mirror of
https://github.com/deepinsight/insightface.git
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838 lines
32 KiB
Python
838 lines
32 KiB
Python
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 data import FaceImageIter
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from data import FaceImageIterList
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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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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 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 AccMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(AccMetric, self).__init__(
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'acc', axis=self.axis,
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output_names=None, label_names=None)
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self.losses = []
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self.count = 0
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def update(self, labels, preds):
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self.count+=1
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if args.loss_type>=2 and args.loss_type<=4 and args.margin_verbose>0:
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if self.count%args.ctx_num==0:
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mbatch = self.count//args.ctx_num
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if mbatch==1 or mbatch%args.margin_verbose==0:
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a = 0.0
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b = 0.0
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if len(preds)>=4:
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a = preds[-2].asnumpy()[0]
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b = preds[-1].asnumpy()[0]
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elif len(preds)==3:
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a = preds[-1].asnumpy()[0]
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b = a
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print('[%d][MARGIN]%f,%f'%(mbatch,a,b))
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#loss = preds[2].asnumpy()[0]
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#if len(self.losses)==20:
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# print('ce loss', sum(self.losses)/len(self.losses))
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# self.losses = []
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#self.losses.append(loss)
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preds = [preds[1]] #use softmax output
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for label, pred_label in zip(labels, preds):
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#print(pred_label)
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#print(label.shape, pred_label.shape)
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if pred_label.shape != label.shape:
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pred_label = mx.ndarray.argmax(pred_label, axis=self.axis)
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pred_label = pred_label.asnumpy().astype('int32').flatten()
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label = label.asnumpy().astype('int32').flatten()
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#print(label)
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#print('label',label)
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#print('pred_label', pred_label)
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assert label.shape==pred_label.shape
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self.sum_metric += (pred_label.flat == label.flat).sum()
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self.num_inst += len(pred_label.flat)
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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='',
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help='')
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parser.add_argument('--prefix', default='../model/model',
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help='directory to save model.')
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parser.add_argument('--pretrained', default='',
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help='')
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parser.add_argument('--retrain', action='store_true', default=False,
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help='true means continue training.')
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parser.add_argument('--ckpt', type=int, default=1, help='')
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parser.add_argument('--network', default='s20', help='')
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parser.add_argument('--version-se', type=int, default=0, help='')
