# THIS FILE IS FOR EXPERIMENTS, USE train_softmax.py FOR NORMAL TRAINING.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import math
import random
import logging
import pickle
import numpy as np
from data import FaceImageIter
from data import FaceImageIterList
import mxnet as mx
from mxnet import ndarray as nd
import argparse
import mxnet.optimizer as optimizer
sys.path.append(os.path.join(os.path.dirname(__file__), 'common'))
import face_image
from noise_sgd import NoiseSGD
sys.path.append(os.path.join(os.path.dirname(__file__), 'eval'))
sys.path.append(os.path.join(os.path.dirname(__file__), 'symbols'))
import fresnet
import finception_resnet_v2
import fmobilenet 
import fmobilenetv2
import fxception
import fdensenet
import fdpn
import fnasnet
import spherenet
#import lfw
import verification
import sklearn
sys.path.append(os.path.join(os.path.dirname(__file__), 'losses'))
import center_loss


logger = logging.getLogger()
logger.setLevel(logging.INFO)


args = None


class AccMetric(mx.metric.EvalMetric):
  def __init__(self):
    self.axis = 1
    super(AccMetric, self).__init__(
        'acc', axis=self.axis,
        output_names=None, label_names=None)
    self.losses = []
    self.count = 0

  def update(self, labels, preds):
    self.count+=1
    if args.loss_type>=2 and args.loss_type<=7 and args.margin_verbose>0:
      if self.count%args.ctx_num==0:
        mbatch = self.count//args.ctx_num
        _verbose = args.margin_verbose
        if mbatch==1 or mbatch%_verbose==0:
          a = 0.0
          b = 0.0
          if len(preds)>=4:
            a = preds[-2].asnumpy()[0]
            b = preds[-1].asnumpy()[0]
          elif len(preds)==3:
            a = preds[-1].asnumpy()[0]
            b = a
          print('[%d][MARGIN]%f,%f'%(mbatch,a,b))
    if args.logits_verbose>0:
      if self.count%args.ctx_num==0:
        mbatch = self.count//args.ctx_num
        _verbose = args.logits_verbose
        if mbatch==1 or mbatch%_verbose==0:
          a = 0.0
          b = 0.0
          if len(preds)>=3:
            v = preds[-1].asnumpy()
            v = np.sort(v)
            num = len(v)//10
            a = np.mean(v[0:num])
            b = np.mean(v[-1*num:])
          print('[LOGITS] %d,%f,%f'%(mbatch,a,b))
    #loss = preds[2].asnumpy()[0]
    #if len(self.losses)==20:
    #  print('ce loss', sum(self.losses)/len(self.losses))
    #  self.losses = []
    #self.losses.append(loss)
    preds = [preds[1]] #use softmax output
    for label, pred_label in zip(labels, preds):
        #print(pred_label)
        #print(label.shape, pred_label.shape)
        if pred_label.shape != label.shape:
            pred_label = mx.ndarray.argmax(pred_label, axis=self.axis)
        pred_label = pred_label.asnumpy().astype('int32').flatten()
        label = label.asnumpy()
        if label.ndim==2:
          label = label[:,0]
        label = label.astype('int32').flatten()
        #print(label)
        #print('label',label)
        #print('pred_label', pred_label)
        assert label.shape==pred_label.shape
        self.sum_metric += (pred_label.flat == label.flat).sum()
        self.num_inst += len(pred_label.flat)

class LossValueMetric(mx.metric.EvalMetric):
  def __init__(self):
    self.axis = 1
    super(LossValueMetric, self).__init__(
        'lossvalue', axis=self.axis,
        output_names=None, label_names=None)
    self.losses = []

  def update(self, labels, preds):
    loss = preds[-1].asnumpy()[0]
    self.sum_metric += loss
    self.num_inst += 1.0
    gt_label = preds[-2].asnumpy()
    #print(gt_label)

