1</CanLogin> <CreateDb>1</CreateDb> <CreateRole>1</CreateRole> <ObjectId>10</ObjectId> <Replication>1</Replication> <SuperRole>1</SuperRole> </role> <tablespace id="17" parent="1" name="pg_default"> <ObjectId>1663</ObjectId> <StateNumber>1</StateNumber> <OwnerName>postgres</OwnerName> </tablespace> <tablespace id="18" parent="1" name="pg_global"> <ObjectId>1664</ObjectId> <StateNumber>1</StateNumber> <OwnerName>postgres</OwnerName> </tablespace> <access-method id="19" parent="2" name="brin"> <Comment>block range index (BRIN) access method</Comment> <ObjectId>3580</ObjectId> <StateNumber>1</StateNumber> <Type>index</Type> <HandlerId>335</HandlerId> <HandlerName>brinhandler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <access-method id="20" parent="2" name="btree"> <Comment>b-tree index access method</Comment> <ObjectId>403</ObjectId> <StateNumber>1</StateNumber> <Type>index</Type> <HandlerId>330</HandlerId> <HandlerName>bthandler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <access-method id="21" parent="2" name="gin"> <Comment>GIN index access method</Comment> <ObjectId>2742</ObjectId> <StateNumber>1</StateNumber> <Type>index</Type> <HandlerId>333</HandlerId> <HandlerName>ginhandler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <access-method id="22" parent="2" name="gist"> <Comment>GiST index access method</Comment> <ObjectId>783</ObjectId> <StateNumber>1</StateNumber> <Type>index</Type> <HandlerId>332</HandlerId> <HandlerName>gisthandler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <access-method id="23" parent="2" name="hash"> <Comment>hash index access method</Comment> <ObjectId>405</ObjectId> <StateNumber>1</StateNumber> <Type>index</Type> <HandlerId>331</HandlerId> <HandlerName>hashhandler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <access-method id="24" parent="2" name="heap"> <Comment>heap table access method</Comment> <ObjectId>2</ObjectId> <StateNumber>1</StateNumber> <Type>table</Type> <HandlerId>3</HandlerId> <HandlerName>heap_tableam_handler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <access-method id="25" parent="2" name="spgist"> <Comment>SP-GiST index access method</Comment> <ObjectId>4000</ObjectId> <StateNumber>1</StateNumber> <Type>index</Type> <HandlerId>334</HandlerId> <HandlerName>spghandler</HandlerName> <HandlerParentName>pg_catalog</HandlerParentName> </access-method> <cast id="26" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10035</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2558</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>16</SourceTypeId> <SourceTypeName>bool</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="27" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10201</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2971</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>16</SourceTypeId> <SourceTypeName>bool</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1042</TargetTypeId> <TargetTypeName>bpchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="28" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10191</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2971</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>16</SourceTypeId> <SourceTypeName>bool</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="29" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10196</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2971</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>16</SourceTypeId> <SourceTypeName>bool</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1043</TargetTypeId> <TargetTypeName>varchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="30" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10143</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>77</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>18</SourceTypeId> <SourceTypeName>char</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="31" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10133</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>946</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>18</SourceTypeId> <SourceTypeName>char</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1043</TargetTypeId> <TargetTypeName>varchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="32" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10131</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>946</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>18</SourceTypeId> <SourceTypeName>char</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="33" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10132</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>860</CastFunctionId> <CastFunctionName>bpchar</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>18</SourceTypeId> <SourceTypeName>char</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1042</TargetTypeId> <TargetTypeName>bpchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="34" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10135</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>408</CastFunctionId> <CastFunctionName>bpchar</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>19</SourceTypeId> <SourceTypeName>name</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1042</TargetTypeId> <TargetTypeName>bpchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="35" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10134</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>406</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>19</SourceTypeId> <SourceTypeName>name</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="36" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10136</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1401</CastFunctionId> <CastFunctionName>varchar</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>19</SourceTypeId> <SourceTypeName>name</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1043</TargetTypeId> <TargetTypeName>varchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="37" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10090</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2206</TargetTypeId> <TargetTypeName>regtype</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="38" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10060</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2203</TargetTypeId> <TargetTypeName>regoper</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="39" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10003</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>482</CastFunctionId> <CastFunctionName>float8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>701</TargetTypeId> <TargetTypeName>float8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="40" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10069</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2204</TargetTypeId> <TargetTypeName>regoperator</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="41" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10001</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>480</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="42" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10044</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>24</TargetTypeId> <TargetTypeName>regproc</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="43" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10113</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4096</TargetTypeId> <TargetTypeName>regrole</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="44" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10120</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4089</TargetTypeId> <TargetTypeName>regnamespace</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="45" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10002</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>652</CastFunctionId> <CastFunctionName>float4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>700</TargetTypeId> <TargetTypeName>float4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="46" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10104</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3769</TargetTypeId> <TargetTypeName>regdictionary</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="47" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10083</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4191</TargetTypeId> <TargetTypeName>regcollation</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="48" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10033</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3812</CastFunctionId> <CastFunctionName>money</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>790</TargetTypeId> <TargetTypeName>money</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="49" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10037</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="50" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10097</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3734</TargetTypeId> <TargetTypeName>regconfig</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="51" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10000</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>714</CastFunctionId> <CastFunctionName>int2</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>21</TargetTypeId> <TargetTypeName>int2</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="52" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10185</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2075</CastFunctionId> <CastFunctionName>bit</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1560</TargetTypeId> <TargetTypeName>bit</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="53" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10004</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1781</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="54" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10053</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2202</TargetTypeId> <TargetTypeName>regprocedure</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="55" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10076</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1287</CastFunctionId> <CastFunctionName>oid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>20</SourceTypeId> <SourceTypeName>int8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2205</TargetTypeId> <TargetTypeName>regclass</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="56" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10045</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>24</TargetTypeId> <TargetTypeName>regproc</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="57" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10091</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2206</TargetTypeId> <TargetTypeName>regtype</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="58" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10084</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4191</TargetTypeId> <TargetTypeName>regcollation</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="59" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10070</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2204</TargetTypeId> <TargetTypeName>regoperator</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="60" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10038</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="61" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10009</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1782</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="62" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10077</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2205</TargetTypeId> <TargetTypeName>regclass</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="63" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10006</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="64" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10054</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2202</TargetTypeId> <TargetTypeName>regprocedure</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="65" parent="2"> 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<TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="67" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10114</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4096</TargetTypeId> <TargetTypeName>regrole</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="68" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10008</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>235</CastFunctionId> <CastFunctionName>float8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>701</TargetTypeId> <TargetTypeName>float8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="69" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10105</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3769</TargetTypeId> <TargetTypeName>regdictionary</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="70" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10121</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4089</TargetTypeId> <TargetTypeName>regnamespace</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="71" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10061</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2203</TargetTypeId> <TargetTypeName>regoper</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="72" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10098</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>313</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>21</SourceTypeId> <SourceTypeName>int2</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3734</TargetTypeId> <TargetTypeName>regconfig</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="73" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10078</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2205</TargetTypeId> <TargetTypeName>regclass</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="74" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10085</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4191</TargetTypeId> <TargetTypeName>regcollation</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="75" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10115</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4096</TargetTypeId> <TargetTypeName>regrole</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="76" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10144</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>78</CastFunctionId> <CastFunctionName>char</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>18</TargetTypeId> <TargetTypeName>char</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="77" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10122</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4089</TargetTypeId> <TargetTypeName>regnamespace</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="78" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10010</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>481</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="79" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10106</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3769</TargetTypeId> <TargetTypeName>regdictionary</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="80" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10099</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3734</TargetTypeId> <TargetTypeName>regconfig</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="81" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10011</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>314</CastFunctionId> <CastFunctionName>int2</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>21</TargetTypeId> <TargetTypeName>int2</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="82" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10092</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2206</TargetTypeId> <TargetTypeName>regtype</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="83" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10071</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2204</TargetTypeId> <TargetTypeName>regoperator</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="84" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10062</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2203</TargetTypeId> <TargetTypeName>regoper</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="85" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10046</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>24</TargetTypeId> <TargetTypeName>regproc</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="86" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10055</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2202</TargetTypeId> <TargetTypeName>regprocedure</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="87" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10034</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2557</CastFunctionId> <CastFunctionName>bool</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>16</TargetTypeId> <TargetTypeName>bool</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="88" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10014</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1740</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="89" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10039</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="90" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10186</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1683</CastFunctionId> <CastFunctionName>bit</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1560</TargetTypeId> <TargetTypeName>bit</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="91" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10012</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>318</CastFunctionId> <CastFunctionName>float4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>700</TargetTypeId> <TargetTypeName>float4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="92" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10013</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>316</CastFunctionId> <CastFunctionName>float8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>701</TargetTypeId> <TargetTypeName>float8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="93" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10032</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3811</CastFunctionId> <CastFunctionName>money</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>23</SourceTypeId> <SourceTypeName>int4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>790</TargetTypeId> <TargetTypeName>money</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="94" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10048</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>24</SourceTypeId> <SourceTypeName>regproc</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="95" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10047</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>24</SourceTypeId> <SourceTypeName>regproc</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="96" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10043</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>24</SourceTypeId> <SourceTypeName>regproc</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="97" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10049</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>24</SourceTypeId> <SourceTypeName>regproc</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2202</TargetTypeId> <TargetTypeName>regprocedure</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="98" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10125</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>25</SourceTypeId> <SourceTypeName>text</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1042</TargetTypeId> <TargetTypeName>bpchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="99" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10140</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>407</CastFunctionId> <CastFunctionName>name</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>25</SourceTypeId> <SourceTypeName>text</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>19</TargetTypeId> <TargetTypeName>name</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="100" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10137</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>944</CastFunctionId> <CastFunctionName>char</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>25</SourceTypeId> <SourceTypeName>text</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>18</TargetTypeId> <TargetTypeName>char</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="101" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10126</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>25</SourceTypeId> <SourceTypeName>text</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1043</TargetTypeId> <TargetTypeName>varchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="102" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10193</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2896</CastFunctionId> <CastFunctionName>xml</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>25</SourceTypeId> <SourceTypeName>text</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>142</TargetTypeId> <TargetTypeName>xml</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="103" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10109</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1079</CastFunctionId> <CastFunctionName>regclass</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>25</SourceTypeId> <SourceTypeName>text</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2205</TargetTypeId> <TargetTypeName>regclass</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="104" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10074</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2205</TargetTypeId> <TargetTypeName>regclass</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="105" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10051</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2202</TargetTypeId> <TargetTypeName>regprocedure</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="106" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10095</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3734</TargetTypeId> <TargetTypeName>regconfig</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="107" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10058</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2203</TargetTypeId> <TargetTypeName>regoper</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="108" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10081</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4191</TargetTypeId> <TargetTypeName>regcollation</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="109" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10067</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2204</TargetTypeId> <TargetTypeName>regoperator</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="110" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10042</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>24</TargetTypeId> <TargetTypeName>regproc</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="111" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10040</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="112" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10111</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4096</TargetTypeId> <TargetTypeName>regrole</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="113" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10102</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3769</TargetTypeId> <TargetTypeName>regdictionary</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="114" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10088</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2206</TargetTypeId> <TargetTypeName>regtype</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="115" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10041</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="116" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10118</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>26</SourceTypeId> <SourceTypeName>oid</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4089</TargetTypeId> <TargetTypeName>regnamespace</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="117" parent="2"> <Context>assignment</Context> <Method>io</Method> <ObjectId>10214</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>114</SourceTypeId> <SourceTypeName>json</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>3802</TargetTypeId> <TargetTypeName>jsonb</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="118" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10202</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>142</SourceTypeId> <SourceTypeName>xml</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1042</TargetTypeId> <TargetTypeName>bpchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="119" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10197</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>142</SourceTypeId> <SourceTypeName>xml</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1043</TargetTypeId> <TargetTypeName>varchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="120" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10192</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>142</SourceTypeId> <SourceTypeName>xml</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="121" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10145</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>194</SourceTypeId> <SourceTypeName>pg_node_tree</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="122" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10165</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4091</CastFunctionId> <CastFunctionName>box</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>600</SourceTypeId> <SourceTypeName>point</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>603</TargetTypeId> <TargetTypeName>box</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="123" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10166</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1532</CastFunctionId> <CastFunctionName>point</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>601</SourceTypeId> <SourceTypeName>lseg</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>600</TargetTypeId> <TargetTypeName>point</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="124" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10167</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1449</CastFunctionId> <CastFunctionName>polygon</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>602</SourceTypeId> <SourceTypeName>path</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>604</TargetTypeId> <TargetTypeName>polygon</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="125" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10168</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1534</CastFunctionId> <CastFunctionName>point</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>603</SourceTypeId> <SourceTypeName>box</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>600</TargetTypeId> <TargetTypeName>point</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="126" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10171</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1479</CastFunctionId> <CastFunctionName>circle</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>603</SourceTypeId> <SourceTypeName>box</