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parser.add_argument('--version-input', type=int, default=1, help='')
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parser.add_argument('--version-output', type=str, default='E', help='')
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parser.add_argument('--version-unit', type=int, default=3, help='')
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parser.add_argument('--end-epoch', type=int, default=100000,
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help='training epoch size.')
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parser.add_argument('--lr', type=float, default=0.1,
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help='')
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parser.add_argument('--wd', type=float, default=0.0005,
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help='')
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parser.add_argument('--mom', type=float, default=0.9,
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help='')
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parser.add_argument('--emb-size', type=int, default=512,
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help='')
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parser.add_argument('--per-batch-size', type=int, default=128,
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help='')
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parser.add_argument('--margin-m', type=float, default=0.35,
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help='')
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parser.add_argument('--margin-s', type=float, default=64.0,
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help='')
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parser.add_argument('--easy-margin', type=int, default=0,
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help='')
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parser.add_argument('--margin-verbose', type=int, default=0,
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help='')
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parser.add_argument('--margin', type=int, default=4,
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help='')
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parser.add_argument('--beta', type=float, default=1000.,
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help='')
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parser.add_argument('--beta-min', type=float, default=5.,
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help='')
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parser.add_argument('--beta-freeze', type=int, default=0,
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help='')
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parser.add_argument('--gamma', type=float, default=0.12,
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help='')
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parser.add_argument('--power', type=float, default=1.0,
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help='')
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parser.add_argument('--scale', type=float, default=0.9993,
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help='')
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parser.add_argument('--center-alpha', type=float, default=0.5, help='')
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parser.add_argument('--center-scale', type=float, default=0.003, help='')
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parser.add_argument('--images-per-identity', type=int, default=0, 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,
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help='')
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parser.add_argument('--incay', type=float, default=0.0,
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help='feature incay')
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parser.add_argument('--use-deformable', type=int, default=0,
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help='')
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parser.add_argument('--rand-mirror', type=int, default=1,
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help='')
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parser.add_argument('--patch', type=str, default='0_0_96_112_0',
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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_ff,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):
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data_shape = (args.image_channel,args.image_h,args.image_w)
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image_shape = ",".join([str(x) for x in data_shape])
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margin_symbols = []