def parse_args():
  parser = argparse.ArgumentParser(description='Train face network')
  # general
  parser.add_argument('--data-dir', default='', help='training set directory')
  parser.add_argument('--prefix', default='../model/model', help='directory to save model.')
  parser.add_argument('--pretrained', default='', help='pretrained model to load')
  parser.add_argument('--ckpt', type=int, default=1, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')
  parser.add_argument('--network', default='r50', help='specify network')
  parser.add_argument('--version-se', type=int, default=0, help='whether to use se in network')
  parser.add_argument('--version-input', type=int, default=1, help='network input config')
  parser.add_argument('--version-output', type=str, default='E', help='network embedding output config')
  parser.add_argument('--version-unit', type=int, default=3, help='resnet unit config')
  parser.add_argument('--version-act', type=str, default='prelu', help='network activation config')
  parser.add_argument('--end-epoch', type=int, default=100000, help='training epoch size.')
  parser.add_argument('--noise-sgd', type=float, default=0.0, help='')
  parser.add_argument('--lr', type=float, default=0.1, help='start learning rate')
  parser.add_argument('--wd', type=float, default=0.0005, help='weight decay')
  parser.add_argument('--mom', type=float, default=0.9, help='momentum')
  parser.add_argument('--emb-size', type=int, default=512, help='embedding length')
  parser.add_argument('--per-batch-size', type=int, default=128, help='batch size in each context')
  parser.add_argument('--margin-m', type=float, default=0.5, help='')
  parser.add_argument('--margin-s', type=float, default=64.0, help='')
  parser.add_argument('--margin-a', type=float, default=0.0, help='')
  parser.add_argument('--margin-b', type=float, default=0.0, help='')
  parser.add_argument('--easy-margin', type=int, default=0, help='')
  parser.add_argument('--margin-verbose', type=int, default=0, help='')
  parser.add_argument('--logits-verbose', type=int, default=0, help='')
  parser.add_argument('--c2c-threshold', type=float, default=0.0, help='')
  parser.add_argument('--c2c-mode', type=int, default=-10, help='')
  parser.add_argument('--output-c2c', type=int, default=0, help='')
  parser.add_argument('--train-limit', type=int, default=0, help='')
  parser.add_argument('--margin', type=int, default=4, help='')
  parser.add_argument('--beta', type=float, default=1000., help='')
  parser.add_argument('--beta-min', type=float, default=5., help='')
  parser.add_argument('--beta-freeze', type=int, default=0, help='')
  parser.add_argument('--gamma', type=float, default=0.12, help='')
  parser.add_argument('--power', type=float, default=1.0, help='')
  parser.add_argument('--scale', type=float, default=0.9993, help='')
  parser.add_argument('--center-alpha', type=float, default=0.5, help='')
  parser.add_argument('--center-scale', type=float, default=0.003, help='')
  parser.add_argument('--images-per-identity', type=int, default=0, help='')
  parser.add_argument('--triplet-bag-size', type=int, default=3600, help='')
  parser.add_argument('--triplet-alpha', type=float, default=0.3, help='')
  parser.add_argument('--triplet-max-ap', type=float, default=0.0, help='')
  parser.add_argument('--verbose', type=int, default=2000, help='')
  parser.add_argument('--loss-type', type=int, default=4, help='')
  parser.add_argument('--incay', type=float, default=0.0, help='feature incay')
  parser.add_argument('--use-deformable', type=int, default=0, help='')
  parser.add_argument('--rand-mirror', type=int, default=1, help='')
  parser.add_argument('--cutoff', type=int, default=0, help='')
  parser.add_argument('--patch', type=str, default='0_0_96_112_0',help='')
  parser.add_argument('--lr-steps', type=str, default='', help='')
  parser.add_argument('--max-steps', type=int, default=0, help='')
  parser.add_argument('--target', type=str, default='lfw,cfp_fp,agedb_30,cplfw,calfw', help='')
  args = parser.parse_args()
  return args


