mirror of
https://github.com/deepinsight/insightface.git
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403 lines
15 KiB
Python
403 lines
15 KiB
Python
# THIS FILE IS FOR EXPERIMENTS, USE train_softmax.py FOR NORMAL TRAINING.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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import math
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import random
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import logging
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import pickle
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import numpy as np
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from triplet_image_iter import FaceImageIter
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import mxnet as mx
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from mxnet import ndarray as nd
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import argparse
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import mxnet.optimizer as optimizer
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sys.path.append(os.path.join(os.path.dirname(__file__), 'common'))
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import face_image
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from noise_sgd import NoiseSGD
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sys.path.append(os.path.join(os.path.dirname(__file__), 'eval'))
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sys.path.append(os.path.join(os.path.dirname(__file__), 'symbols'))
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import fresnet
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import finception_resnet_v2
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import fmobilenet
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import fmobilenetv2
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import fxception
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import fdensenet
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import fdpn
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import fnasnet
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import spherenet
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#import lfw
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import verification
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import sklearn
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sys.path.append(os.path.join(os.path.dirname(__file__), 'losses'))
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import center_loss
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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args = None
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class LossValueMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(LossValueMetric, self).__init__(
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'lossvalue', axis=self.axis,
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output_names=None, label_names=None)
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self.losses = []
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def update(self, labels, preds):
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loss = preds[-1].asnumpy()[0]
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self.sum_metric += loss
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self.num_inst += 1.0
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gt_label = preds[-2].asnumpy()
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#print(gt_label)
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def parse_args():
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parser = argparse.ArgumentParser(description='Train face network')
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# general
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parser.add_argument('--data-dir', default='', help='training set directory')
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parser.add_argument('--prefix', default='../model/model', help='directory to save model.')
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parser.add_argument('--pretrained', default='', help='pretrained model to load')
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parser.add_argument('--ckpt', type=int, default=3, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')
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parser.add_argument('--network', default='r50', help='specify network')
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parser.add_argument('--version-se', type=int, default=0, help='whether to use se in network')
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parser.add_argument('--version-input', type=int, default=1, help='network input config')
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parser.add_argument('--version-output', type=str, default='E', help='network embedding output config')
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parser.add_argument('--version-unit', type=int, default=3, help='resnet unit config')
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parser.add_argument('--version-act', type=str, default='prelu', help='network activation config')
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parser.add_argument('--end-epoch', type=int, default=100000, help='training epoch size.')
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parser.add_argument('--noise-sgd', type=float, default=0.0, help='')
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parser.add_argument('--lr', type=float, default=0.1, help='start learning rate')
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parser.add_argument('--wd', type=float, default=0.0005, help='weight decay')
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parser.add_argument('--mom', type=float, default=0.9, help='momentum')
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parser.add_argument('--emb-size', type=int, default=512, help='embedding length')
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parser.add_argument('--per-batch-size', type=int, default=128, help='batch size in each context')
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parser.add_argument('--images-per-identity', type=int, default=5, help='')
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parser.add_argument('--triplet-bag-size', type=int, default=3600, help='')
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parser.add_argument('--triplet-alpha', type=float, default=0.3, help='')
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parser.add_argument('--triplet-max-ap', type=float, default=0.0, help='')
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parser.add_argument('--verbose', type=int, default=2000, help='')
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parser.add_argument('--loss-type', type=int, default=1, help='')
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parser.add_argument('--use-deformable', type=int, default=0, help='')
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parser.add_argument('--rand-mirror', type=int, default=1, help='')
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parser.add_argument('--cutoff', type=int, default=0, help='')
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parser.add_argument('--lr-steps', type=str, default='', help='')
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parser.add_argument('--max-steps', type=int, default=0, help='')
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parser.add_argument('--target', type=str, default='lfw,cfp_fp,agedb_30', help='')
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args = parser.parse_args()
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return args
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def get_symbol(args, arg_params, aux_params, sym_embedding=None):
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if sym_embedding is None:
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if args.network[0]=='d':
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embedding = fdensenet.get_symbol(args.emb_size, args.num_layers,
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version_se=args.version_se, version_input=args.version_input,
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version_output=args.version_output, version_unit=args.version_unit)
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elif args.network[0]=='m':
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print('init mobilenet', args.num_layers)
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if args.num_layers==1:
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embedding = fmobilenet.get_symbol(args.emb_size,
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version_se=args.version_se, version_input=args.version_input,
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version_output=args.version_output, version_unit=args.version_unit)
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else:
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embedding = fmobilenetv2.get_symbol(args.emb_size)
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elif args.network[0]=='i':
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print('init inception-resnet-v2', args.num_layers)
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embedding = finception_resnet_v2.get_symbol(args.emb_size,
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version_se=args.version_se, version_input=args.version_input,
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version_output=args.version_output, version_unit=args.version_unit)
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elif args.network[0]=='x':
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print('init xception', args.num_layers)
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embedding = fxception.get_symbol(args.emb_size,
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version_se=args.version_se, version_input=args.version_input,
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version_output=args.version_output, version_unit=args.version_unit)
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elif args.network[0]=='p':
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print('init dpn', args.num_layers)
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embedding = fdpn.get_symbol(args.emb_size, args.num_layers,