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>718</TargetTypeId> <TargetTypeName>circle</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="127" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10169</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1541</CastFunctionId> <CastFunctionName>lseg</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>603</SourceTypeId> <SourceTypeName>box</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>601</TargetTypeId> <TargetTypeName>lseg</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="128" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10170</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1448</CastFunctionId> <CastFunctionName>polygon</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>603</SourceTypeId> <SourceTypeName>box</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>604</TargetTypeId> <TargetTypeName>polygon</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="129" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10172</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1540</CastFunctionId> <CastFunctionName>point</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>604</SourceTypeId> <SourceTypeName>polygon</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>600</TargetTypeId> <TargetTypeName>point</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="130" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10175</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1474</CastFunctionId> <CastFunctionName>circle</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>604</SourceTypeId> <SourceTypeName>polygon</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>718</TargetTypeId> <TargetTypeName>circle</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="131" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10174</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1446</CastFunctionId> <CastFunctionName>box</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>604</SourceTypeId> <SourceTypeName>polygon</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>603</TargetTypeId> <TargetTypeName>box</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="132" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10173</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1447</CastFunctionId> <CastFunctionName>path</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>604</SourceTypeId> <SourceTypeName>polygon</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>602</TargetTypeId> <TargetTypeName>path</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="133" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10194</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>730</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>650</SourceTypeId> <SourceTypeName>cidr</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1043</TargetTypeId> <TargetTypeName>varchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="134" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10199</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>730</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>650</SourceTypeId> <SourceTypeName>cidr</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1042</TargetTypeId> <TargetTypeName>bpchar</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="135" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10189</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>730</CastFunctionId> <CastFunctionName>text</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>650</SourceTypeId> <SourceTypeName>cidr</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="136" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10181</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>650</SourceTypeId> <SourceTypeName>cidr</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>869</TargetTypeId> <TargetTypeName>inet</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="137" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10016</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>238</CastFunctionId> <CastFunctionName>int2</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>700</SourceTypeId> <SourceTypeName>float4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>21</TargetTypeId> <TargetTypeName>int2</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="138" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10015</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>653</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>700</SourceTypeId> <SourceTypeName>float4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="139" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10018</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>311</CastFunctionId> <CastFunctionName>float8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>700</SourceTypeId> <SourceTypeName>float4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>701</TargetTypeId> <TargetTypeName>float8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="140" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10019</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1742</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>700</SourceTypeId> <SourceTypeName>float4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="141" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10017</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>319</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>700</SourceTypeId> <SourceTypeName>float4</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="142" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10024</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1743</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>701</SourceTypeId> <SourceTypeName>float8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="143" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10020</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>483</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>701</SourceTypeId> <SourceTypeName>float8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="144" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10021</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>237</CastFunctionId> <CastFunctionName>int2</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>701</SourceTypeId> <SourceTypeName>float8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>21</TargetTypeId> <TargetTypeName>int2</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="145" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10022</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>317</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>701</SourceTypeId> <SourceTypeName>float8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="146" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10023</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>312</CastFunctionId> <CastFunctionName>float4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>701</SourceTypeId> <SourceTypeName>float8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>700</TargetTypeId> <TargetTypeName>float4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="147" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10178</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1544</CastFunctionId> <CastFunctionName>polygon</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>718</SourceTypeId> <SourceTypeName>circle</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>604</TargetTypeId> <TargetTypeName>polygon</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="148" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10176</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1416</CastFunctionId> <CastFunctionName>point</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>718</SourceTypeId> <SourceTypeName>circle</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>600</TargetTypeId> <TargetTypeName>point</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="149" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10177</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1480</CastFunctionId> <CastFunctionName>box</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>718</SourceTypeId> <SourceTypeName>circle</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>603</TargetTypeId> <TargetTypeName>box</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="150" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10180</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4124</CastFunctionId> <CastFunctionName>macaddr</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>774</SourceTypeId> <SourceTypeName>macaddr8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>829</TargetTypeId> <TargetTypeName>macaddr</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="151" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10030</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3823</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>790</SourceTypeId> <SourceTypeName>money</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="152" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10179</ObjectId> 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<CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>1700</SourceTypeId> <SourceTypeName>numeric</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="197" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10213</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1703</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>1700</SourceTypeId> <SourceTypeName>numeric</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="198" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10029</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1746</CastFunctionId> <CastFunctionName>float8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>1700</SourceTypeId> <SourceTypeName>numeric</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>701</TargetTypeId> <TargetTypeName>float8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="199" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10031</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3824</CastFunctionId> <CastFunctionName>money</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>1700</SourceTypeId> <SourceTypeName>numeric</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>790</TargetTypeId> <TargetTypeName>money</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="200" parent="2"> <Context>implicit</Context> <Method>function</Method> <ObjectId>10028</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1745</CastFunctionId> <CastFunctionName>float4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>1700</SourceTypeId> <SourceTypeName>numeric</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>700</TargetTypeId> <TargetTypeName>float4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="201" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10057</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2202</SourceTypeId> <SourceTypeName>regprocedure</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="202" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10052</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2202</SourceTypeId> <SourceTypeName>regprocedure</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="203" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10056</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>2202</SourceTypeId> <SourceTypeName>regprocedure</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="204" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10050</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2202</SourceTypeId> <SourceTypeName>regprocedure</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>24</TargetTypeId> <TargetTypeName>regproc</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="205" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10065</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2203</SourceTypeId> <SourceTypeName>regoper</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2204</TargetTypeId> <TargetTypeName>regoperator</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="206" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10063</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>2203</SourceTypeId> <SourceTypeName>regoper</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="207" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10059</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2203</SourceTypeId> <SourceTypeName>regoper</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="208" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10064</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2203</SourceTypeId> <SourceTypeName>regoper</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="209" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10073</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2204</SourceTypeId> <SourceTypeName>regoperator</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="210" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10068</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2204</SourceTypeId> <SourceTypeName>regoperator</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="211" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10072</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>2204</SourceTypeId> <SourceTypeName>regoperator</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="212" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10066</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2204</SourceTypeId> <SourceTypeName>regoperator</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>2203</TargetTypeId> <TargetTypeName>regoper</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="213" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10079</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>2205</SourceTypeId> <SourceTypeName>regclass</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="214" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10075</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2205</SourceTypeId> <SourceTypeName>regclass</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="215" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10080</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2205</SourceTypeId> <SourceTypeName>regclass</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="216" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10093</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>2206</SourceTypeId> <SourceTypeName>regtype</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="217" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10094</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2206</SourceTypeId> <SourceTypeName>regtype</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="218" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10089</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>2206</SourceTypeId> <SourceTypeName>regtype</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="219" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10146</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3361</SourceTypeId> <SourceTypeName>pg_ndistinct</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>17</TargetTypeId> <TargetTypeName>bytea</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="220" parent="2"> <Context>implicit</Context> <Method>io</Method> <ObjectId>10147</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3361</SourceTypeId> <SourceTypeName>pg_ndistinct</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="221" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10148</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3402</SourceTypeId> <SourceTypeName>pg_dependencies</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>17</TargetTypeId> <TargetTypeName>bytea</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="222" parent="2"> <Context>implicit</Context> <Method>io</Method> <ObjectId>10149</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3402</SourceTypeId> <SourceTypeName>pg_dependencies</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="223" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10096</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3734</SourceTypeId> <SourceTypeName>regconfig</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="224" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10100</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3734</SourceTypeId> <SourceTypeName>regconfig</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="225" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10101</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3734</SourceTypeId> <SourceTypeName>regconfig</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="226" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10103</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3769</SourceTypeId> <SourceTypeName>regdictionary</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="227" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10108</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3769</SourceTypeId> <SourceTypeName>regdictionary</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="228" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10107</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3769</SourceTypeId> <SourceTypeName>regdictionary</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="229" parent="2"> <Context>assignment</Context> <Method>io</Method> <ObjectId>10215</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>114</TargetTypeId> <TargetTypeName>json</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="230" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10218</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3450</CastFunctionId> <CastFunctionName>int2</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>21</TargetTypeId> <TargetTypeName>int2</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="231" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10220</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3452</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="232" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10219</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3451</CastFunctionId> <CastFunctionName>int4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="233" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10216</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3556</CastFunctionId> <CastFunctionName>bool</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>16</TargetTypeId> <TargetTypeName>bool</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="234" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10221</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3453</CastFunctionId> <CastFunctionName>float4</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>700</TargetTypeId> <TargetTypeName>float4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="235" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10217</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>3449</CastFunctionId> <CastFunctionName>numeric</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>1700</TargetTypeId> <TargetTypeName>numeric</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="236" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10222</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>2580</CastFunctionId> <CastFunctionName>float8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3802</SourceTypeId> <SourceTypeName>jsonb</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>701</TargetTypeId> <TargetTypeName>float8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="237" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10223</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4281</CastFunctionId> <CastFunctionName>int4multirange</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3904</SourceTypeId> <SourceTypeName>int4range</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4451</TargetTypeId> <TargetTypeName>int4multirange</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="238" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10225</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4284</CastFunctionId> <CastFunctionName>nummultirange</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3906</SourceTypeId> <SourceTypeName>numrange</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4532</TargetTypeId> <TargetTypeName>nummultirange</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="239" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10227</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4287</CastFunctionId> <CastFunctionName>tsmultirange</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3908</SourceTypeId> <SourceTypeName>tsrange</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4533</TargetTypeId> <TargetTypeName>tsmultirange</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="240" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10228</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4290</CastFunctionId> <CastFunctionName>tstzmultirange</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3910</SourceTypeId> <SourceTypeName>tstzrange</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4534</TargetTypeId> <TargetTypeName>tstzmultirange</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="241" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10226</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4293</CastFunctionId> <CastFunctionName>datemultirange</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3912</SourceTypeId> <SourceTypeName>daterange</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4535</TargetTypeId> <TargetTypeName>datemultirange</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="242" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10224</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>4296</CastFunctionId> <CastFunctionName>int8multirange</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>3926</SourceTypeId> <SourceTypeName>int8range</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>4536</TargetTypeId> <TargetTypeName>int8multirange</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="243" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10124</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>4089</SourceTypeId> <SourceTypeName>regnamespace</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="244" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10123</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>4089</SourceTypeId> <SourceTypeName>regnamespace</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="245" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10119</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>4089</SourceTypeId> <SourceTypeName>regnamespace</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="246" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10117</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>4096</SourceTypeId> <SourceTypeName>regrole</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="247" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10112</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>4096</SourceTypeId> <SourceTypeName>regrole</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="248" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10116</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>4096</SourceTypeId> <SourceTypeName>regrole</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="249" parent="2"> <Context>assignment</Context> <Method>function</Method> <ObjectId>10086</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>1288</CastFunctionId> <CastFunctionName>int8</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>4191</SourceTypeId> <SourceTypeName>regcollation</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>20</TargetTypeId> <TargetTypeName>int8</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="250" parent="2"> <Context>assignment</Context> <Method>binary</Method> <ObjectId>10087</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>4191</SourceTypeId> <SourceTypeName>regcollation</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>23</TargetTypeId> <TargetTypeName>int4</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="251" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10082</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>4191</SourceTypeId> <SourceTypeName>regcollation</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>26</TargetTypeId> <TargetTypeName>oid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="252" parent="2"> <Context>implicit</Context> <Method>binary</Method> <ObjectId>10150</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>5017</SourceTypeId> <SourceTypeName>pg_mcv_list</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>17</TargetTypeId> <TargetTypeName>bytea</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="253" parent="2"> <Context>implicit</Context> <Method>io</Method> <ObjectId>10151</ObjectId> <StateNumber>1</StateNumber> <SourceTypeId>5017</SourceTypeId> <SourceTypeName>pg_mcv_list</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>25</TargetTypeId> <TargetTypeName>text</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <cast id="254" parent="2"> <Context>explicit</Context> <Method>function</Method> <ObjectId>10036</ObjectId> <StateNumber>1</StateNumber> <CastFunctionId>5071</CastFunctionId> <CastFunctionName>xid</CastFunctionName> <CastFunctionParentName>pg_catalog</CastFunctionParentName> <SourceTypeId>5069</SourceTypeId> <SourceTypeName>xid8</SourceTypeName> <SourceTypeParentName>pg_catalog</SourceTypeParentName> <TargetTypeId>28</TargetTypeId> <TargetTypeName>xid</TargetTypeName> <TargetTypeParentName>pg_catalog</TargetTypeParentName> </cast> <extension id="255" parent="2" name="plpgsql"> <Comment>PL/pgSQL procedural language</Comment> <ObjectId>13561</ObjectId> <StateNumber>678</StateNumber> <Version>1.0</Version> <ExtSchemaId>11</ExtSchemaId> <ExtSchemaName>pg_catalog</ExtSchemaName> <MemberIds>13562 13563 13564 13565</MemberIds> </extension> <language id="256" parent="2" name="c"> <Comment>dynamically-loaded C functions</Comment> <ObjectId>13</ObjectId> <StateNumber>1</StateNumber> <ValidatorName>fmgr_c_validator</ValidatorName> <ValidatorSchema>pg_catalog</ValidatorSchema> </language> <language id="257" parent="2" name="internal"> <Comment>built-in functions</Comment> <ObjectId>12</ObjectId> <StateNumber>1</StateNumber> <ValidatorName>fmgr_internal_validator</ValidatorName> <ValidatorSchema>pg_catalog</ValidatorSchema> </language> <language id="258" parent="2" name="plpgsql"> <Comment>PL/pgSQL procedural language</Comment> <HandlerName>plpgsql_call_handler</HandlerName> <HandlerSchema>pg_catalog</HandlerSchema> <InlineHandlerName>plpgsql_inline_handler</InlineHandlerName> <InlineHandlerSchema>pg_catalog</InlineHandlerSchema> <ObjectId>13565</ObjectId> <StateNumber>678</StateNumber> <Trusted>1</Trusted> <ValidatorName>plpgsql_validator</ValidatorName> <ValidatorSchema>pg_catalog</ValidatorSchema> </language> <language id="259" parent="2" name="sql"> <Comment>SQL-language functions</Comment> <ObjectId>14</ObjectId> <StateNumber>1</StateNumber> <Trusted>1</Trusted> <ValidatorName>fmgr_sql_validator</ValidatorName> <ValidatorSchema>pg_catalog</ValidatorSchema> </language> <schema id="260" parent="2" name="information_schema"> <ObjectId>13209</ObjectId> <StateNumber>524</StateNumber> <OwnerName>postgres</OwnerName> </schema> <schema id="261" parent="2" name="pg_catalog"> <Comment>system catalog schema</Comment> <ObjectId>11</ObjectId> <StateNumber>518</StateNumber> <OwnerName>postgres</OwnerName> </schema> <schema id="262" parent="2" name="public"> <Comment>standard public schema</Comment> <Current>1</Current> <IntrospectionStateNumber>747</IntrospectionStateNumber> <LastIntrospectionLocalTimestamp>2024-04-18.06:49:15</LastIntrospectionLocalTimestamp> <ObjectId>2200</ObjectId> <StateNumber>518</StateNumber> <OwnerName>pg_database_owner</OwnerName> </schema> </database-model> </dataSource>