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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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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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gt_label = mx.symbol.Variable('softmax_label')
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assert args.loss_type>=0
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extra_loss = None
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if args.loss_type==0: #softmax
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_weight = mx.symbol.Variable('fc7_weight')
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_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
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fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name='fc7')
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elif args.loss_type==1: #sphere
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_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
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_weight = mx.symbol.L2Normalization(_weight, mode='instance')
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fc7 = mx.sym.LSoftmax(data=embedding, label=gt_label, num_hidden=args.num_classes,
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weight = _weight,
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beta=args.beta, margin=args.margin, scale=args.scale,
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beta_min=args.beta_min, verbose=1000, name='fc7')
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elif args.loss_type==8: #centerloss, TODO
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_weight = mx.symbol.Variable('fc7_weight')
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_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
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fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name='fc7')
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print('center-loss', args.center_alpha, args.center_scale)
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extra_loss = mx.symbol.Custom(data=embedding, label=gt_label, name='center_loss', op_type='centerloss',\
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num_class=args.num_classes, alpha=args.center_alpha, scale=args.center_scale, batchsize=args.per_batch_size)
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elif args.loss_type==2:
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s = args.margin_s
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m = args.margin_m
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_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
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_weight = mx.symbol.L2Normalization(_weight, mode='instance')
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if s>0.0:
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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
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fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
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if m>0.0:
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if args.margin_verbose>0:
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zy = mx.sym.pick(fc7, gt_label, axis=1)
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cos_t = zy/s
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margin_symbols.append(mx.symbol.mean(cos_t))
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s_m = s*m
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gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
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fc7 = fc7-gt_one_hot
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if args.margin_verbose>0:
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new_zy = mx.sym.pick(fc7, gt_label, axis=1)
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new_cos_t = new_zy/s
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margin_symbols.append(mx.symbol.mean(new_cos_t))
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else:
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fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
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if m>0.0:
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body = embedding*embedding
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body = mx.sym.sum_axis(body, axis=1, keepdims=True)
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body = mx.sym.sqrt(body)
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body = body*m
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gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
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body = mx.sym.broadcast_mul(gt_one_hot, body)
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fc7 = fc7-body
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elif args.loss_type==3:
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s = args.margin_s
