mirror of
https://github.com/deepinsight/insightface.git
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544 lines
20 KiB
Python
544 lines
20 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 copy
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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 age_iter import FaceImageIter
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from age_iter 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 spherenet
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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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AGE = 100
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USE_FR = False
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USE_GENDER = False
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USE_AGE = True
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class AccMetric(mx.metric.EvalMetric):
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def __init__(self, pred_idx = 1, name='acc'):
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self.axis = 1
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self.pred_idx = pred_idx
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super(AccMetric, self).__init__(
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'name', 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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preds = [preds[self.pred_idx]] #use softmax output
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for label, pred_label in zip(labels, preds):
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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()
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if label.ndim==2:
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label = label[:,0]
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label = label.astype('int32').flatten()
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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 MAEMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(MAEMetric, self).__init__(
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'MAE', 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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label = labels[0].asnumpy()
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label = label[:,(AGE*-1):]
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label_age = np.count_nonzero(label, axis=1)
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pred_age = np.zeros( label_age.shape, dtype=np.int)
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for i in xrange(-1*AGE, -1):
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pred = preds[i].asnumpy()
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pred = np.argmax(pred, axis=1)
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pred_age += pred
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mae = np.mean(np.abs(label_age - pred_age))
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self.sum_metric += mae
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self.num_inst += 1.0
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class CUMMetric(mx.metric.EvalMetric):
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def __init__(self, n=5):
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self.axis = 1
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self.n = n
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super(CUMMetric, self).__init__(
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'CUM_%d'%n, 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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label = labels[0].asnumpy()
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label = label[:,(AGE*-1):]
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label_age = np.count_nonzero(label, axis=1)
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pred_age = np.zeros( label_age.shape, dtype=np.int)
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for i in xrange(-1*AGE, -1):
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pred = preds[i].asnumpy()
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pred = np.argmax(pred, axis=1)
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pred_age += pred
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diff = np.abs(label_age - pred_age)
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cum = np.sum( (diff<self.n) )
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self.sum_metric += cum
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self.num_inst += len(label_age)
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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=1, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')
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parser.add_argument('--loss-type', type=int, default=4, help='loss type')
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parser.add_argument('--verbose', type=int, default=2000, help='do verification testing and model saving every verbose batches')
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parser.add_argument('--max-steps', type=int, default=0, help='max training batches')
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parser.add_argument('--end-epoch', type=int, default=100000, help='training epoch size.')
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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('--use-deformable', type=int, default=0, help='use deformable cnn in network')
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parser.add_argument('--lr', type=float, default=0.1, help='start learning rate')
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parser.add_argument('--lr-steps', type=str, default='', help='steps of lr changing')
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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('--margin-m', type=float, default=0.5, help='margin for loss')
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parser.add_argument('--margin-s', type=float, default=64.0, help='scale for feature')
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parser.add_argument('--margin-a', type=float, default=1.0, help='')
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parser.add_argument('--margin-b', type=float, default=0.0, help='')
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parser.add_argument('--easy-margin', type=int, default=0, help='')
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parser.add_argument('--rand-mirror', type=int, default=1, help='if do random mirror in training')
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parser.add_argument('--cutoff', type=int, default=0, help='cut off aug')
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parser.add_argument('--target', type=str, default='lfw,cfp_fp,agedb_30', help='verification targets')
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parser.add_argument('--ignore-label', type=int, default=-1, help='ignore label')
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args = parser.parse_args()
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return args
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def get_softmax(args, embedding, nembedding, gt_label, name):
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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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if args.margin_a==0.0:
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_weight = mx.symbol.Variable(name+"_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
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_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)
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fc = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name=name)
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else:
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_weight = mx.symbol.Variable(name+"_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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fc = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name=name)
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if args.margin_a!=1.0 or args.margin_m!=0.0 or args.margin_b!=0.0:
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if args.margin_a==1.0 and args.margin_m==0.0:
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s_m = s*args.margin_b
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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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fc = fc-gt_one_hot
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else:
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zy = mx.sym.pick(fc, gt_label, axis=1)
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cos_t = zy/s
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t = mx.sym.arccos(cos_t)
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if args.margin_a!=1.0:
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t = t*args.margin_a
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if args.margin_m>0.0:
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t = t+args.margin_m
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body = mx.sym.cos(t)
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if args.margin_b>0.0:
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body = body - args.margin_b
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new_zy = body*s
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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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fc = fc+body
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if args.ignore_label==0:
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softmax = mx.symbol.SoftmaxOutput(data=fc, label = gt_label, name=name+'_softmax', normalization='valid', grad_scale = args.grad_scale)
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else:
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softmax = mx.symbol.SoftmaxOutput(data=fc, label = gt_label, name=name+'_softmax', normalization='valid', use_ignore=True, ignore_label=args.ignore_label, grad_scale = args.grad_scale)