pgvector/.idea/dataSources/87c72d4e-a07d-45ed-a981-d45b32efa52d.xml/0

package org.example.pgvector; import java.lang.Override; import java.lang.String; import java.util.HashMap; import java.util.Map; import org.springframework.aot.generate.Generated; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.DefaultListableBeanFactory; import org.springframework.beans.factory.support.RootBeanDefinition; import org.springframework.context.ApplicationContextInitializer; import org.springframework.context.annotation.ContextAnnotationAutowireCandidateResolver; import org.springframework.context.annotation.ImportAwareAotBeanPostProcessor; import org.springframework.context.support.GenericApplicationContext; import org.springframework.core.annotation.AnnotationAwareOrderComparator; /** * {@link ApplicationContextInitializer} to restore an application context based on previous AOT processing. */ @Generated public class PgVectorApplicationKt__ApplicationContextInitializer implements ApplicationContextInitializer<GenericApplicationContext> { @Override public void initialize(GenericApplicationContext applicationContext) { DefaultListableBeanFactory beanFactory = applicationContext.getDefaultListableBeanFactory(); beanFactory.setAutowireCandidateResolver(new ContextAnnotationAutowireCandidateResolver()); beanFactory.setDependencyComparator(AnnotationAwareOrderComparator.INSTANCE); addImportAwareBeanPostProcessors(beanFactory); new PgVectorApplicationKt__BeanFactoryRegistrations().registerBeanDefinitions(beanFactory); new PgVectorApplicationKt__BeanFactoryRegistrations().registerAliases(beanFactory); } /** * Add ImportAwareBeanPostProcessor to support ImportAware beans. */ private void addImportAwareBeanPostProcessors(DefaultListableBeanFactory beanFactory) { Map<String, String> mappings = new HashMap<>(); mappings.put("org.springframework.transaction.annotation.ProxyTransactionManagementConfiguration", "org.springframework.boot.autoconfigure.transaction.TransactionAutoConfiguration$EnableTransactionManagementConfiguration$CglibAutoProxyConfiguration"); RootBeanDefinition beanDefinition = new RootBeanDefinition(ImportAwareAotBeanPostProcessor.class); beanDefinition.setRole(BeanDefinition.ROLE_INFRASTRUCTURE); beanDefinition.setInstanceSupplier(() -> new ImportAwareAotBeanPostProcessor(mappings)); beanFactory.registerBeanDefinition("org.springframework.context.annotation.internalImportAwareAotProcessor", beanDefinition); } }

pgvector/build/generated/aotSources/org/example/pgvector/PgVectorApplicationKt__ApplicationContextInitializer.java/0

package org.springframework.boot.autoconfigure.data.web; import org.springframework.aot.generate.Generated; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; /** * Bean definitions for {@link SpringDataWebProperties}. */ @Generated public class SpringDataWebProperties__BeanDefinitions { /** * Get the bean definition for 'springDataWebProperties'. */ public static BeanDefinition getSpringDataWebPropertiesBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(SpringDataWebProperties.class); beanDefinition.setInstanceSupplier(SpringDataWebProperties::new); return beanDefinition; } }

pgvector/build/generated/aotSources/org/springframework/boot/autoconfigure/data/web/SpringDataWebProperties__BeanDefinitions.java/0

package org.springframework.boot.autoconfigure.sql.init; import org.springframework.aot.generate.Generated; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; /** * Bean definitions for {@link SqlInitializationProperties}. */ @Generated public class SqlInitializationProperties__BeanDefinitions { /** * Get the bean definition for 'sqlInitializationProperties'. */ public static BeanDefinition getSqlInitializationPropertiesBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(SqlInitializationProperties.class); beanDefinition.setInstanceSupplier(SqlInitializationProperties::new); return beanDefinition; } }

pgvector/build/generated/aotSources/org/springframework/boot/autoconfigure/sql/init/SqlInitializationProperties__BeanDefinitions.java/0

package org.springframework.boot.autoconfigure.web.client; import org.springframework.aot.generate.Generated; import org.springframework.beans.factory.ObjectProvider; import org.springframework.beans.factory.aot.BeanInstanceSupplier; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; import org.springframework.boot.web.client.RestTemplateBuilder; /** * Bean definitions for {@link RestTemplateAutoConfiguration}. */ @Generated public class RestTemplateAutoConfiguration__BeanDefinitions { /** * Get the bean definition for 'restTemplateAutoConfiguration'. */ public static BeanDefinition getRestTemplateAutoConfigurationBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(RestTemplateAutoConfiguration.class); beanDefinition.setInstanceSupplier(RestTemplateAutoConfiguration::new); return beanDefinition; } /** * Get the bean instance supplier for 'restTemplateBuilderConfigurer'. */ private static BeanInstanceSupplier<RestTemplateBuilderConfigurer> getRestTemplateBuilderConfigurerInstanceSupplier( ) { return BeanInstanceSupplier.<RestTemplateBuilderConfigurer>forFactoryMethod(RestTemplateAutoConfiguration.class, "restTemplateBuilderConfigurer", ObjectProvider.class, ObjectProvider.class, ObjectProvider.class) .withGenerator((registeredBean, args) -> registeredBean.getBeanFactory().getBean(RestTemplateAutoConfiguration.class).restTemplateBuilderConfigurer(args.get(0), args.get(1), args.get(2))); } /** * Get the bean definition for 'restTemplateBuilderConfigurer'. */ public static BeanDefinition getRestTemplateBuilderConfigurerBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(RestTemplateBuilderConfigurer.class); beanDefinition.setLazyInit(true); beanDefinition.setInstanceSupplier(getRestTemplateBuilderConfigurerInstanceSupplier()); return beanDefinition; } /** * Get the bean instance supplier for 'restTemplateBuilder'. */ private static BeanInstanceSupplier<RestTemplateBuilder> getRestTemplateBuilderInstanceSupplier( ) { return BeanInstanceSupplier.<RestTemplateBuilder>forFactoryMethod(RestTemplateAutoConfiguration.class, "restTemplateBuilder", RestTemplateBuilderConfigurer.class) .withGenerator((registeredBean, args) -> registeredBean.getBeanFactory().getBean(RestTemplateAutoConfiguration.class).restTemplateBuilder(args.get(0))); } /** * Get the bean definition for 'restTemplateBuilder'. */ public static BeanDefinition getRestTemplateBuilderBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(RestTemplateBuilder.class); beanDefinition.setLazyInit(true); beanDefinition.setInstanceSupplier(getRestTemplateBuilderInstanceSupplier()); return beanDefinition; } }

pgvector/build/generated/aotSources/org/springframework/boot/autoconfigure/web/client/RestTemplateAutoConfiguration__BeanDefinitions.java/0

package org.springframework.boot.jackson; import org.springframework.aot.generate.Generated; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; /** * Bean definitions for {@link JsonMixinModuleEntries}. */ @Generated public class JsonMixinModuleEntries__BeanDefinitions { /** * Get the bean instance for 'jsonMixinModuleEntries'. */ private static JsonMixinModuleEntries getJsonMixinModuleEntriesInstance() { return JsonMixinModuleEntries.create((mixins) -> { } ); } /** * Get the bean definition for 'jsonMixinModuleEntries'. */ public static BeanDefinition getJsonMixinModuleEntriesBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition("org.springframework.boot.autoconfigure.jackson.JacksonAutoConfiguration$JacksonMixinConfiguration"); beanDefinition.setTargetType(JsonMixinModuleEntries.class); beanDefinition.setInstanceSupplier(JsonMixinModuleEntries__BeanDefinitions::getJsonMixinModuleEntriesInstance); return beanDefinition; } }

pgvector/build/generated/aotSources/org/springframework/boot/jackson/JsonMixinModuleEntries__BeanDefinitions.java/0

package org.springframework.data.web.config; import org.springframework.aot.generate.Generated; import org.springframework.beans.factory.aot.BeanInstanceSupplier; import org.springframework.beans.factory.config.BeanDefinition; import org.springframework.beans.factory.support.RootBeanDefinition; import org.springframework.data.geo.GeoModule; /** * Bean definitions for {@link SpringDataJacksonConfiguration}. */ @Generated public class SpringDataJacksonConfiguration__BeanDefinitions { /** * Get the bean definition for 'springDataJacksonConfiguration'. */ public static BeanDefinition getSpringDataJacksonConfigurationBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(SpringDataJacksonConfiguration.class); beanDefinition.setInstanceSupplier(SpringDataJacksonConfiguration::new); return beanDefinition; } /** * Get the bean instance supplier for 'jacksonGeoModule'. */ private static BeanInstanceSupplier<GeoModule> getJacksonGeoModuleInstanceSupplier() { return BeanInstanceSupplier.<GeoModule>forFactoryMethod(SpringDataJacksonConfiguration.class, "jacksonGeoModule") .withGenerator((registeredBean) -> registeredBean.getBeanFactory().getBean(SpringDataJacksonConfiguration.class).jacksonGeoModule()); } /** * Get the bean definition for 'jacksonGeoModule'. */ public static BeanDefinition getJacksonGeoModuleBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(GeoModule.class); beanDefinition.setInstanceSupplier(getJacksonGeoModuleInstanceSupplier()); return beanDefinition; } /** * Get the bean instance supplier for 'pageModule'. */ private static BeanInstanceSupplier<SpringDataJacksonConfiguration.PageModule> getPageModuleInstanceSupplier( ) { return BeanInstanceSupplier.<SpringDataJacksonConfiguration.PageModule>forFactoryMethod(SpringDataJacksonConfiguration.class, "pageModule") .withGenerator((registeredBean) -> registeredBean.getBeanFactory().getBean(SpringDataJacksonConfiguration.class).pageModule()); } /** * Get the bean definition for 'pageModule'. */ public static BeanDefinition getPageModuleBeanDefinition() { RootBeanDefinition beanDefinition = new RootBeanDefinition(SpringDataJacksonConfiguration.PageModule.class); beanDefinition.setInstanceSupplier(getPageModuleInstanceSupplier()); return beanDefinition; } }

pgvector/build/generated/aotSources/org/springframework/data/web/config/SpringDataJacksonConfiguration__BeanDefinitions.java/0

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"name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.beanvalidation.BeanValidationIntegrator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.beanvalidation.TypeSafeActivator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.AnnotatedColumn"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.BasicValueBinder"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.CollectionBinder"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.CollectionSecondPass"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.EntityBinder"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.PropertyBinder"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.PropertyContainer"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.QueryBinder"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.boot.model.internal.TableBinder"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.bytecode.internal.BytecodeProviderInitiator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.cfg.Environment"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.dialect.Dialect"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.config.internal.ConfigurationServiceImpl"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.internal.Collections"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.internal.StatefulPersistenceContext"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.internal.Versioning"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.jdbc.env.internal.JdbcEnvironmentInitiator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.jdbc.env.internal.LobCreatorBuilderImpl"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.jdbc.spi.SqlExceptionHelper"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.jndi.internal.JndiServiceImpl"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.spi.CascadingActions"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.transaction.jta.platform.internal.JtaPlatformInitiator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.event.internal.AbstractFlushingEventListener"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { 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"name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.id.enhanced.PooledOptimizer"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.id.enhanced.TableGenerator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.CoreLogging"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.stat.internal.StatisticsInitiator"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.type.CollectionType"}, "name":"org.hibernate.internal.CoreMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.HEMLogging"}, "name":"org.hibernate.internal.EntityManagerMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.jpa.internal.util.LogHelper"}, "name":"org.hibernate.internal.EntityManagerMessageLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.log.ConnectionAccessLogger"}, "name":"org.hibernate.internal.log.ConnectionAccessLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.log.ConnectionPoolingLogger"}, "name":"org.hibernate.internal.log.ConnectionPoolingLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.log.DeprecationLogger"}, "name":"org.hibernate.internal.log.DeprecationLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.log.IncubationLogger"}, "name":"org.hibernate.internal.log.IncubationLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.internal.log.UrlMessageBundle"}, "name":"org.hibernate.internal.log.UrlMessageBundle_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.metamodel.mapping.MappingModelCreationLogger"}, "name":"org.hibernate.metamodel.mapping.MappingModelCreationLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.persister.internal.PersisterFactoryImpl"}, "name":"org.hibernate.persister.collection.BasicCollectionPersister", "methods":[{"name":"<init>","parameterTypes":["org.hibernate.mapping.Collection","org.hibernate.cache.spi.access.CollectionDataAccess","org.hibernate.metamodel.spi.RuntimeModelCreationContext"] }] }, { "condition":{"typeReachable":"org.hibernate.persister.internal.PersisterFactoryImpl"}, "name":"org.hibernate.persister.collection.OneToManyPersister", "methods":[{"name":"<init>","parameterTypes":["org.hibernate.mapping.Collection","org.hibernate.cache.spi.access.CollectionDataAccess","org.hibernate.metamodel.spi.RuntimeModelCreationContext"] }] }, { "condition":{"typeReachable":"org.hibernate.persister.internal.PersisterFactoryImpl"}, "name":"org.hibernate.persister.entity.SingleTableEntityPersister", "methods":[{"name":"<init>","parameterTypes":["org.hibernate.mapping.PersistentClass","org.hibernate.cache.spi.access.EntityDataAccess","org.hibernate.cache.spi.access.NaturalIdDataAccess","org.hibernate.metamodel.spi.RuntimeModelCreationContext"] }] }, { "condition":{"typeReachable":"org.hibernate.metamodel.model.domain.internal.MappingMetamodelImpl"}, "name":"org.hibernate.persister.internal.PersisterFactoryImpl", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.persister.internal.PersisterFactoryImpl"}, "name":"org.hibernate.persister.internal.StandardPersisterClassResolver", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.mapping.Property"}, "name":"org.hibernate.property.access.internal.PropertyAccessStrategyResolverStandardImpl", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.query.QueryLogging"}, "name":"org.hibernate.query.QueryLogging_$logger", 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"methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.sql.results.ResultsLogger"}, "name":"org.hibernate.sql.results.ResultsLogger_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.sql.ast.spi.AbstractSqlAstTranslator"}, "name":"org.hibernate.sql.results.jdbc.internal.JdbcValuesMappingProducerProviderStandard", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.internal.SessionFactoryImpl"}, "name":"org.hibernate.stat.internal.StatisticsImpl", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.tool.schema.spi.SchemaManagementToolCoordinator"}, "name":"org.hibernate.tool.schema.internal.HibernateSchemaManagementTool", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.tool.schema.internal.SchemaDropperImpl"}, "name":"org.hibernate.tool.schema.internal.script.SingleLineSqlScriptExtractor", "queryAllPublicMethods":true }, { "condition":{"typeReachable":"org.hibernate.type.descriptor.JdbcTypeNameMapper"}, "name":"org.hibernate.type.SqlTypes", "allPublicFields":true }, { "condition":{"typeReachable":"org.hibernate.dialect.PgJdbcHelper"}, "name":"org.postgresql.util.PGobject" }, { "condition":{"typeReachable":"org.hibernate.dialect.DialectLogging"}, "name":"org.hibernate.dialect.DialectLogging_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.metamodel.mapping.MappingModelCreationLogging"}, "name":"org.hibernate.metamodel.mapping.MappingModelCreationLogging_$logger", "methods":[{"name":"<init>","parameterTypes":["org.jboss.logging.Logger"] }] }, { "condition":{"typeReachable":"org.hibernate.engine.jdbc.env.internal.LobCreationLogging"}, "name":"org.hibernate.engine.jdbc.env.internal.LobCreationLogging_$logger", 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pgvector/build/native-reachability-metadata/META-INF/native-image/org.hibernate.orm/hibernate-core/6.4.4.Final/reflect-config.json/0