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m = args.margin_m
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assert args.margin==2 or args.margin==4
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_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
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_weight = mx.symbol.L2Normalization(_weight, mode='instance')
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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
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fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
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zy = mx.sym.pick(fc7, gt_label, axis=1)
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cos_t = zy/s
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if args.margin_verbose>0:
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margin_symbols.append(mx.symbol.mean(cos_t))
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#threshold = math.cos(args.margin_m)
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#cond_v = cos_t - threshold
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#cond = mx.symbol.Activation(data=cond_v, act_type='relu')
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#body = cos_t
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#for i in xrange(args.margin//2):
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# body = body*body
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# body = body*2-1
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#new_zy = body*s
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#zy_keep = zy
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#new_zy = mx.sym.where(cond, new_zy, zy_keep)
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#if args.margin_verbose>0:
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# new_cos_t = new_zy/s
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# margin_symbols.append(mx.symbol.mean(new_cos_t))
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#diff = new_zy - zy
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#diff = mx.sym.expand_dims(diff, 1)
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#gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
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#body = mx.sym.broadcast_mul(gt_one_hot, diff)
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#fc7 = fc7+body
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elif args.loss_type==4:
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s = args.margin_s
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m = args.margin_m
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assert s>0.0
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assert m>0.0
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assert m<(math.pi/2)
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cos_m = math.cos(m)
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sin_m = math.sin(m)
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mm = math.sin(math.pi-m)*m
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_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
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_weight = mx.symbol.L2Normalization(_weight, mode='instance')
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#threshold = 0.0
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threshold = math.cos(math.pi-m)
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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
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fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
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zy = mx.sym.pick(fc7, gt_label, axis=1)
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cos_t = zy/s
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if args.margin_verbose>0:
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margin_symbols.append(mx.symbol.mean(cos_t))
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if args.easy_margin:
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cond = mx.symbol.Activation(data=cos_t, act_type='relu')
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#cond_v = cos_t - 0.4
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#cond = mx.symbol.Activation(data=cond_v, act_type='relu')
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else:
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cond_v = cos_t - threshold
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cond = mx.symbol.Activation(data=cond_v, act_type='relu')
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#theta = mx.sym.arccos(costheta)
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#sintheta = mx.sym.sin(theta)
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body = cos_t*cos_t
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body = 1.0-body
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sin_t = mx.sym.sqrt(body)