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version_se=args.version_se, version_input=args.version_input,
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version_output=args.version_output, version_unit=args.version_unit)
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elif args.network[0]=='n':
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print('init nasnet', args.num_layers)
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embedding = fnasnet.get_symbol(args.emb_size)
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elif args.network[0]=='s':
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print('init spherenet', args.num_layers)
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embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
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else:
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print('init resnet', args.num_layers)
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embedding = fresnet.get_symbol(args.emb_size, args.num_layers,
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version_se=args.version_se, version_input=args.version_input,
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version_output=args.version_output, version_unit=args.version_unit,
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version_act=args.version_act)
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else:
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embedding = sym_embedding
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assert args.loss_type==1 or args.loss_type==2
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gt_label = mx.symbol.Variable('softmax_label')
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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
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anchor = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=args.per_batch_size//3)
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positive = mx.symbol.slice_axis(nembedding, axis=0, begin=args.per_batch_size//3, end=2*args.per_batch_size//3)
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negative = mx.symbol.slice_axis(nembedding, axis=0, begin=2*args.per_batch_size//3, end=args.per_batch_size)
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if args.loss_type==1:
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ap = anchor - positive
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an = anchor - negative
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ap = ap*ap
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an = an*an
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ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)
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an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)
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triplet_loss = mx.symbol.Activation(data = (ap-an+args.triplet_alpha), act_type='relu')
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triplet_loss = mx.symbol.mean(triplet_loss)
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elif args.loss_type==2:
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ap = anchor*positive
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an = anchor*negative
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ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)
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an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)
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ap = mx.sym.arccos(ap)
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an = mx.sym.arccos(an)
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triplet_loss = mx.symbol.Activation(data = (ap-an+args.triplet_alpha), act_type='relu')
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triplet_loss = mx.symbol.mean(triplet_loss)
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#triplet_loss = mx.symbol.sum(triplet_loss)/(args.per_batch_size//3)
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triplet_loss = mx.symbol.MakeLoss(triplet_loss)
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out_list = [mx.symbol.BlockGrad(embedding)]
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out_list.append(mx.sym.BlockGrad(gt_label))
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out_list.append(triplet_loss)
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out = mx.symbol.Group(out_list)
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return (out, arg_params, aux_params)
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def train_net(args):
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ctx = []
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cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()
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if len(cvd)>0:
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for i in xrange(len(cvd.split(','))):
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ctx.append(mx.gpu(i))
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if len(ctx)==0:
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ctx = [mx.cpu()]
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print('use cpu')
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else:
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print('gpu num:', len(ctx))
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prefix = args.prefix
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prefix_dir = os.path.dirname(prefix)
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if not os.path.exists(prefix_dir):
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os.makedirs(prefix_dir)
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end_epoch = args.end_epoch
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args.ctx_num = len(ctx)
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args.num_layers = int(args.network[1:])
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print('num_layers', args.num_layers)
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if args.per_batch_size==0:
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args.per_batch_size = 128
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args.batch_size = args.per_batch_size*args.ctx_num
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args.image_channel = 3
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data_dir_list = args.data_dir.split(',')
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assert len(data_dir_list)==1
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data_dir = data_dir_list[0]
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path_imgrec = None
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path_imglist = None
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prop = face_image.load_property(data_dir)
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args.num_classes = prop.num_classes
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image_size = prop.image_size
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args.image_h = image_size[0]
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args.image_w = image_size[1]
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print('image_size', image_size)
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assert(args.num_classes>0)
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print('num_classes', args.num_classes)
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#path_imglist = "/raid5data/dplearn/MS-Celeb-Aligned/lst2"
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path_imgrec = os.path.join(data_dir, "train.rec")
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assert args.images_per_identity>=2
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assert args.triplet_bag_size%args.batch_size==0
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print('Called with argument:', args)
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data_shape = (args.image_channel,image_size[0],image_size[1])
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mean = None
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begin_epoch = 0
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base_lr = args.lr
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base_wd = args.wd
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base_mom = args.mom
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if len(args.pretrained)==0:
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arg_params = None
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aux_params = None
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sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
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else:
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vec = args.pretrained.split(',')
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print('loading', vec)
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sym, arg_params, aux_params = mx.model.load_checkpoint(vec[0], int(vec[1]))
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all_layers = sym.get_internals()
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sym = all_layers['fc1_output']
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sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params, sym_embedding = sym)
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data_extra = None
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hard_mining = False
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triplet_params = [args.triplet_bag_size, args.triplet_alpha, args.triplet_max_ap]
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model = mx.mod.Module(
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context = ctx,
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symbol = sym,
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#data_names = ('data',),
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#label_names = None,
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#label_names = ('softmax_label',),
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)
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label_shape = (args.batch_size,)
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val_dataiter = None
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train_dataiter = FaceImageIter(