package org.example.pgvector.pojo data class Vector( var userId: String? = null, var frames: ArrayList<Int> = arrayListOf(), var rects: ArrayList<ArrayList<ArrayList<Double>>> = arrayListOf(), var attrs: ArrayList<ArrayList<String>> = arrayListOf(), var kpts: ArrayList<String> = arrayListOf(), var features: ArrayList<ArrayList<Float>> = arrayListOf(), var qualities: ArrayList<Double> = arrayListOf(), var skeletonAction: ArrayList<String> = arrayListOf(), var vehicleplate: ArrayList<String> )

pgvector/src/main/kotlin/org/example/pgvector/pojo/Vector.kt/0

<?xml version="1.0" encoding="UTF-8"?> <module type="PYTHON_MODULE" version="4"> <component name="NewModuleRootManager"> <content url="file://$MODULE_DIR$" /> <orderEntry type="jdk" jdkName="swift" jdkType="Python SDK" /> <orderEntry type="sourceFolder" forTests="false" /> </component> <component name="PyDocumentationSettings"> <option name="format" value="GOOGLE" /> <option name="myDocStringFormat" value="Google" /> </component> </module>

swift/.idea/swift.iml/0

# Copyright (c) Alibaba, Inc. and its affiliates. # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License import os import datasets import pandas as pd _CITATION = """\ @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} } """ _DESCRIPTION = """\ C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. """ _HOMEPAGE = "https://cevalbenchmark.com" _LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" _URL = r"https://modelscope.oss-cn-beijing.aliyuncs.com/open_data/c-eval/ceval-exam.zip" task_list = [ "computer_network", "operating_system", "computer_architecture", "college_programming", "college_physics", "college_chemistry", "advanced_mathematics", "probability_and_statistics", "discrete_mathematics", "electrical_engineer", "metrology_engineer", "high_school_mathematics", "high_school_physics", "high_school_chemistry", "high_school_biology", "middle_school_mathematics", "middle_school_biology", "middle_school_physics", "middle_school_chemistry", "veterinary_medicine", "college_economics", "business_administration", "marxism", "mao_zedong_thought", "education_science", "teacher_qualification", "high_school_politics", "high_school_geography", "middle_school_politics", "middle_school_geography", "modern_chinese_history", "ideological_and_moral_cultivation", "logic", "law", "chinese_language_and_literature", "art_studies", "professional_tour_guide", "legal_professional", "high_school_chinese", "high_school_history", "middle_school_history", "civil_servant", "sports_science", "plant_protection", "basic_medicine", "clinical_medicine", "urban_and_rural_planner", "accountant", "fire_engineer", "environmental_impact_assessment_engineer", "tax_accountant", "physician", ] class CevalExamConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super().__init__(version=datasets.Version("1.0.0"), **kwargs) class CevalExam(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ CevalExamConfig( name=task_name, ) for task_name in task_list ] def _info(self): features = datasets.Features( { "id":datasets.Value("int32"), "question": datasets.Value("string"), "A": datasets.Value("string"), "B": datasets.Value("string"), "C": datasets.Value("string"), "D": datasets.Value("string"), "answer": datasets.Value("string"), "explanation":datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(_URL) task_name = self.config.name return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join( data_dir, "test", f"{task_name}_test.csv" ), }, ), datasets.SplitGenerator( name=datasets.Split("val"), gen_kwargs={ "filepath": os.path.join( data_dir, "val", f"{task_name}_val.csv" ), }, ), datasets.SplitGenerator( name=datasets.Split("dev"), gen_kwargs={ "filepath": os.path.join( data_dir, "dev", f"{task_name}_dev.csv" ), }, ), ] def _generate_examples(self, filepath): df = pd.read_csv(filepath,encoding="utf-8") for i, instance in enumerate(df.to_dict(orient="records")): if "answer" not in instance.keys(): instance["answer"]="" if "explanation" not in instance.keys(): instance["explanation"]="" yield i, instance

swift/benchmarks/modelscope_ceval-exam/modelscope/ceval-exam/master/meta/ceval-exam.py/0

MIT

swift/benchmarks/modelscope_mmlu/mmlu/clinical_knowledge/1.0.0/fedfb5e4f551779e93567fbaaa992d74323de5ed8041b2a38b33dc9af632e3f5/LICENSE/0

MIT

swift/benchmarks/modelscope_mmlu/mmlu/high_school_computer_science/1.0.0/fedfb5e4f551779e93567fbaaa992d74323de5ed8041b2a38b33dc9af632e3f5/LICENSE/0

MIT

swift/benchmarks/modelscope_mmlu/mmlu/logical_fallacies/1.0.0/fedfb5e4f551779e93567fbaaa992d74323de5ed8041b2a38b33dc9af632e3f5/LICENSE/0

MIT

swift/benchmarks/modelscope_mmlu/mmlu/security_studies/1.0.0/fedfb5e4f551779e93567fbaaa992d74323de5ed8041b2a38b33dc9af632e3f5/LICENSE/0

# Grok 300B训练和推理实战 本文介绍了使用8卡环境对Grok-MoE 300B模型进行微调和推理的流程。 ## 目录 - [环境准备](#环境准备) - [微调](#微调) - [推理](#推理) ## 环境准备 ```shell git clone https://github.com/modelscope/swift.git cd swift pip install -e '.[llm]' ``` ## 微调 ### 实验环境 - GPU:8*A100 80G - 镜像:ModelScope官方镜像1.13.1版本 - peft:0.10.0 ### 数据集准备 Grok是base模型，因此我们使用了[问题生成数据集DuReader](https://www.modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)作为训练集。该数据集约15000条，max-length设置为512，训练数据约10000条（平均长度305±92 tokens）。 ### 模型准备 Grok模型我们使用了[ColossalAI提供的版本](https://www.modelscope.cn/models/colossalai/grok-1-pytorch/summary)，其中我们额外准备了[符合transformers标准的tokenizer](https://www.modelscope.cn/models/AI-ModelScope/grok-1-tokenizer/summary)。 ### 训练 由于Grok模型过大，device_map和deepspeed zero3非offload均无法运行训练，因此本次实验我们使用了LoRA+deepspeed zero3 offload模式运行训练。训练完整脚本如下： ```shell # cd examples/pytorch/llm first nproc_per_node=8 PYTHONPATH=../../.. \ torchrun \ --nproc_per_node=$nproc_per_node \ --master_port 29500 \ llm_sft.py \ --model_type grok-1 \ --sft_type lora \ --tuner_backend peft \ --dtype bf16 \ --output_dir output \ --ddp_backend nccl \ --dataset dureader-robust-zh \ --train_dataset_sample -1 \ --num_train_epochs 1 \ --max_length 512 \ --check_dataset_strategy warning \ --lora_rank 8 \ --lora_alpha 32 \ --lora_dropout_p 0.05 \ --lora_dtype AUTO \ --lora_target_modules DEFAULT \ --gradient_checkpointing true \ --batch_size 2 \ --weight_decay 0.1 \ --learning_rate 1e-4 \ --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \ --max_grad_norm 0.5 \ --warmup_ratio 0.03 \ --eval_steps 100 \ --save_steps 100 \ --save_total_limit 2 \ --logging_steps 10 \ --deepspeed zero3-offload \ ``` 完整的训练文件可以在[这里](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/grok-1/lora_ddp_ds)找到。 下面是训练过程的一些benchmark： | 指标 | 值 | | -------- | ------------------------------------------------------------ | | 显存占用 | 8*21G | | 训练速度 | 45s/it | | 总iter数 | 340（10000(dataset_length)/16(gradient_accumulation)/2(batch_size)） | <img src="../../resources/image-20240329122854204.png" alt="image-20240329122854204" style="zoom: 33%;" /> 由于显存占用不到24G，理论上可以在RTX3090/A10环境中运行训练。 <img src="../../resources/grok_train_loss.png" alt="train_loss (1)" style="zoom:33%;" /> <img src="../../resources/grok_train_acc.png" alt="train_acc" style="zoom:33%;" /> 训练时长约4小时。 ### 推理 SWIFT框架目前并不支持deepspeed推理，因此我们仍然使用transformers的device_map进行推理支持。但由于模型过大，因此部分layers会被offload到CPU上，并影响LoRA加载使推理出错，因此我们针对peft的实现进行了一定patch（原Linear在meta设备上时不迁移LoRA，并在运行时动态迁移weights）。 推理脚本如下： ```shell # cd examples/pytorch/llm first PYTHONPATH=../../.. \ python llm_infer.py \ --ckpt_dir output/grok-1/vx-xxx-xxx/checkpoint-xxx \ --dtype bf16 \ --load_dataset_config true \ --max_new_tokens 64 \ --do_sample true \ --dtype bf16 \ --eval_human false \ --merge_lora false \ ``` 推理结果： ```text [PROMPT]Task: Question Generation Context: 我个人感觉是吕颂贤版,剧情和原著差别不大,虽然TVB演员颜值和风光没有大陆的好。但是香港特区人口和地域的限制,只能注重在演员的演技方面发挥很出色,楼主看过大陆排《笑傲江湖》吧!在台词上表现的很生硬没有香港的注重神色配台词,比如杜燕歌把吕颂贤表情和性格几乎和原著差别不大。武打几乎沿用徐克和程小东动作的风格很注重实际技巧,没有大陆版的在武打场面依靠电脑特效表现的太夸张了。李亚鹏版的武打动作和导演还是香港的元彬,大陆毕竟还是在武侠剧起步的比较晚,主要是还是靠明星大腕压阵而香港却是恰恰相反。 Answer: 吕颂贤版 Question:[OUTPUT]笑傲江湖哪个版本好看</s> [LABELS]笑傲江湖哪个版本好看 -------------------------------------------------- [PROMPT]Task: Question Generation Context: 这位朋友你好,女性出现妊娠反应一般是从6-12周左右,也就是女性怀孕1个多月就会开始出现反应,第3个月的时候,妊辰反应基本结束。 而大部分女性怀孕初期都会出现恶心、呕吐的感觉,这些症状都是因人而异的,除非恶心、呕吐的非常厉害,才需要就医,否则这些都是刚怀孕的的正常症状。1-3个月的时候可以观察一下自己的皮肤,一般女性怀孕初期可能会产生皮肤色素沉淀或是腹壁产生妊娠纹,特别是在怀孕的后期更加明显。 还有很多女性怀孕初期会出现疲倦、嗜睡的情况。怀孕三个月的时候,膀胱会受到日益胀大的子宫的压迫,容量会变小,所以怀孕期间也会有尿频的现象出现。月经停止也是刚怀孕最容易出现的症状,只要是平时月经正常的女性,在性行为后超过正常经期两周,就有可能是怀孕了。 如果你想判断自己是否怀孕,可以看看自己有没有这些反应。当然这也只是多数人的怀孕表现,也有部分女性怀孕表现并不完全是这样,如果你无法确定自己是否怀孕,最好去医院检查一下。 Answer: 6-12周 Question:[OUTPUT]怀孕几个月开始反应</s> [LABELS]怀孕多久会有反应 -------------------------------------------------- ```