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new_zy = cos_t*cos_m
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b = sin_t*sin_m
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new_zy = new_zy - b
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new_zy = new_zy*s
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if args.easy_margin:
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zy_keep = zy
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else:
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zy_keep = zy - s*mm
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new_zy = mx.sym.where(cond, new_zy, zy_keep)
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if args.margin_verbose>0:
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new_cos_t = new_zy/s
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margin_symbols.append(mx.symbol.mean(new_cos_t))
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diff = new_zy - zy
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diff = mx.sym.expand_dims(diff, 1)
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gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
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body = mx.sym.broadcast_mul(gt_one_hot, diff)
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fc7 = fc7+body
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elif args.loss_type==10: #marginal loss
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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
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params = [1.2, 0.3, 1.0]
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n1 = mx.sym.expand_dims(nembedding, axis=1) #N,1,C
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n2 = mx.sym.expand_dims(nembedding, axis=0) #1,N,C
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body = mx.sym.broadcast_sub(n1, n2) #N,N,C
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body = body * body
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body = mx.sym.sum(body, axis=2) # N,N
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#body = mx.sym.sqrt(body)
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body = body - params[0]
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mask = mx.sym.Variable('extra')
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body = body*mask
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body = body+params[1]
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#body = mx.sym.maximum(body, 0.0)
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body = mx.symbol.Activation(data=body, act_type='relu')
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body = mx.sym.sum(body)
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body = body/(args.per_batch_size*args.per_batch_size-args.per_batch_size)
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extra_loss = mx.symbol.MakeLoss(body, grad_scale=params[2])
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elif args.loss_type==11: #npair loss
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params = [0.9, 0.2]
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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
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nembedding = mx.sym.transpose(nembedding)
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nembedding = mx.symbol.reshape(nembedding, (args.emb_size, args.per_identities, args.images_per_identity))
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nembedding = mx.sym.transpose(nembedding, axes=(2,1,0)) #2*id*512
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#nembedding = mx.symbol.reshape(nembedding, (args.emb_size, args.images_per_identity, args.per_identities))
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#nembedding = mx.sym.transpose(nembedding, axes=(1,2,0)) #2*id*512
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n1 = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=1)
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n2 = mx.symbol.slice_axis(nembedding, axis=0, begin=1, end=2)
|
|
#n1 = []
|
|
#n2 = []
|
|
#for i in xrange(args.per_identities):
|
|
# _n1 = mx.symbol.slice_axis(nembedding, axis=0, begin=2*i, end=2*i+1)
|
|
# _n2 = mx.symbol.slice_axis(nembedding, axis=0, begin=2*i+1, end=2*i+2)
|
|
# n1.append(_n1)
|
|
# n2.append(_n2)
|
|
#n1 = mx.sym.concat(*n1, dim=0)
|
|
#n2 = mx.sym.concat(*n2, dim=0)
|
|
#rembeddings = mx.symbol.reshape(nembedding, (args.images_per_identity, args.per_identities, 512))
|
|
#n1 = mx.symbol.slice_axis(rembeddings, axis=0, begin=0, end=1)
|
|
#n2 = mx.symbol.slice_axis(rembeddings, axis=0, begin=1, end=2)
|
|
n1 = mx.symbol.reshape(n1, (args.per_identities, args.emb_size))
|
|
n2 = mx.symbol.reshape(n2, (args.per_identities, args.emb_size))
|
|
cosine_matrix = mx.symbol.dot(lhs=n1, rhs=n2, transpose_b = True) #id*id, id=N of N-pair
|
|
data_extra = mx.sym.Variable('extra')