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batch_size = args.batch_size,
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data_shape = data_shape,
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path_imgrec = path_imgrec,
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shuffle = True,
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rand_mirror = args.rand_mirror,
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mean = mean,
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cutoff = args.cutoff,
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ctx_num = args.ctx_num,
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images_per_identity = args.images_per_identity,
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triplet_params = triplet_params,
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mx_model = model,
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)
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_metric = LossValueMetric()
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eval_metrics = [mx.metric.create(_metric)]
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if args.network[0]=='r':
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initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style
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elif args.network[0]=='i' or args.network[0]=='x':
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initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2) #inception
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else:
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initializer = mx.init.Xavier(rnd_type='uniform', factor_type="in", magnitude=2)
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_rescale = 1.0/args.ctx_num
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if args.noise_sgd>0.0:
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print('use noise sgd')
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opt = NoiseSGD(scale = args.noise_sgd, learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
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else:
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opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
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som = 2
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_cb = mx.callback.Speedometer(args.batch_size, som)
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ver_list = []
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ver_name_list = []
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for name in args.target.split(','):
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path = os.path.join(data_dir,name+".bin")
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if os.path.exists(path):
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data_set = verification.load_bin(path, image_size)
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ver_list.append(data_set)
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ver_name_list.append(name)
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print('ver', name)
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def ver_test(nbatch):
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results = []
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for i in xrange(len(ver_list)):
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acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, 10, None, label_shape)
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print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))
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#print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1))
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print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))
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results.append(acc2)
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return results
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highest_acc = [0.0, 0.0] #lfw and target
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#for i in xrange(len(ver_list)):
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# highest_acc.append(0.0)
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global_step = [0]
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save_step = [0]
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if len(args.lr_steps)==0:
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lr_steps = [1000000000]
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else:
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lr_steps = [int(x) for x in args.lr_steps.split(',')]
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print('lr_steps', lr_steps)
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def _batch_callback(param):
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#global global_step
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global_step[0]+=1
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mbatch = global_step[0]
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for _lr in lr_steps:
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if mbatch==_lr:
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opt.lr *= 0.1
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print('lr change to', opt.lr)
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break
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_cb(param)
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if mbatch%1000==0:
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print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch)
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if mbatch>=0 and mbatch%args.verbose==0:
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acc_list = ver_test(mbatch)
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save_step[0]+=1
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msave = save_step[0]
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do_save = False
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is_highest = False
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if len(acc_list)>0:
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#lfw_score = acc_list[0]
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#if lfw_score>highest_acc[0]:
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# highest_acc[0] = lfw_score
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# if lfw_score>=0.998:
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# do_save = True
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score = sum(acc_list)
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if acc_list[-1]>=highest_acc[-1]:
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if acc_list[-1]>highest_acc[-1]:
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is_highest = True
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else:
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if score>=highest_acc[0]:
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is_highest = True
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highest_acc[0] = score
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highest_acc[-1] = acc_list[-1]
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#if lfw_score>=0.99:
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# do_save = True
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if is_highest:
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do_save = True
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if args.ckpt==0:
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do_save = False
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elif args.ckpt==2:
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do_save = True
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elif args.ckpt==3:
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msave = 1
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if do_save:
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print('saving', msave)
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arg, aux = model.get_params()
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mx.model.save_checkpoint(prefix, msave, model.symbol, arg, aux)
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print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))
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if args.max_steps>0 and mbatch>args.max_steps:
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sys.exit(0)
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#epoch_cb = mx.callback.do_checkpoint(prefix, 1)
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epoch_cb = None
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model.fit(train_dataiter,
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begin_epoch = begin_epoch,
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num_epoch = end_epoch,
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eval_data = val_dataiter,
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eval_metric = eval_metrics,
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kvstore = 'device',
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optimizer = opt,
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#optimizer_params = optimizer_params,
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initializer = initializer,
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arg_params = arg_params,
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aux_params = aux_params,
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allow_missing = True,
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batch_end_callback = _batch_callback,
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epoch_end_callback = epoch_cb )
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def main():
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#time.sleep(3600*6.5)
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global args
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args = parse_args()
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train_net(args)
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if __name__ == '__main__':
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main()
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