swift/docs/source/LLM/Grok训练和推理.md/0

# 自定义与拓展 ## 目录 - [自定义数据集](#自定义数据集) - [自定义模型](#自定义模型) - [自定义对话模板](#自定义对话模板) ## 自定义数据集 我们支持三种**自定义数据集**的方法. 1. 【推荐】直接命令行传参的方式，指定`--dataset xxx.json yyy.jsonl zzz.csv`, **更加方便支持自定义数据集**, 支持五种数据集格式（即使用`SmartPreprocessor`，支持的数据集格式见下方）, 支持`dataset_id`和`dataset_path`. 不需要修改`dataset_info.json`文件. 2. 添加数据集到`dataset_info.json`中, 比第一种方式更灵活但繁琐, 支持对数据集使用两种预处理器并指定其参数: `RenameColumnsPreprocessor`, `ConversationsPreprocessor`（默认使用`SmartPreprocessor`）. 支持直接修改swift内置的`dataset_info.json`, 或者通过`--custom_dataset_info xxx.json`的方式传入外置的json文件（方便pip install而非git clone的用户拓展数据集）. 3. **注册数据集**的方式: 比第1、2种方式更加灵活但繁琐, 支持使用函数对数据集进行预处理. 方法1、2在实现上借助了方法3. 可以直接修改源码进行拓展, 或者通过`--custom_register_path xxx.py`的方式传入, 脚本会对py文件进行解析（方便pip install的用户）. ### 📌 【推荐】直接命令行传参 支持直接传入行自定义的**dataset_id**(兼容MS和HF)和**dataset_path**, 以及同时传入多个自定义数据集以及对应采样数, 脚本会进行自动的预处理和拼接. 如果传入的是`dataset_id`, 默认会使用dataset\_id中的'default'子数据集, 并设置split为'train'. 如果该dataset\_id已经注册, 则会使用注册时传入的subsets、split以及预处理函数. 如果传入的是`dataset_path`, 则可以指定为相对路径和绝对路径, 其中相对路径为相对于当前运行目录. ```bash --dataset {dataset_id} {dataset_path} # 数据集混合: 以下取dataset_id中subset1和subset2子数据集并采样20000条 --dataset {dataset_name}#20000 {dataset_id}:{subset1}/{subset2}#20000 {dataset_path}#10000 ``` 脚本支持的文件格式包含`csv`, `json`, `jsonl`格式. 你需要将传入的文件符合以下数据集格式（只列出了一部分）. 以下格式都支持system (需要注意的是, csv如果指定了system字段, 则无法设置为`None`, 只能指定为空字符串. json和jsonl没有这个限制). `json`, `jsonl`格式的文件支持多轮对话 (`csv`不支持). **格式1:** 预训练: ```csv response 11111 aaaaa AAAAA ``` ```jsonl {"response": "11111"} {"response": "aaaaa"} {"response": "AAAAA"} ``` 单轮对话: ```csv system,query,response 00000,11111,22222 00001,aaaaa,bbbbb 00002,AAAAA,BBBBB ``` ```jsonl {"system": "00000", "query": "11111", "response": "22222"} {"query": "aaaaa", "response": "bbbbb"} {"query": "AAAAA", "response": "BBBBB"} ``` 多轮对话: ```jsonl {"system": "00000", "query": "55555", "response": "66666"} {"query": "eeeee", "response": "fffff", "history": []} {"query": "EEEEE", "response": "FFFFF", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]]} ``` ```json [{"system": "00000", "query": "55555", "response": "66666"}, {"query": "eeeee", "response": "fffff", "history": []}, {"query": "EEEEE", "response": "FFFFF", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]]}] ``` **格式2:** ```jsonl {"conversations": [{"from": "system", "value": "00000"}, {"from": "user", "value": "11111"}, {"from": "assistant", "value": "22222"}]} {"conversations": [{"from": "user", "value": "aaaaa"}, {"from": "assistant", "value": "bbbbb"}, {"from": "user", "value": "ccccc"}, {"from": "assistant", "value": "ddddd"}]} {"conversations": [{"from": "user", "value": "AAAAA"}, {"from": "assistant", "value": "BBBBB"}, {"from": "user", "value": "CCCCC"}, {"from": "assistant", "value": "DDDDD"}]} ``` **格式3:** ```jsonl {"messages": [{"role": "system", "content": "00000"}, {"role": "user", "content": "11111"}, {"role": "assistant", "content": "22222"}]} {"messages": [{"role": "user", "content": "aaaaa"}, {"role": "assistant", "content": "bbbbb"}, {"role": "user", "content": "ccccc"}, {"role": "assistant", "content": "ddddd"}]} {"messages": [{"role": "user", "content": "AAAAA"}, {"role": "assistant", "content": "BBBBB"}, {"role": "user", "content": "CCCCC"}, {"role": "assistant", "content": "DDDDD"}]} ``` **格式4:** ```jsonl {"system": "00000", "conversation": [{"human": "11111", "assistant": "22222"}]} {"conversation": [{"human": "aaaaa", "assistant": "bbbbb"}]} {"conversation": [{"human": "AAAAA", "assistant": "BBBBB"}, {"human": "CCCCC", "assistant": "DDDDD"}, {"human": "EEEEE", "assistant": "FFFFF"}]} ``` **格式5:** ```csv system,instruction,input,output 00000,11111,22222,33333 00001,aaaaa,bbbbb,ccccc 00002,AAAAA,BBBBB,CCCCC ``` **人类对齐（DPO/ORPO/SimPO/CPO）** ```jsonl {"system": "123", "query": "11111", "response": "22222", "rejected_response": "33333", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]]} {"system": "123", "query": "aaaaa", "response": "bbbbb", "rejected_response": "ccccc", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]]} {"system": "123", "query": "AAAAA", "response": "BBBBB", "rejected_response": "CCCCC", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]]} ``` 其中`system`和`history`为可选项 **Tool-Calling Agent** 格式1 ```jsonl {"tools":"{API_LIST}","conversations": [{"from": "system", "value": "00000"}, {"from": "user", "value": "11111"}, {"from": "assistant", "value": "22222"}]} {"tools":"{API_LIST}","conversations": [{"from": "user", "value": "aaaaa"}, {"from": "assistant", "value": "bbbbb"}, {"from": "tool", "value": "ccccc"}, {"from": "assistant", "value": "ddddd"}]} {"tools":"{API_LIST}","conversations": [{"from": "user", "value": "AAAAA"}, {"from": "assistant", "value": "BBBBB"}, {"from": "tool", "value": "CCCCC"}, {"from": "assistant", "value": "DDDDD"}]} ``` 格式2 ```jsonl {"tools":"{API_LIST}","messages": [{"role": "system", "content": "00000"}, {"role": "user", "content": "11111"}, {"role": "assistant", "content": "22222"}]} {"tools":"{API_LIST}","messages": [{"role": "user", "content": "aaaaa"}, {"role": "assistant", "content": "bbbbb"}, {"role": "tool", "content": "ccccc"}, {"role": "assistant", "content": "ddddd"}]} {"tools":"{API_LIST}","messages": [{"role": "user", "content": "AAAAA"}, {"role": "assistant", "content": "BBBBB"}, {"role": "tool", "content": "CCCCC"}, {"role": "assistant", "content": "DDDDD"}]} ``` 其中tools格式参考[Agent部署文档](./Agent部署最佳实践.md#tools字段), 你可以通过设置`--tools_prompt`来选择对应的prompt `tool`字段表示工具调用返回结果 ### 添加dataset_info.json 可以参考[swift内置的dataset_info.json](https://github.com/modelscope/swift/blob/main/swift/llm/data/dataset_info.json)进行数据集拓展. 你可以直接在内置的dataset_info.json中添加, 也可以通过`--custom_dataset_info 1.json`传入外置的dataset_info.json的路径、json字符串或者字典. 添加dataset\_id: ```python # MS # 使用: `--dataset <dataset_name>` "<dataset_name>": { "dataset_id": "xxx/xxx" } # HF # 使用: `--dataset HF::<dataset_name>` 或者 直接使用`USE_HF`环境变量. "<dataset_name>": { "hf_dataset_id": "xxx/xxx" } ``` 添加dataset\_path: ```python # 可以指定相对路径和绝对路径. 相对路径相对于dataset_info.json文件所在目录. # 使用: `--dataset <dataset_name>` "<dataset_name>": { "dataset_path": "xxx" } ``` 支持以下参数: - dataset\_id: 数据集对应的ModelScope的dataset\_id, 默认为`None`. 最简的设置必须指定`dataset_id`、`hf_dataset_id`和`dataset_path`中的一个. - subsets: 子数据集的名字列表, 默认为`[]`, 即使用'default'子数据集. - split: 默认为`['train']`, 通常不需要修改. - hf\_dataset\_id: 数据集对应的HuggingFace的datasset\_id, 默认为`None`. - dataset\_path: 用于指定数据集的本地路径, e.g. 1.jsonl等, 默认为`None`. 可以传入相对路径和绝对路径. 如果使用相对路径, 则相对于`dataset_info.json`文件所在目录. 如果设置了dataset\_path, 那么dataset\_id, subsets, hf\_dataset\_id参数失效. - columns: 默认使用的预处理器为`SmartPreprocessor`, 指定此参数则指定为`RenameColumnsPreprocessor`, 你需要rename数据集中的列并转换为上述**格式1**的样式. - conversations: 指定此参数则指定预处理器为`ConversationsPreprocessor` ('columns'的优先级高于'conversations'). - remove\_useless\_columns: 指定是否移除无用的列 (包括: 'query', 'response', 'rejected\_response', 'system', 'history', 'images'), 默认为`True`, 通常不需要设置. - tags: 用于注释数据集, 默认为`[]`, 通常不需要设置. 如果`dataset_info.json`中参数无法满足您的要求, 例如你需要添加自定义的prompt、需要对数据集提前进行清洗或者进行复杂的数据集获取与预处理, 则可以使用注册数据集的方式, 使用函数的方式来进行数据获取与预处理. ### 注册数据集的方式 以下是一个**注册数据集**的案例. 完整的py文件可以查看[custom.py](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/custom.py), sh脚本可以查看[custom](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/custom). 你可以通过指定`--custom_register_path xxx.py`对注册的内容进行解析. ```python from typing import Optional, Tuple from datasets import Dataset as HfDataset from modelscope import MsDataset from swift.llm import get_dataset, register_dataset, get_dataset_from_repo from swift.utils import get_logger logger = get_logger() class CustomDatasetName: stsb_en = 'stsb-en' def _preprocess_stsb(dataset: HfDataset) -> HfDataset: prompt = """Task: Based on the given two sentences, provide a similarity score between 0.0 and 5.0. Sentence 1: {text1} Sentence 2: {text2} Similarity score: """ query = [] response = [] for d in dataset: query.append(prompt.format(text1=d['text1'], text2=d['text2'])) response.append(f"{d['label']:.1f}") return HfDataset.from_dict({'query': query, 'response': response}) register_dataset(CustomDatasetName.stsb_en, 'huangjintao/stsb', None, _preprocess_stsb, get_dataset_from_repo) if __name__ == '__main__': # test dataset train_dataset, val_dataset = get_dataset([CustomDatasetName.stsb_en], check_dataset_strategy='warning') print(f'train_dataset: {train_dataset}') print(f'val_dataset: {val_dataset}') ``` `register_dataset`会在`DATASET_MAPPING`中注册数据集, 该函数的参数含义如下: - `dataset_name`: 必填项, 表示数据集的名字, 也是数据集的唯一id. - `dataset_id_or_path`: 必填项. 表示数据集在ModelScope Hub上的`dataset_id`或者本地的`dataset_dir`. - `subsets`: 数据集的子数据集列表, 默认为`[]`. - `preprocess_func`: 预处理函数. - `get_function`: 默认值为`None`. 获取数据集的函数. 如果传入None, 则使用修饰器方案进行数据集注册. 如果传入一个函数, 则使用正常方案进行注册. > `get_function`需要返回`HfDataset`或`Tuple[HfDataset, Optional[HfDataset]]`. 如果只返回一个数据集, 则该数据集为train\_dataset. 如果返回两个数据集, 则分别作为train\_dataset和val\_dataset. `get_dataset`函数支持获取多个数据集, 例如:`get_dataset(['dataset1', 'dataset2'])`. 我们会将各个子数据集的训练集和验证集部分分别进行拼接, 最终返回合并后的训练集和验证集. > 函数返回的`HfDataset`需要符合一定的规范. 如果你要进行**预训练**, 那么只需要包含`response`字段, 具体可以参考`'tigerbot-law-zh'`数据集. 如果是**指令微调(单轮对话)**的情况下, 需包含`query`, `response`字段, 分别代表指令微调的用户询问和AI助手的回答, 具体可以参考`'alpaca-zh'`数据集. 如果是**多轮对话**, 则需要额外加上`history`字段, 代表对话的历史信息, 具体可以参考`'damo-agent-mini-zh'`数据集. 如果每个数据集样例具有不同的`system`, 则需要额外加上system字段, 具体你也可以参考`'damo-agent-mini-zh'`数据集. - `**kwargs`: 其他用于注释数据集的参数. 该参数一般不需要设置. ## 自定义模型 以下是一个**自定义模型**的案例. 完整的py文件可以查看[custom.py](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/custom.py), sh脚本可以查看[custom](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/custom). 你可以通过指定`--custom_register_path xxx.py`对注册的内容进行解析. ```python from typing import Any, Dict from modelscope import AutoConfig, AutoModelForCausalLM, AutoTokenizer from torch import dtype as Dtype from transformers.utils.versions import require_version from swift.llm import LoRATM, TemplateType, get_model_tokenizer, register_model from swift.utils import get_logger logger = get_logger() class CustomModelType: tigerbot_7b = 'tigerbot-7b' tigerbot_13b = 'tigerbot-13b' tigerbot_13b_chat = 'tigerbot-13b-chat' class CustomTemplateType: tigerbot = 'tigerbot' @register_model(CustomModelType.tigerbot_7b, 'TigerResearch/tigerbot-7b-base-v3', LoRATM.llama, TemplateType.default_generation) @register_model(CustomModelType.tigerbot_13b, 'TigerResearch/tigerbot-13b-base-v2', LoRATM.llama, TemplateType.default_generation) @register_model(CustomModelType.tigerbot_13b_chat, 'TigerResearch/tigerbot-13b-chat-v4', LoRATM.llama, CustomTemplateType.tigerbot) def get_tigerbot_model_tokenizer(model_dir: str, torch_dtype: Dtype, model_kwargs: Dict[str, Any], load_model: bool = True, **kwargs): use_flash_attn = kwargs.pop('use_flash_attn', False) if use_flash_attn: require_version('transformers>=4.34') logger.info('Setting use_flash_attention_2: True') model_kwargs['use_flash_attention_2'] = True model_config = AutoConfig.from_pretrained( model_dir, trust_remote_code=True) model_config.pretraining_tp = 1 model_config.torch_dtype = torch_dtype logger.info(f'model_config: {model_config}') tokenizer = AutoTokenizer.from_pretrained( model_dir, trust_remote_code=True) model = None if load_model: model = AutoModelForCausalLM.from_pretrained( model_dir, config=model_config, torch_dtype=torch_dtype, trust_remote_code=True, **model_kwargs) return model, tokenizer if __name__ == '__main__': # test model base model, tokenizer = get_model_tokenizer( CustomModelType.tigerbot_7b, use_flash_attn=False) print(model.__class__.__name__) # test model chat model, tokenizer = get_model_tokenizer( CustomModelType.tigerbot_13b_chat, use_flash_attn=False) print(model.__class__.__name__) ``` `register_model`会在`MODEL_MAPPING`中注册模型, 该函数的参数含义如下: - `model_type`: 必填项. 表示模型的名字, 也是唯一的id. - `model_id_or_path`: 必填项. 表示模型在ModelScope Hub中的`model_id`, 或者是本地的模型目录`model_dir`. - `lora_target_modules`: 默认为`None`. 表示在sh脚本中指定`--lora_target_modules DEFAULT`或`--lora_target_modules AUTO`或未指定`--lora_target_modules`情况下默认使用的lora_target_modules. - `template`: 默认为`TemplateType.default`. 表示在sh脚本中指定`--template_type AUTO`或未指定`--template_type`情况下默认使用的对话模板. - `get_function`: 默认值为`None`. 获取model和tokenizer的函数. 如果传入None, 则使用修饰器方案进行模型注册. 如果传入一个函数, 则使用正常方案进行注册. - `requires`: 默认为`[]`. 表示模型所需要的区别于其他模型的依赖. 该参数一般不需要设置. - `torch_dtype`: 默认为`None`. 表示模型所推荐使用的torch_dtype. 该参数一般不需要设置. - `revision`: 默认为`None`. 用于指定模型的版本号. 如果`model_id_or_path`是本地的模型目录, 则该参数失效. 该参数一般不需要设置. - `ignore_file_pattern`: 默认为`None`. 表示下载的时候需要忽略的文件名的正则pattern, 该参数会传递给`snapshot_download`. 例如`r'.+\.bin$'`, `r'.+\.savetensors$'`等. 该参数一般不需要设置. - `**kwargs`: 其他用于注释模型能力的参数. 该参数一般不需要设置. ## 自定义对话模板 以下是一个**自定义模型**的案例. 完整的py文件可以查看[custom.py](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/custom.py), sh脚本可以查看[custom](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/custom). ```python from swift.llm import (Template, ModelType, dataset_map, get_model_tokenizer, get_template, get_dataset, print_example, register_template, DatasetName) from swift.utils import get_logger logger = get_logger() class CustomTemplateType: tigerbot = 'tigerbot' # Ref: https://github.com/TigerResearch/TigerBot/blob/main/infer.py register_template( CustomTemplateType.tigerbot, Template(['{{SYSTEM}}'], ['\n\n### Instruction:\n{{QUERY}}\n\n### Response:\n'], [], [['eos_token_id']])) if __name__ == '__main__': # test template train_dataset, _ = get_dataset(DatasetName.blossom_math_zh) _, tokenizer = get_model_tokenizer(ModelType.qwen_7b_chat, load_model=False) template = get_template(CustomTemplateType.tigerbot, tokenizer) train_dataset = dataset_map(train_dataset, template.encode) print_example(train_dataset[0], tokenizer) ``` `register_template`会在`TEMPLATE_MAPPING`中注册对话模板, 该函数的参数含义如下: - `template_type`: 必填项, 表示对话模板的名字, 也是template的唯一id. - `template`: 必填项, 需要传入一个`Template`. 初始化`Template`需要传入以下参数: `prefix`, `prompt`, `chat_sep`, `suffix`, `default_system`. 模板初始化函数会根据这四个内容, 获取完整的chat template. 其中这四个配置内容的含义如下. - `prefix`: 表示对话模板中的前缀部分, 一般为system部分, 前缀token, bos token等内容. 我们使用`{{SYSTEM}}`作为system的占位符. 如果`{{SYSTEM}}`没有在prefix中存在, 则该Template不支持system, e.g. `damo-agent-mini-zh`数据集. - `prompt`: 表示对话模板中的一轮对话. 我们使用`{{QUERY}}`作为每轮对话中, human询问部分的占位符, `{{ROUND0}}`则表示本次对话是第几轮的占位符, 从0开始计数, `{{ROUND1}}`从1开始计数. AI助手的回复部分会拼接在`prompt`的后面, 因此我们没有设计其占位符. 我们只会对AI助手的回复部分计算损失. - `chat_sep`: 如果需要进行多轮对话, `chat_sep`会作为每轮对话之间的分隔符, 例如: 换行等. 如果设置为None, 则该Template不支持多轮对话. - `suffix`: 作为对话模板的后缀部分, 一般为eos token. 会拼接在最后一轮的对话后面. - `default_system`: 默认的system.