|
|
data_extra = mx.sym.slice_axis(data_extra, axis=0, begin=0, end=args.per_identities)
|
|
mask = cosine_matrix * data_extra
|
|
#body = mx.sym.mean(mask)
|
|
fii = mx.sym.sum_axis(mask, axis=1)
|
|
fij_fii = mx.sym.broadcast_sub(cosine_matrix, fii)
|
|
fij_fii = mx.sym.exp(fij_fii)
|
|
row = mx.sym.sum_axis(fij_fii, axis=1)
|
|
row = mx.sym.log(row)
|
|
body = mx.sym.mean(row)
|
|
extra_loss = mx.sym.MakeLoss(body)
|
|
elif args.loss_type==12: #triplet loss
|
|
nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
|
|
anchor = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=args.per_batch_size//3)
|
|
positive = mx.symbol.slice_axis(nembedding, axis=0, begin=args.per_batch_size//3, end=2*args.per_batch_size//3)
|
|
negative = mx.symbol.slice_axis(nembedding, axis=0, begin=2*args.per_batch_size//3, end=args.per_batch_size)
|
|
ap = anchor - positive
|
|
an = anchor - negative
|
|
ap = ap*ap
|
|
an = an*an
|
|
ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)
|
|
an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)
|
|
triplet_loss = mx.symbol.Activation(data = (ap-an+args.triplet_alpha), act_type='relu')
|
|
triplet_loss = mx.symbol.mean(triplet_loss)
|
|
#triplet_loss = mx.symbol.sum(triplet_loss)/(args.per_batch_size//3)
|
|
extra_loss = mx.symbol.MakeLoss(triplet_loss)
|
|
elif args.loss_type==9: #coco loss
|
|
centroids = []
|
|
for i in xrange(args.per_identities):
|
|
xs = mx.symbol.slice_axis(embedding, axis=0, begin=i*args.images_per_identity, end=(i+1)*args.images_per_identity)
|
|
mean = mx.symbol.mean(xs, axis=0, keepdims=True)
|
|
mean = mx.symbol.L2Normalization(mean, mode='instance')
|
|
centroids.append(mean)
|
|
centroids = mx.symbol.concat(*centroids, dim=0)
|
|
nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*args.coco_scale
|
|
fc7 = mx.symbol.dot(nembedding, centroids, transpose_b = True) #(batchsize, per_identities)
|
|
#extra_loss = mx.symbol.softmax_cross_entropy(fc7, gt_label, name='softmax_ce')/args.per_batch_size
|
|
#extra_loss = mx.symbol.BlockGrad(extra_loss)
|
|
else:
|
|
#embedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*float(args.loss_type)
|
|
embedding = embedding * 5
|
|
_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
|
|
_weight = mx.symbol.L2Normalization(_weight, mode='instance') * 2
|
|
fc7 = mx.sym.LSoftmax(data=embedding, label=gt_label, num_hidden=args.num_classes,
|
|
weight = _weight,
|
|
beta=args.beta, margin=args.margin, scale=args.scale,
|
|
beta_min=args.beta_min, verbose=100, name='fc7')
|
|
|
|
#fc7 = mx.sym.Custom(data=embedding, label=gt_label, weight=_weight, num_hidden=args.num_classes,
|
|
# beta=args.beta, margin=args.margin, scale=args.scale,
|
|
# op_type='ASoftmax', name='fc7')
|
|
if args.loss_type<=1 and args.incay>0.0:
|
|
params = [1.e-10]
|
|
sel = mx.symbol.argmax(data = fc7, axis=1)
|
|
sel = (sel==gt_label)
|
|
norm = embedding*embedding
|
|
norm = mx.symbol.sum(norm, axis=1)
|
|
norm = norm+params[0]
|
|
feature_incay = sel/norm
|
|
feature_incay = mx.symbol.mean(feature_incay) * args.incay
|
|
extra_loss = mx.symbol.MakeLoss(feature_incay)
|
|
#out = softmax
|
|
#l2_embedding = mx.symbol.L2Normalization(embedding)
|
|
|
|
#ce = mx.symbol.softmax_cross_entropy(fc7, gt_label, name='softmax_ce')/args.per_batch_size
|
|
#out = mx.symbol.Group([mx.symbol.BlockGrad(embedding), softmax, mx.symbol.BlockGrad(ce)])
|
|
out_list = [mx.symbol.BlockGrad(embedding)]
|
|
softmax = None
|
|
if args.loss_type<10:
|
|
softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid')
|
|
out_list.append(softmax)
|
|
if softmax is None:
|
|
out_list.append(mx.sym.BlockGrad(gt_label))
|
|
if extra_loss is not None:
|
|
out_list.append(extra_loss)
|
|
for _sym in margin_symbols:
|
|
_sym = mx.sym.BlockGrad(_sym)
|
|
out_list.append(_sym)
|
|
out = mx.symbol.Group(out_list)
|
|
return (out, arg_params, aux_params)
|
|
|
|
def train_net(args):
|
|
ctx = []
|
|
cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()
|
|
if len(cvd)>0:
|
|
for i in xrange(len(cvd.split(','))):
|
|
ctx.append(mx.gpu(i))
|
|
if len(ctx)==0:
|
|
ctx = [mx.cpu()]
|
|
print('use cpu')
|
|
else:
|
|
print('gpu num:', len(ctx))
|
|
prefix = args.prefix
|
|
prefix_dir = os.path.dirname(prefix)
|
|
if not os.path.exists(prefix_dir):
|
|
os.makedirs(prefix_dir)
|
|
end_epoch = args.end_epoch
|
|
args.ctx_num = len(ctx)
|
|
args.num_layers = int(args.network[1:])
|
|
print('num_layers', args.num_layers)
|
|
if args.per_batch_size==0:
|
|
args.per_batch_size = 128
|
|
if args.loss_type==10:
|
|
args.per_batch_size = 256
|
|
args.batch_size = args.per_batch_size*args.ctx_num
|
|
args.rescale_threshold = 0
|
|
args.image_channel = 3
|
|
ppatch = [int(x) for x in args.patch.split('_')]
|
|
assert len(ppatch)==5
|
|
|
|
|
|
os.environ['BETA'] = str(args.beta)
|
|
data_dir_list = args.data_dir.split(',')
|
|
if args.loss_type!=12:
|
|
assert len(data_dir_list)==1
|
|
data_dir = data_dir_list[0]
|
|
args.use_val = False
|
|
path_imgrec = None
|
|
path_imglist = None