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return softmax
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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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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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all_label = mx.symbol.Variable('softmax_label')
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gt_label = all_label
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extra_loss = None
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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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nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
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out_list = [mx.symbol.BlockGrad(embedding)]
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_args = copy.deepcopy(args)
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if USE_FR:
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_args.grad_scale = 1.0
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fr_label = mx.symbol.slice_axis(all_label, axis=1, begin=0, end=1)
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fr_label = mx.symbol.reshape(fr_label, (args.per_batch_size,))
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fr_softmax = get_softmax(_args, embedding, nembedding, fr_label, 'fc7')
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out_list.append(fr_softmax)
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if USE_GENDER:
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_args.grad_scale = 0.2
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_args.margin_a = 0.0
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_args.num_classes = 2
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gender_label = mx.symbol.slice_axis(all_label, axis=1, begin=1, end=2)
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gender_label = mx.symbol.reshape(gender_label, (args.per_batch_size,))
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gender_softmax = get_softmax(_args, embedding, nembedding, gender_label, 'fc8')
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out_list.append(gender_softmax)
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if USE_AGE:
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_args.grad_scale = 0.01
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_args.margin_a = 0.0
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_args.num_classes = 2
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for i in xrange(AGE):
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age_label = mx.symbol.slice_axis(all_label, axis=1, begin=2+i, end=3+i)
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age_label = mx.symbol.reshape(age_label, (args.per_batch_size,))
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age_softmax = get_softmax(_args, embedding, nembedding, age_label, 'fc9_%d'%(i))
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out_list.append(age_softmax)
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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.rescale_threshold = 0
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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_imgrec = os.path.join(data_dir, "train.rec")
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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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_, arg_params, aux_params = mx.model.load_checkpoint(vec[0], int(vec[1]))
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sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
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if args.network[0]=='s':
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data_shape_dict = {'data' : (args.per_batch_size,)+data_shape}
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spherenet.init_weights(sym, data_shape_dict, args.num_layers)
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#label_name = 'softmax_label'
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#label_shape = (args.batch_size,)
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model = mx.mod.Module(
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context = ctx,
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symbol = sym,
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)
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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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)
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val_rec = os.path.join(data_dir, "val.rec")
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val_iter = None
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if os.path.exists(val_rec):
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val_iter = FaceImageIter(
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batch_size = args.batch_size,
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data_shape = data_shape,
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path_imgrec = val_rec,
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shuffle = False,
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rand_mirror = False,
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mean = mean,
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)
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if args.loss_type<10:
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_metric = AccMetric()
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else:
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_metric = LossValueMetric()
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eval_metrics = []
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if USE_FR:
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_metric = AccMetric(pred_idx=1)
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eval_metrics.append(_metric)
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if USE_GENDER:
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_metric = AccMetric(pred_idx=2, name='gender')
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eval_metrics.append(_metric)
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elif USE_GENDER:
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_metric = AccMetric(pred_idx=1, name='gender')
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eval_metrics.append(_metric)
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if USE_AGE:
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_metric = MAEMetric()
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eval_metrics.append(_metric)
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_metric = CUMMetric()
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eval_metrics.append(_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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opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
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som = 20
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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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|
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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, None)
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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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def val_test():
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_metric = MAEMetric()
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val_metric = mx.metric.create(_metric)
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val_metric.reset()
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_metric2 = CUMMetric()
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val_metric2 = mx.metric.create(_metric2)
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|
val_metric2.reset()
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|
val_iter.reset()
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|
for i, eval_batch in enumerate(val_iter):
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model.forward(eval_batch, is_train=False)
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model.update_metric(val_metric, eval_batch.label)
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model.update_metric(val_metric2, eval_batch.label)
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_value = val_metric.get_name_value()[0][1]
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print('MAE: %f'%(_value))
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|
_value = val_metric2.get_name_value()[0][1]
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|
print('CUM: %f'%(_value))
|
|
|
|
|
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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]
|
|
save_step = [0]
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|
if len(args.lr_steps)==0:
|
|
lr_steps = [40000, 60000, 80000]
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|
if args.loss_type>=1 and args.loss_type<=7:
|
|
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)
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|
else:
|
|
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):
|
|
#global global_step
|
|
global_step[0]+=1
|
|
mbatch = global_step[0]
|
|
for _lr in lr_steps:
|
|
if mbatch==_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:
|
|
if val_iter is not None:
|
|
val_test()
|
|
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
|
|
if do_save:
|
|
print('saving', msave)
|
|
arg, aux = model.get_params()
|
|
mx.model.save_checkpoint(prefix, msave, model.symbol, arg, aux)
|
|
print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))
|
|
if args.max_steps>0 and mbatch>args.max_steps:
|
|
sys.exit(0)
|
|
|
|
epoch_cb = None
|
|
|
|
model.fit(train_dataiter,
|
|
begin_epoch = begin_epoch,
|
|
num_epoch = end_epoch,
|
|
eval_data = None,
|
|
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()
|
|
|