swift/docs/source/LLM/自定义与拓展.md/0

# Qwen-Audio 最佳实践 ## 目录 - [环境准备](#环境准备) - [推理](#推理) - [微调](#微调) - [微调后推理](#微调后推理) ## 环境准备 ```shell pip install 'ms-swift[llm]' -U ``` ## 推理 推理[qwen-audio-chat](https://modelscope.cn/models/qwen/Qwen-Audio-Chat/summary): ```shell # Experimental environment: A10, 3090, V100... # 21GB GPU memory CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen-audio-chat ``` 输出: (支持传入本地路径或URL) ```python """ <<< multi-line [INFO:swift] End multi-line input with `#`. [INFO:swift] Input `single-line` to switch to single-line input mode. <<<[M] 你是谁？# 我是来自达摩院的大规模语言模型，我叫通义千问。 -------------------------------------------------- <<<[M] Audio 1:<audio>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/music.wav</audio> 这是首什么样的音乐# 这是电子、实验流行风格的音乐。 -------------------------------------------------- <<<[M] Audio 1:<audio>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/weather.wav</audio> 这段语音说了什么# 这段语音中说了中文："今天天气真好呀"。 -------------------------------------------------- <<<[M] 这段语音是男生还是女生# 根据音色判断，这段语音是男性。 """ ``` **单样本推理** ```python import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' from swift.llm import ( get_model_tokenizer, get_template, inference, ModelType, get_default_template_type, inference_stream ) from swift.utils import seed_everything import torch model_type = ModelType.qwen_audio_chat template_type = get_default_template_type(model_type) print(f'template_type: {template_type}') model, tokenizer = get_model_tokenizer(model_type, torch.float16, model_kwargs={'device_map': 'auto'}) model.generation_config.max_new_tokens = 256 template = get_template(template_type, tokenizer) seed_everything(42) query = """Audio 1:<audio>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/weather.wav</audio> 这段语音说了什么""" response, history = inference(model, template, query) print(f'query: {query}') print(f'response: {response}') # 流式 query = '这段语音是男生还是女生' gen = inference_stream(model, template, query, history) print_idx = 0 print(f'query: {query}\nresponse: ', end='') for response, history in gen: delta = response[print_idx:] print(delta, end='', flush=True) print_idx = len(response) print() print(f'history: {history}') """ query: Audio 1:<audio>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/weather.wav</audio> 这段语音说了什么 response: 这段语音说了中文："今天天气真好呀"。 query: 这段语音是男生还是女生 response: 根据音色判断，这段语音是男性。 history: [['Audio 1:<audio>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/weather.wav</audio>\n这段语音说了什么', '这段语音说了中文："今天天气真好呀"。'], ['这段语音是男生还是女生', '根据音色判断，这段语音是男性。']] """ ``` ## 微调 多模态大模型微调通常使用**自定义数据集**进行微调. 这里展示可直接运行的demo: LoRA微调: (默认只对LLM部分的qkv进行lora微调. 如果你想对所有linear含audio模型部分都进行微调, 可以指定`--lora_target_modules ALL`) ```shell # Experimental environment: A10, 3090, V100... # 22GB GPU memory CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type qwen-audio-chat \ --dataset aishell1-mini-zh \ ``` 全参数微调: ```shell # MP # Experimental environment: 2 * A100 # 2 * 50 GPU memory CUDA_VISIBLE_DEVICES=0,1 swift sft \ --model_type qwen-audio-chat \ --dataset aishell1-mini-zh \ --sft_type full \ # ZeRO2 # Experimental environment: 4 * A100 # 4 * 80 GPU memory NPROC_PER_NODE=4 CUDA_VISIBLE_DEVICES=0,1,2,3 swift sft \ --model_type qwen-audio-chat \ --dataset aishell1-mini-zh \ --sft_type full \ --use_flash_attn true \ --deepspeed default-zero2 ``` [自定义数据集](../LLM/自定义与拓展.md#-推荐命令行参数的形式)支持json, jsonl样式, 以下是自定义数据集的例子: (支持多轮对话, 支持每轮对话含多段语音或不含语音, 支持传入本地路径或URL) ```json [ {"conversations": [ {"from": "user", "value": "Audio 1:<audio>audio_path</audio>\n11111"}, {"from": "assistant", "value": "22222"} ]}, {"conversations": [ {"from": "user", "value": "Audio 1:<audio>audio_path</audio>\nAudio 2:<audio>audio_path2</audio>\nAudio 3:<audio>audio_path3</audio>\naaaaa"}, {"from": "assistant", "value": "bbbbb"}, {"from": "user", "value": "Audio 1:<audio>audio_path</audio>\nccccc"}, {"from": "assistant", "value": "ddddd"} ]}, {"conversations": [ {"from": "user", "value": "AAAAA"}, {"from": "assistant", "value": "BBBBB"}, {"from": "user", "value": "CCCCC"}, {"from": "assistant", "value": "DDDDD"} ]} ] ``` ## 微调后推理 直接推理: ```shell CUDA_VISIBLE_DEVICES=0 swift infer \ --ckpt_dir output/qwen-audio-chat/vx-xxx/checkpoint-xxx \ --load_dataset_config true \ ``` **merge-lora**并推理: ```shell CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir output/qwen-audio-chat/vx-xxx/checkpoint-xxx \ --merge_lora true CUDA_VISIBLE_DEVICES=0 swift infer \ --ckpt_dir output/qwen-audio-chat/vx-xxx/checkpoint-xxx-merged \ --load_dataset_config true ```