|
|
val_rec = None
|
|
prop = face_image.load_property(data_dir)
|
|
args.num_classes = prop.num_classes
|
|
image_size = prop.image_size
|
|
args.image_h = image_size[0]
|
|
args.image_w = image_size[1]
|
|
print('image_size', image_size)
|
|
|
|
assert(args.num_classes>0)
|
|
print('num_classes', args.num_classes)
|
|
args.coco_scale = 0.5*math.log(float(args.num_classes-1))+3
|
|
|
|
#path_imglist = "/raid5data/dplearn/MS-Celeb-Aligned/lst2"
|
|
path_imgrec = os.path.join(data_dir, "train.rec")
|
|
val_rec = os.path.join(data_dir, "val.rec")
|
|
if os.path.exists(val_rec) and args.loss_type<10:
|
|
args.use_val = True
|
|
else:
|
|
val_rec = None
|
|
#args.use_val = False
|
|
|
|
if args.loss_type==1 and args.num_classes>20000:
|
|
args.beta_freeze = 5000
|
|
args.gamma = 0.06
|
|
|
|
if args.loss_type<9:
|
|
assert args.images_per_identity==0
|
|
else:
|
|
if args.images_per_identity==0:
|
|
if args.loss_type==11:
|
|
args.images_per_identity = 2
|
|
elif args.loss_type==10 or args.loss_type==9:
|
|
args.images_per_identity = 16
|
|
elif args.loss_type==12:
|
|
args.images_per_identity = 5
|
|
assert args.per_batch_size%3==0
|
|
assert args.images_per_identity>=2
|
|
args.per_identities = int(args.per_batch_size/args.images_per_identity)
|
|
|
|
print('Called with argument:', args)
|
|
|
|
data_shape = (args.image_channel,image_size[0],image_size[1])
|
|
mean = None
|
|
|
|
|
|
|
|
|
|
begin_epoch = 0
|
|
base_lr = args.lr
|
|
base_wd = args.wd
|
|
base_mom = args.mom
|
|
if len(args.pretrained)==0:
|
|
arg_params = None
|
|
aux_params = None
|
|
sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
|
|
else:
|
|
vec = args.pretrained.split(',')
|
|
print('loading', vec)
|
|
_, arg_params, aux_params = mx.model.load_checkpoint(vec[0], int(vec[1]))
|
|
sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
|
|
|
|
data_extra = None
|
|
hard_mining = False
|
|
triplet_params = None
|
|
coco_mode = False
|
|
if args.loss_type==10:
|
|
hard_mining = True
|
|
_shape = (args.batch_size, args.per_batch_size)
|
|
data_extra = np.full(_shape, -1.0, dtype=np.float32)
|
|
c = 0
|
|
while c<args.batch_size:
|
|
a = 0
|
|
while a<args.per_batch_size:
|
|
b = a+args.images_per_identity
|
|
data_extra[(c+a):(c+b),a:b] = 1.0
|
|
#print(c+a, c+b, a, b)
|
|
a = b
|
|
c += args.per_batch_size
|
|
elif args.loss_type==11:
|
|
data_extra = np.zeros( (args.batch_size, args.per_identities), dtype=np.float32)
|
|
c = 0
|
|
while c<args.batch_size:
|
|
for i in xrange(args.per_identities):
|
|
data_extra[c+i][i] = 1.0
|
|
c+=args.per_batch_size
|
|
elif args.loss_type==12:
|
|
triplet_params = [args.triplet_bag_size, args.triplet_alpha, args.triplet_max_ap]
|
|
elif args.loss_type==9:
|
|
coco_mode = True
|
|
|
|
label_name = 'softmax_label'
|
|
if data_extra is None:
|
|
model = mx.mod.Module(
|
|
context = ctx,
|
|
symbol = sym,
|
|
)
|
|
else:
|
|
data_names = ('data', 'extra')
|
|
#label_name = ''
|
|
model = mx.mod.Module(
|
|
context = ctx,
|
|
symbol = sym,
|
|
data_names = data_names,
|
|
label_names = (label_name,),
|
|
)
|
|
|
|
if args.use_val:
|
|
val_dataiter = FaceImageIter(
|
|
batch_size = args.batch_size,
|
|
data_shape = data_shape,
|
|
path_imgrec = val_rec,
|
|
#path_imglist = val_path,
|
|
shuffle = False,
|
|
rand_mirror = False,
|
|
mean = mean,
|
|
ctx_num = args.ctx_num,
|
|
data_extra = data_extra,
|
|
)
|
|
else:
|
|
val_dataiter = None
|
|
|
|
if len(data_dir_list)==1 and args.loss_type!=12:
|
|
train_dataiter = FaceImageIter(
|
|
batch_size = args.batch_size,
|
|
data_shape = data_shape,
|
|
path_imgrec = path_imgrec,
|
|
shuffle = True,
|
|
rand_mirror = args.rand_mirror,
|
|
mean = mean,
|
|
ctx_num = args.ctx_num,
|
|
images_per_identity = args.images_per_identity,
|
|
data_extra = data_extra,
|
|
hard_mining = hard_mining,
|
|
triplet_params = triplet_params,
|
|
coco_mode = coco_mode,
|
|
mx_model = model,
|
|
label_name = label_name,
|
|
)
|
|
else:
|
|
iter_list = []
|
|
for _data_dir in data_dir_list:
|
|
_path_imgrec = os.path.join(_data_dir, "train.rec")
|
|
_dataiter = FaceImageIter(
|
|
batch_size = args.batch_size,
|
|
data_shape = data_shape,
|
|
path_imgrec = _path_imgrec,
|
|
shuffle = True,
|
|
rand_mirror = args.rand_mirror,
|
|
mean = mean,
|
|
ctx_num = args.ctx_num,
|
|
images_per_identity = args.images_per_identity,
|
|
data_extra = data_extra,
|
|
hard_mining = hard_mining,
|
|
triplet_params = triplet_params,
|
|
coco_mode = coco_mode,
|
|
mx_model = model,
|
|
label_name = label_name,
|
|
)
|
|
iter_list.append(_dataiter)
|
|
iter_list.append(_dataiter)
|
|
train_dataiter = FaceImageIterList(iter_list)
|
|
|
|
if args.loss_type<10:
|
|
_metric = AccMetric()
|
|
else:
|
|
_metric = LossValueMetric()
|
|
eval_metrics = [mx.metric.create(_metric)]
|
|
|
|
if args.network[0]=='r':
|
|
initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style
|
|
elif args.network[0]=='i' or args.network[0]=='x':
|
|
initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2) #inception
|
|
else:
|
|