swift/docs/source/Multi-Modal/qwen-audio最佳实践.md/0

# Basic Usage "Tuner" refers to additional structures attached to a model to reduce the number of training parameters or improve training accuracy. Currently, SWIFT supports the following tuners: 1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/abs/2106.09685) 2. LoRA+: [LoRA+: Efficient Low Rank Adaptation of Large Models](https://arxiv.org/pdf/2402.12354.pdf) 3. LLaMA PRO: [LLAMA PRO: Progressive LLaMA with Block Expansion](https://arxiv.org/pdf/2401.02415.pdf) 4. GaLore: [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://arxiv.org/abs/2403.03507) 5. LISA: [LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning](https://arxiv.org/abs/2403.17919) 6. UnSloth: https://github.com/unslothai/unsloth 7. SCEdit: [SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing](https://arxiv.org/abs/2312.11392) < [arXiv](https://arxiv.org/abs/2312.11392) | [Project Page](https://scedit.github.io/) > 8. NEFTune: [Noisy Embeddings Improve Instruction Finetuning](https://arxiv.org/abs/2310.05914) 9. LongLoRA: [Efficient Fine-tuning of Long-Context Large Language Models](https://arxiv.org/abs/2309.12307) 10. Adapter: [Parameter-Efficient Transfer Learning for NLP](http://arxiv.org/abs/1902.00751) 11. Vision Prompt Tuning: [Visual Prompt Tuning](https://arxiv.org/abs/2203.12119) 12. Side: [Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks](https://arxiv.org/abs/1912.13503) 13. Res-Tuning: [Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone](https://arxiv.org/abs/2310.19859) < [arXiv](https://arxiv.org/abs/2310.19859) | [Project Page](https://res-tuning.github.io/) | [Usage](docs/source/GetStarted/ResTuning.md) > 14. Tuners provided by [PEFT](https://github.com/huggingface/peft), such as IA3, AdaLoRA, etc. ## Using in Training Call `Swift.prepare_model()` to add tuners to the model: ```python from modelscope import Model from swift import Swift, LoraConfig import torch model = Model.from_pretrained('ZhipuAI/chatglm3-6b', torch_dtype=torch.bfloat16, device_map='auto') lora_config = LoraConfig( r=16, target_modules=['query_key_value'], lora_alpha=32, lora_dropout=0.) model = Swift.prepare_model(model, lora_config) ``` Multiple tuners can also be used simultaneously: ```python from modelscope import Model from swift import Swift, LoraConfig, AdapterConfig import torch model = Model.from_pretrained('ZhipuAI/chatglm3-6b', torch_dtype=torch.bfloat16, device_map='auto') lora_config = LoraConfig( r=16, target_modules=['query_key_value'], lora_alpha=32, lora_dropout=0.) adapter_config = AdapterConfig( dim=model.config.hidden_size, target_modules=['mlp'], method_name='forward', hidden_pos=0, adapter_length=32, ) model = Swift.prepare_model(model, {'first_tuner': lora_config, 'second_tuner': adapter_config}) # use model to do other things ``` When using multiple tuners, the second parameter should be a Dict where the key is the tuner name and the value is the tuner configuration. After training, you can call: ```python model.save_pretrained(save_directory='./output') ``` to store the model checkpoint. The model checkpoint file will only include the weights of the tuners, not the weights of the model itself. The stored structure is as follows: > outputs > > ​ |-- configuration.json > > ​ |-- first_tuner > > ​ |-- adapter_config.json > > ​ |-- adapter_model.bin > > ​ |-- second_tuner > > ​ |-- adapter_config.json > > ​ |-- adapter_model.bin > > ​ |-- ... If only a single config is passed in, the default name `default` will be used: > outputs > > ​ |-- configuration.json > > ​ |-- default > > ​ |-- adapter_config.json > > ​ |-- adapter_model.bin > > ​ |-- ... ### Complete Training Code ```python # A100 18G memory from swift import Seq2SeqTrainer, Seq2SeqTrainingArguments from modelscope import MsDataset, AutoTokenizer from modelscope import AutoModelForCausalLM from swift import Swift, LoraConfig from swift.llm import get_template, TemplateType import torch # load model model = AutoModelForCausalLM.from_pretrained('ZhipuAI/chatglm3-6b', torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True) lora_config = LoraConfig( r=16, target_modules=['query_key_value'], lora_alpha=32, lora_dropout=0.05) model = Swift.prepare_model(model, lora_config) tokenizer = AutoTokenizer.from_pretrained('ZhipuAI/chatglm3-6b', trust_remote_code=True) dataset = MsDataset.load('AI-ModelScope/alpaca-gpt4-data-en', split='train') template = get_template(TemplateType.chatglm3, tokenizer, max_length=1024) def encode(example): inst, inp, output = example['instruction'], example.get('input', None), example['output'] if output is None: return {} if inp is None or len(inp) == 0: q = inst else: q = f'{inst}\n{inp}' example, kwargs = template.encode({'query': q, 'response': output}) return example dataset = dataset.map(encode).filter(lambda e: e.get('input_ids')) dataset = dataset.train_test_split(test_size=0.001) train_dataset, val_dataset = dataset['train'], dataset['test'] train_args = Seq2SeqTrainingArguments( output_dir='output', learning_rate=1e-4, num_train_epochs=2, eval_steps=500, save_steps=500, evaluation_strategy='steps', save_strategy='steps', dataloader_num_workers=4, per_device_train_batch_size=1, gradient_accumulation_steps=16, logging_steps=10, ) trainer = Seq2SeqTrainer( model=model, args=train_args, data_collator=template.data_collator, train_dataset=train_dataset, eval_dataset=val_dataset, tokenizer=tokenizer) trainer.train() ``` ## Using in Inference Use `Swift.from_pretrained()` to load the stored checkpoint: ```python from modelscope import Model from swift import Swift import torch model = Model.from_pretrained('ZhipuAI/chatglm2-6b', torch_dtype=torch.bfloat16, device_map='auto') model = Swift.from_pretrained(model, './output') ``` ### Complete Inference Code ```python # A100 14G memory import torch from modelscope import AutoModelForCausalLM, GenerationConfig from modelscope import AutoTokenizer from swift import Swift from swift.llm import get_template, TemplateType, to_device # load model model = AutoModelForCausalLM.from_pretrained('ZhipuAI/chatglm3-6b', torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True) model = Swift.from_pretrained(model, 'output/checkpoint-xxx') tokenizer = AutoTokenizer.from_pretrained('ZhipuAI/chatglm3-6b', trust_remote_code=True) template = get_template(TemplateType.chatglm3, tokenizer, max_length=1024) examples, tokenizer_kwargs = template.encode({'query': 'How are you?'}) if 'input_ids' in examples: input_ids = torch.tensor(examples['input_ids'])[None] examples['input_ids'] = input_ids token_len = input_ids.shape[1] generation_config = GenerationConfig( max_new_tokens=1024, temperature=0.3, top_k=25, top_p=0.8, do_sample=True, repetition_penalty=1.0, num_beams=10, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id) device = next(model.parameters()).device examples = to_device(examples, device) generate_ids = model.generate( generation_config=generation_config, **examples) generate_ids = template.get_generate_ids(generate_ids, token_len) print(tokenizer.decode(generate_ids, **tokenizer_kwargs)) # I'm an AI language model, so I don't have feelings or physical sensations. However, I'm here to assist you with any questions or tasks you may have. How can I help you today? ``` # Interface List ## Swift Class Static Interfaces - `Swift.prepare_model(model, config, **kwargs)` - Explain: Load a tuner onto the model. If it is a subclass of PeftConfig, use the corresponding interface of the Peft library to load the tuner. When using SwiftConfig, this interface can accept a SwiftModel instance and be called repeatedly, which has the same effect as passing a dictionary to config. - This interface supports parallel loading of multiple tuners of different types for simultaneous use. - Parameters: - `model`: An instance of `torch.nn.Module` or `SwiftModel`, the model to be loaded - `config`: An instance of `SwiftConfig`, `PeftConfig`, or a dictionary of custom tuner names to configs - Return value: An instance of `SwiftModel` or `PeftModel` - `Swift.merge_and_unload(model)` - Explain: Merge the LoRA weights back into the original model and completely unload the LoRA part - Parameters: - model: An instance of `SwiftModel` or `PeftModel`, the model instance with LoRA loaded - Return value: None - `Swift.merge(model)` - Explain: Merge the LoRA weights back into the original model without unloading the LoRA part - Parameters: - model: An instance of `SwiftModel` or `PeftModel`, the model instance with LoRA loaded - Return value: None - `Swift.unmerge(model)` - Explain: Split the LoRA weights from the original model weights back into the LoRA structure - Parameters: - model: An instance of `SwiftModel` or `PeftModel`, the model instance with LoRA loaded - Return value: None - `Swift.save_to_peft_format(ckpt_dir, output_dir)` - Explain: Convert the stored LoRA checkpoint to a Peft compatible format. The main changes are: - `default` will be split from the corresponding `default` folder into the output_dir root directory - The `{tuner_name}.` field in weights will be removed, for example `model.layer.0.self.in_proj.lora_A.default.weight` will become `model.layer.0.self.in_proj.lora_A.weight` - The prefix `basemodel.model` will be added to the keys in weights - Note: Only LoRA can be converted, other types of tuners cannot be converted due to Peft itself not supporting them. Additionally, when there are extra parameters like `dtype` set in LoRAConfig, it does not support conversion to Peft format. In this case, you can manually delete the corresponding fields in adapter_config.json - Parameters: - ckpt_dir: Original weights directory - output_dir: Target weights directory - Return value: None - `Swift.from_pretrained(model, model_id, adapter_name, revision, **kwargs)` - Explain: Load tuners from the stored weights directory onto the model. If adapter_name is not passed, all tuners under the model_id directory will be loaded. Same as `prepare_model`, this interface can be called repeatedly. - Parameters: - model: An instance of `torch.nn.Module` or `SwiftModel`, the model to be loaded - model_id: `str` type, the tuner checkpoint to be loaded, can be a ModelScope hub id or a local directory produced by training - adapter_name: `str` or `List[str]` or `Dict[str, str]` type or `None`, the tuner name in the tuner directory to be loaded. If `None`, all named tuners will be loaded. If `str` or `List[str]`, only certain specific tuners will be loaded. If `Dict`, the tuner indicated by `key` will be loaded and renamed to `value`. - revision: If model_id is a ModelScope id, revision can specify the corresponding version number ## SwiftModel Interface The following lists the interfaces that users may call. Other internal interfaces or interfaces not recommended for use can be viewed through the `make docs` command to generate the API Doc documentation. - `SwiftModel.create_optimizer_param_groups(self, **defaults)` - Explain: Create parameter groups based on the loaded tuners, currently only effective for the `LoRA+` algorithm - Parameters: - defaults: Default parameters for `optimizer_groups`, such as `lr` and `weight_decay` - Return value: - The created `optimizer_groups` - `SwiftModel.add_weighted_adapter(self, ...)` - Explain: Merge existing LoRA tuners into one - Parameters: - This interface is a transparent pass-through of PeftModel.add_weighted_adapter, parameters can refer to: [add_weighted_adapter documentation](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter) - `SwiftModel.save_pretrained(self, save_directory, safe_serialization, adapter_name)` - Explain: Store tuner weights - Parameters: - save_directory: Storage directory - safe_serialization: Whether to use safe_tensors, default is False - adapter_name: The adapter tuner to store, if not passed, all tuners will be stored by default - `SwiftModel.set_active_adapters(self, adapter_names, offload=None)` - Explain: Set the currently active adapters, adapters not in the list will be deactivated - In `inference`, the environment variable `USE_UNIQUE_THREAD=0/1` is supported, default value is `1`. If `0`, set_active_adapters only takes effect for the current thread. In this case, the tuners activated by this thread are used by default, and tuners in different threads do not interfere with each other. - Parameters: - adapter_names: Activated tuners - offload: How to handle deactivated adapters, default is `None` which means leave them in GPU memory. Both `cpu` and `meta` are supported, indicating offloading to cpu and meta devices to reduce GPU memory consumption. When `USE_UNIQUE_THREAD=0`, do not pass a value to offload to avoid affecting other threads. - Return value: None - `SwiftModel.activate_adapter(self, adapter_name)` - Explain: Activate a tuner - In `inference`, the environment variable `USE_UNIQUE_THREAD=0/1` is supported, default value is `1`. If `0`, activate_adapter only takes effect for the current thread. In this case, the tuners activated by this thread are used by default, and tuners in different threads do not interfere with each other. - Parameters: - adapter_name: The name of the tuner to activate - Return value: None - `SwiftModel.deactivate_adapter(self, adapter_name, offload)` - Explain: Deactivate a tuner - When the environment variable `USE_UNIQUE_THREAD=0`, do not call this interface - Parameters: - adapter_name: The name of the tuner to deactivate - offload: How to handle deactivated adapters, default is `None` which means leave them in GPU memory. Both `cpu` and `meta` are supported, indicating offloading to cpu and meta devices to reduce GPU memory consumption - Return value: None - `SwiftModel.get_trainable_parameters(self)` - Explain: Return training parameter information - Parameters: None - Return value: Training parameter information, format is as follows: ```text trainable params: 100M || all params: 1000M || trainable%: 10.00% || cuda memory: 10GiB. ```

swift/docs/source_en/GetStarted/Tuners.md/0

# LLM Quantization Documentation Swift supports the use of awq, gptq, bnb, hqq, and eetq technologies to quantize models. Among them, awq and gptq quantization technologies support vllm for accelerated inference, requiring the use of a calibration dataset for better quantization performance, but with slower quantization speed. On the other hand, bnb, hqq, and eetq do not require calibration data and have faster quantization speed. All five quantization methods support qlora fine-tuning. Quantization using awq and gptq requires the use of 'swift export', while bnb, hqq, and eetq can be quickly quantized during sft and infer. From the perspective of vllm inference acceleration support, it is more recommended to use awq and gptq for quantization. From the perspective of quantization effectiveness, it is more recommended to use awq, hqq, and gptq for quantization. And from the perspective of quantization speed, it is more recommended to use hqq for quantization. ## Table of Contents - [Environment Preparation](#environment-preparation) - [Original Model](#original-model) - [Fine-tuned Model](#fine-tuned-model) - [QLoRA](#QLoRA) - [Pushing Models](#pushing-models) ## Environment Preparation GPU devices: A10, 3090, V100, A100 are all supported. ```bash # Install ms-swift git clone https://github.com/modelscope/swift.git cd swift pip install -e '.[llm]' # Using AWQ quantization: # AutoAWQ and CUDA versions have a corresponding relationship, please select the version according to `https://github.com/casper-hansen/AutoAWQ` pip install autoawq -U # Using GPTQ quantization: # Auto_GPTQ and CUDA versions have a corresponding relationship, please select the version according to `https://github.com/PanQiWei/AutoGPTQ#quick-installation` pip install auto_gptq -U # Using bnb quantization: pip install bitsandbytes -U # Using hqq quantization: # Requires transformers version >4.40, install from source pip install git+https://github.com/huggingface/transformers pip install hqq # If compatibility with training is desired, install peft from source pip install git+https://github.com/huggingface/peft.git # Using eetq quantization: # Requires transformers version >4.40, install from source pip install git+https://github.com/huggingface/transformers # See https://github.com/NetEase-FuXi/EETQ for reference git clone https://github.com/NetEase-FuXi/EETQ.git cd EETQ/ git submodule update --init --recursive pip install . # If compatibility with training is desired, install peft from source pip install git+https://github.com/huggingface/peft.git # Environment alignment (usually not needed. If you encounter errors, you can run the code below, the repository uses the latest environment for testing) pip install -r requirements/framework.txt -U pip install -r requirements/llm.txt -U ``` ## Original Model ### awq, gptq Here we demonstrate AWQ and GPTQ quantization on the qwen1half-7b-chat model. ```bash # AWQ-INT4 quantization (takes about 18 minutes using A100, memory usage: 13GB) # If OOM occurs during quantization, you can appropriately reduce `--quant_n_samples` (default 256) and `--quant_seqlen` (default 2048). # GPTQ-INT4 quantization (takes about 20 minutes using A100, memory usage: 7GB) # AWQ: Use `alpaca-zh alpaca-en sharegpt-gpt4:default` as the quantization dataset CUDA_VISIBLE_DEVICES=0 swift export \ --model_type qwen1half-7b-chat --quant_bits 4 \ --dataset alpaca-zh alpaca-en sharegpt-gpt4:default --quant_method awq # GPTQ: Use `alpaca-zh alpaca-en sharegpt-gpt4:default` as the quantization dataset # For GPTQ quantization, please first refer to this issue: https://github.com/AutoGPTQ/AutoGPTQ/issues/439 OMP_NUM_THREADS=14 CUDA_VISIBLE_DEVICES=0 swift export \ --model_type qwen1half-7b-chat --quant_bits 4 \ --dataset alpaca-zh alpaca-en sharegpt-gpt4:default --quant_method gptq # AWQ: Use custom quantization dataset # Same for GPTQ CUDA_VISIBLE_DEVICES=0 swift export \ --model_type qwen1half-7b-chat --quant_bits 4 \ --dataset xxx.jsonl \ --quant_method awq # Inference using swift quantized model # AWQ CUDA_VISIBLE_DEVICES=0 swift infer \ --model_type qwen1half-7b-chat \ --model_id_or_path qwen1half-7b-chat-awq-int4 # GPTQ CUDA_VISIBLE_DEVICES=0 swift infer \ --model_type qwen1half-7b-chat \ --model_id_or_path qwen1half-7b-chat-gptq-int4 # Inference using original model CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat ``` ### bnb, hqq, eetq For bnb, hqq, and eetq, we only need to use `swift infer` for rapid quantization and inference. ```bash CUDA_VISIBLE_DEVICES=0 swift infer \ --model_type qwen1half-7b-chat \ --quant_method bnb \ --quantization_bit 4 CUDA_VISIBLE_DEVICES=0 swift infer \ --model_type qwen1half-7b-chat \ --quant_method hqq \ --quantization_bit 4 CUDA_VISIBLE_DEVICES=0 swift infer \ --model_type qwen1half-7b-chat \ --quant_method eetq \ --dtype fp16 ``` ## Fine-tuned Model Assume you fine-tuned qwen1half-4b-chat using LoRA, and the model weights directory is: `output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx`. Here we only introduce using the AWQ technique to quantize the fine-tuned model. Using GPTQ for quantization would be similar. **Merge-LoRA & Quantization** ```shell # Use `alpaca-zh alpaca-en sharegpt-gpt4:default` as the quantization dataset CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir 'output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx' \ --merge_lora true --quant_bits 4 \ --dataset alpaca-zh alpaca-en sharegpt-gpt4:default --quant_method awq # Use the dataset from fine-tuning as the quantization dataset CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir 'output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx' \ --merge_lora true --quant_bits 4 \ --load_dataset_config true --quant_method awq ``` **Inference using quantized model** ```shell # AWQ/GPTQ quantized models support VLLM inference acceleration. They also support model deployment. CUDA_VISIBLE_DEVICES=0 swift infer --ckpt_dir 'output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx-merged-awq-int4' ``` **Deploying the quantized model** Server: ```shell CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir 'output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx-merged-awq-int4' ``` Testing: ```shell curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "qwen1half-4b-chat", "messages": [{"role": "user", "content": "How to fall asleep at night?"}], "max_tokens": 256, "temperature": 0 }' ``` ## QLoRA ### awq, gptq If you want to fine-tune the models quantized with awq and gptq using QLoRA, you need to perform pre-quantization. For example, you can use `swift export` to quantize the original model. Then, for fine-tuning, you need to specify `--quant_method` to specify the corresponding quantization method using the following command: ```bash # awq CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type qwen1half-7b-chat \ --model_id_or_path qwen1half-7b-chat-awq-int4 \ --quant_method awq \ --sft_type lora \ --dataset alpaca-zh#5000 \ # gptq CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type qwen1half-7b-chat \ --model_id_or_path qwen1half-7b-chat-gptq-int4 \ --quant_method gptq \ --sft_type lora \ --dataset alpaca-zh#5000 \ ``` ### bnb, hqq, eetq If you want to use bnb, hqq, eetq for QLoRA fine-tuning, you need to specify `--quant_method` and `--quantization_bit` during training: ```bash # bnb CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type qwen1half-7b-chat \ --sft_type lora \ --dataset alpaca-zh#5000 \ --quant_method bnb \ --quantization_bit 4 \ --dtype fp16 \ # hqq CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type qwen1half-7b-chat \ --sft_type lora \ --dataset alpaca-zh#5000 \ --quant_method hqq \ --quantization_bit 4 \ # eetq CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type qwen1half-7b-chat \ --sft_type lora \ --dataset alpaca-zh#5000 \ --quant_method eetq \ --dtype fp16 \ ``` **Note** - hqq supports more customizable parameters, such as specifying different quantization configurations for different network layers. For details, please see [Command Line Arguments](https://github.com/modelscope/swift/blob/main/docs/source_en/LLM/Command-line-parameters.md). - eetq quantization uses 8-bit quantization, and there's no need to specify quantization_bit. Currently, bf16 is not supported; you need to specify dtype as fp16. - Currently, eetq's qlora speed is relatively slow; it is recommended to use hqq instead. For reference, see the [issue](https://github.com/NetEase-FuXi/EETQ/issues/17). ## Pushing Models Assume you fine-tuned qwen1half-4b-chat using LoRA, and the model weights directory is: `output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx`. ```shell # Push the original quantized model CUDA_VISIBLE_DEVICES=0 swift export \ --model_type qwen1half-7b-chat \ --model_id_or_path qwen1half-7b-chat-gptq-int4 \ --push_to_hub true \ --hub_model_id qwen1half-7b-chat-gptq-int4 \ --hub_token '<your-sdk-token>' # Push LoRA incremental model CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx \ --push_to_hub true \ --hub_model_id qwen1half-4b-chat-lora \ --hub_token '<your-sdk-token>' # Push merged model CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx \ --push_to_hub true \ --hub_model_id qwen1half-4b-chat-lora \ --hub_token '<your-sdk-token>' \ --merge_lora true # Push quantized model CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx \ --push_to_hub true \ --hub_model_id qwen1half-4b-chat-lora \ --hub_token '<your-sdk-token>' \ --merge_lora true \ --quant_bits 4 ```

swift/docs/source_en/LLM/LLM-quantization.md/0

# Llava Best Practice The document corresponds to the following models | model | model_type | |-------|------------| | [llava-v1.6-mistral-7b](https://modelscope.cn/models/AI-ModelScope/llava-v1.6-mistral-7b/summary) | llava1_6-mistral-7b-instruct | | [llava-v1.6-34b](https://www.modelscope.cn/models/AI-ModelScope/llava-v1.6-34b/summary) | llava1_6-yi-34b-instruct | |[llama3-llava-next-8b](https://modelscope.cn/models/AI-ModelScope/llama3-llava-next-8b/summary)|llama3-llava-next-8b| |[llava-next-72b](https://modelscope.cn/models/AI-ModelScope/llava-next-72b/summary)|llava-next-72b| |[llava-next-110b](https://modelscope.cn/models/AI-ModelScope/llava-next-110b/summary)|llava-next-110b| The following practices take `llava-v1.6-mistral-7b` as an example. You can also switch to other models by specifying the `--model_type`. ## Table of Contents - [Environment Setup](#environment-setup) - [Inference](#inference) - [Fine-tuning](#fine-tuning) - [Inference after Fine-tuning](#inference-after-fine-tuning) ## Environment Setup ```shell git clone https://github.com/modelscope/swift.git cd swift pip install -e '.[llm]' ``` ## Inference ```shell # Experimental environment: A100 # 20GB GPU memory CUDA_VISIBLE_DEVICES=0 swift infer --model_type llava1_6-mistral-7b-instruct # 70GB GPU memory CUDA_VISIBLE_DEVICES=0 swift infer --model_type llava1_6-yi-34b-instruct # 4*20GB GPU memory CUDA_VISIBLE_DEVICES=0,1,2,3 swift infer --model_type llava1_6-yi-34b-instruct ``` Output: (supports passing in local path or URL) ```python """ <<< Describe this image. Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png The image shows a close-up of a kitten with a soft, blurred background that suggests a natural, outdoor setting. The kitten has a mix of white and gray fur with darker stripes, typical of a tabby pattern. Its eyes are wide open, with a striking blue color that contrasts with the kitten's fur. The kitten's nose is small and pink, and its whiskers are long and white, adding to the kitten's cute and innocent appearance. The lighting in the image is soft and diffused, creating a gentle and warm atmosphere. The focus is sharp on the kitten's face, while the rest of the image is slightly out of focus, which draws attention to the kitten's features. -------------------------------------------------- <<< How many sheep are in the picture? Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png There are four sheep in the picture. -------------------------------------------------- <<< What is the calculation result? Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/math.png The calculation result is 14352 + 45304 = 145304. -------------------------------------------------- <<< Write a poem based on the content of the picture. Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/poem.png In the quiet of the night, A solitary boat takes flight, Across the water's gentle swell, Underneath the stars that softly fell. The boat, a vessel of the night, Carries but one, a lone delight, A solitary figure, lost in thought, In the tranquil calm, they find a wraith. The stars above, like diamonds bright, Reflect upon the water's surface light, Creating a path for the boat's journey, Guiding through the night with a gentle purity. The boat, a silent sentinel, In the stillness, it gently swells, A vessel of peace and calm, In the quiet of the night, it carries on. The figure on board, a soul at ease, In the serene embrace of nature's peace, They sail through the night, Under the watchful eyes of the stars' light. The boat, a symbol of solitude, In the vast expanse of the universe's beauty, A lone journey, a solitary quest, In the quiet of the night, it finds its rest. """ ``` Example images are as follows: cat: <img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png" width="250" style="display: inline-block;"> animal: <img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png" width="250" style="display: inline-block;"> math: <img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/math.png" width="250" style="display: inline-block;"> poem: <img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/poem.png" width="250" style="display: inline-block;"> **Single Sample Inference** ```python import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' from swift.llm import ( get_model_tokenizer, get_template, inference, ModelType, get_default_template_type, inference_stream ) from swift.utils import seed_everything import torch model_type = 'llava1_6-mistral-7b-instruct' template_type = get_default_template_type(model_type) print(f'template_type: {template_type}') model, tokenizer = get_model_tokenizer(model_type, torch.float16, model_kwargs={'device_map': 'auto'}) model.generation_config.max_new_tokens = 256 template = get_template(template_type, tokenizer) seed_everything(42) images = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png'] query = 'How far is it from each city?' response, _ = inference(model, template, query, images=images) print(f'query: {query}') print(f'response: {response}') # Streaming query = 'Which city is the farthest?' gen = inference_stream(model, template, query, images=images) print_idx = 0 print(f'query: {query}\nresponse: ', end='') for response, _ in gen: delta = response[print_idx:] print(delta, end='', flush=True) print_idx = len(response) print() """ query: How far is it from each city? response: The image shows a road sign indicating the distances to three cities: Mata, Yangjiang, and Guangzhou. The distances are given in kilometers. - Mata is 14 kilometers away. - Yangjiang is 62 kilometers away. - Guangzhou is 293 kilometers away. Please note that these distances are as the crow flies and do not take into account the actual driving distance due to road conditions, traffic, or other factors. query: Which city is the farthest? response: The farthest city listed on the sign is Mata, which is 14 kilometers away. """ ``` Example image is as follows: road: <img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png" width="250" style="display: inline-block;"> ## Fine-tuning Multimodal large model fine-tuning usually uses **custom datasets** for fine-tuning. Here is a demo that can be run directly: LoRA fine-tuning: (By default, only the qkv of the LLM part is fine-tuned using LoRA. If you want to fine-tune all linear layers including the vision model part, you can specify `--lora_target_modules ALL`.) ```shell # Experimental environment: A10, 3090, V100... # 21GB GPU memory CUDA_VISIBLE_DEVICES=0 swift sft \ --model_type llava1_6-mistral-7b-instruct \ --dataset coco-en-2-mini \ # 2*45GB GPU memory CUDA_VISIBLE_DEVICES=0,1 swift sft \ --model_type llava1_6-yi-34b-instruct \ --dataset coco-en-2-mini \ ``` Full parameter fine-tuning: ```shell # Experimental environment: 4 * A100 # 4 * 70 GPU memory NPROC_PER_NODE=4 CUDA_VISIBLE_DEVICES=0,1,2,3 swift sft \ --model_type llava1_6-mistral-7b-instruct \ --dataset coco-en-2-mini \ --sft_type full \ --deepspeed default-zero2 # 8 * 50 GPU memory CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 swift sft \ --model_type llava1_6-yi-34b-instruct \ --dataset coco-en-2-mini \ --sft_type full \ ``` [Custom datasets](../LLM/Customization.md#-Recommended-Command-line-arguments) support json, jsonl formats. Here is an example of a custom dataset: (Only single-turn dialogue is supported. Each turn of dialogue must contain one image. Local paths or URLs can be passed in.) ```jsonl {"query": "55555", "response": "66666", "images": ["image_path"]} {"query": "eeeee", "response": "fffff", "images": ["image_path"]} {"query": "EEEEE", "response": "FFFFF", "images": ["image_path"]} ``` ## Inference after Fine-tuning Direct inference: ```shell model_type="llava1_6-mistral-7b-instruct" CUDA_VISIBLE_DEVICES=0 swift infer \ --ckpt_dir output/${model_type}/vx-xxx/checkpoint-xxx \ --load_dataset_config true ``` **merge-lora** and inference: ```shell model_type="llava1_6-mistral-7b-instruct" CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir "output/${model_type}/vx-xxx/checkpoint-xxx" \ --merge_lora true CUDA_VISIBLE_DEVICES=0 swift infer \ --ckpt_dir "output/${model_type}/vx-xxx/checkpoint-xxx-merged" \ --load_dataset_config true ```

swift/docs/source_en/Multi-Modal/llava-best-practice.md/0

# Copyright (c) Alibaba, Inc. and its affiliates. from swift.aigc import animatediff_infer_main if __name__ == '__main__': animatediff_infer_main()

swift/examples/pytorch/animatediff/animatediff_infer.py/0

# Experimental environment: 2 * 3090 # 2 * 17GB GPU memory nproc_per_node=2 PYTHONPATH=../../.. \ CUDA_VISIBLE_DEVICES=0,1 \ torchrun \ --nproc_per_node=$nproc_per_node \ --master_port 29500 \ llm_sft.py \ --model_id_or_path ZhipuAI/chatglm3-6b-base \ --model_revision master \ --sft_type lora \ --tuner_backend peft \ --template_type chatglm-generation \ --dtype AUTO \ --output_dir output \ --ddp_backend nccl \ --dataset dureader-robust-zh \ --num_train_epochs 1 \ --max_length 2048 \ --check_dataset_strategy warning \ --lora_rank 8 \ --lora_alpha 32 \ --lora_dropout_p 0.05 \ --lora_target_modules DEFAULT \ --gradient_checkpointing true \ --batch_size 1 \ --weight_decay 0.1 \ --learning_rate 1e-4 \ --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \ --max_grad_norm 0.5 \ --warmup_ratio 0.03 \ --eval_steps 100 \ --save_steps 100 \ --save_total_limit 2 \ --logging_steps 10 \ --deepspeed default-zero2 \

swift/examples/pytorch/llm/scripts/chatglm3_6b_base/lora_ddp_ds/sft.sh/0