initializer = mx.init.Xavier(rnd_type='uniform', factor_type="in", magnitude=2)
|
|
_rescale = 1.0/args.ctx_num
|
|
opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
|
|
som = 20
|
|
if args.loss_type==12:
|
|
som = 2
|
|
_cb = mx.callback.Speedometer(args.batch_size, som)
|
|
|
|
ver_list = []
|
|
ver_name_list = []
|
|
for name in args.target.split(','):
|
|
path = os.path.join(data_dir,name+".bin")
|
|
if os.path.exists(path):
|
|
data_set = verification.load_bin(path, image_size)
|
|
ver_list.append(data_set)
|
|
ver_name_list.append(name)
|
|
print('ver', name)
|
|
|
|
|
|
|
|
def ver_test(nbatch):
|
|
results = []
|
|
for i in xrange(len(ver_list)):
|
|
acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, data_extra)
|
|
print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))
|
|
#print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1))
|
|
print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))
|
|
results.append(acc2)
|
|
return results
|
|
|
|
|
|
def val_test():
|
|
acc = AccMetric()
|
|
val_metric = mx.metric.create(acc)
|
|
val_metric.reset()
|
|
val_dataiter.reset()
|
|
for i, eval_batch in enumerate(val_dataiter):
|
|
model.forward(eval_batch, is_train=False)
|
|
model.update_metric(val_metric, eval_batch.label)
|
|
acc_value = val_metric.get_name_value()[0][1]
|
|
print('VACC: %f'%(acc_value))
|
|
|
|
|
|
highest_acc = [0.0, 0.0] #lfw and target
|
|
#for i in xrange(len(ver_list)):
|
|
# highest_acc.append(0.0)
|
|
global_step = [0]
|
|
save_step = [0]
|
|
if len(args.lr_steps)==0:
|
|
lr_steps = [40000, 60000, 80000]
|
|
if args.loss_type>=1 and args.loss_type<=5:
|
|
lr_steps = [100000, 140000, 160000]
|
|
p = 512.0/args.batch_size
|
|
for l in xrange(len(lr_steps)):
|
|
lr_steps[l] = int(lr_steps[l]*p)
|
|
else:
|
|
lr_steps = [int(x) for x in args.lr_steps.split(',')]
|
|
print('lr_steps', lr_steps)
|
|
def _batch_callback(param):
|
|
#global global_step
|
|
global_step[0]+=1
|
|
mbatch = global_step[0]
|
|
for _lr in lr_steps:
|
|
if mbatch==args.beta_freeze+_lr:
|
|
opt.lr *= 0.1
|
|
print('lr change to', opt.lr)
|
|
break
|
|
|
|
_cb(param)
|
|
if mbatch%1000==0:
|
|
print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch)
|
|
|
|
if mbatch>=0 and mbatch%args.verbose==0:
|
|
acc_list = ver_test(mbatch)
|
|
save_step[0]+=1
|
|
msave = save_step[0]
|
|
do_save = False
|
|
if len(acc_list)>0:
|
|
lfw_score = acc_list[0]
|
|
if lfw_score>highest_acc[0]:
|
|
highest_acc[0] = lfw_score
|
|
if lfw_score>=0.998:
|
|
do_save = True
|
|
if acc_list[-1]>=highest_acc[-1]:
|
|
highest_acc[-1] = acc_list[-1]
|
|
if lfw_score>=0.99:
|
|
do_save = True
|
|
if args.ckpt==0:
|
|
do_save = False
|
|
elif args.ckpt>1:
|
|
do_save = True
|
|
#for i in xrange(len(acc_list)):
|
|
# acc = acc_list[i]
|
|
# if acc>=highest_acc[i]:
|
|
# highest_acc[i] = acc
|
|
# if lfw_score>=0.99:
|
|
# do_save = True
|
|
#if args.loss_type==1 and mbatch>lr_steps[-1] and mbatch%10000==0:
|
|
# do_save = True
|
|
if do_save:
|
|
print('saving', msave)
|
|
if val_dataiter is not None:
|
|
val_test()
|
|
arg, aux = model.get_params()
|
|
mx.model.save_checkpoint(prefix, msave, model.symbol, arg, aux)
|
|
#if acc>=highest_acc[0]:
|
|
# lfw_npy = "%s-lfw-%04d" % (prefix, msave)
|
|
# X = np.concatenate(embeddings_list, axis=0)
|
|
# print('saving lfw npy', X.shape)
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# np.save(lfw_npy, X)
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print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))
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if mbatch<=args.beta_freeze:
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_beta = args.beta
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else:
|
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move = max(0, mbatch-args.beta_freeze)
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_beta = max(args.beta_min, args.beta*math.pow(1+args.gamma*move, -1.0*args.power))
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#print('beta', _beta)
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|
os.environ['BETA'] = str(_beta)
|
|
if args.max_steps>0 and mbatch>args.max_steps:
|
|
sys.exit(0)
|
|
|
|
#epoch_cb = mx.callback.do_checkpoint(prefix, 1)
|
|
epoch_cb = None
|
|
|
|
|
|
|
|
#def _epoch_callback(epoch, sym, arg_params, aux_params):
|
|
# print('epoch-end', epoch)
|
|
|
|
model.fit(train_dataiter,
|
|
begin_epoch = begin_epoch,
|
|
num_epoch = end_epoch,
|
|
eval_data = val_dataiter,
|
|
eval_metric = eval_metrics,
|
|
kvstore = 'device',
|
|
optimizer = opt,
|
|
#optimizer_params = optimizer_params,
|
|
initializer = initializer,
|
|
arg_params = arg_params,
|
|
aux_params = aux_params,
|
|
allow_missing = True,
|
|
batch_end_callback = _batch_callback,
|
|
epoch_end_callback = epoch_cb )
|
|
|
|
def main():
|
|
#time.sleep(3600*6.5)
|
|
global args
|
|
args = parse_args()
|
|
train_net(args)
|
|
|
|
if __name__ == '__main__':
|
|
main()
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|
|