def get_symbol(args, arg_params, aux_params):
  data_shape = (args.image_channel,args.image_h,args.image_w)
  image_shape = ",".join([str(x) for x in data_shape])
  margin_symbols = []
  if args.network[0]=='d':
    embedding = fdensenet.get_symbol(args.emb_size, args.num_layers,
        version_se=args.version_se, version_input=args.version_input, 
        version_output=args.version_output, version_unit=args.version_unit)
  elif args.network[0]=='m':
    print('init mobilenet', args.num_layers)
    if args.num_layers==1:
      embedding = fmobilenet.get_symbol(args.emb_size, 
          version_se=args.version_se, version_input=args.version_input, 
          version_output=args.version_output, version_unit=args.version_unit)
    else:
      embedding = fmobilenetv2.get_symbol(args.emb_size)
  elif args.network[0]=='i':
    print('init inception-resnet-v2', args.num_layers)
    embedding = finception_resnet_v2.get_symbol(args.emb_size,
        version_se=args.version_se, version_input=args.version_input, 
        version_output=args.version_output, version_unit=args.version_unit)
  elif args.network[0]=='x':
    print('init xception', args.num_layers)
    embedding = fxception.get_symbol(args.emb_size,
        version_se=args.version_se, version_input=args.version_input, 
        version_output=args.version_output, version_unit=args.version_unit)
  elif args.network[0]=='p':
    print('init dpn', args.num_layers)
    embedding = fdpn.get_symbol(args.emb_size, args.num_layers,
        version_se=args.version_se, version_input=args.version_input, 
        version_output=args.version_output, version_unit=args.version_unit)
  elif args.network[0]=='n':
    print('init nasnet', args.num_layers)
    embedding = fnasnet.get_symbol(args.emb_size)
  elif args.network[0]=='s':
    print('init spherenet', args.num_layers)
    embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
  else:
    print('init resnet', args.num_layers)
    embedding = fresnet.get_symbol(args.emb_size, args.num_layers, 
        version_se=args.version_se, version_input=args.version_input, 
        version_output=args.version_output, version_unit=args.version_unit,
        version_act=args.version_act)
  all_label = mx.symbol.Variable('softmax_label')
  if not args.output_c2c:
    gt_label = all_label
  else:
    gt_label = mx.symbol.slice_axis(all_label, axis=1, begin=0, end=1)
    gt_label = mx.symbol.reshape(gt_label, (args.per_batch_size,))
    c2c_label = mx.symbol.slice_axis(all_label, axis=1, begin=1, end=2)
    c2c_label = mx.symbol.reshape(c2c_label, (args.per_batch_size,))
  assert args.loss_type>=0
  extra_loss = None
  if args.loss_type==0: #softmax
    _weight = mx.symbol.Variable('fc7_weight')
    _bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
    fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name='fc7')
  elif args.loss_type==1: #sphere
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    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=1000, name='fc7')
  elif args.loss_type==8: #centerloss, TODO
    _weight = mx.symbol.Variable('fc7_weight')
    _bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
    fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name='fc7')
    print('center-loss', args.center_alpha, args.center_scale)
    extra_loss = mx.symbol.Custom(data=embedding, label=gt_label, name='center_loss', op_type='centerloss',\
          num_class=args.num_classes, alpha=args.center_alpha, scale=args.center_scale, batchsize=args.per_batch_size)
  elif args.loss_type==2:
    s = args.margin_s
    m = args.margin_m
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    if s>0.0:
      nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
      fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
      if m>0.0:
        if args.margin_verbose>0:
          zy = mx.sym.pick(fc7, gt_label, axis=1)
          cos_t = zy/s
          margin_symbols.append(mx.symbol.mean(cos_t))

        s_m = s*m
        gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
        fc7 = fc7-gt_one_hot

        if args.margin_verbose>0:
          new_zy = mx.sym.pick(fc7, gt_label, axis=1)
          new_cos_t = new_zy/s
          margin_symbols.append(mx.symbol.mean(new_cos_t))
    else:
      fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
      if m>0.0:
        body = embedding*embedding
        body = mx.sym.sum_axis(body, axis=1, keepdims=True)
        body = mx.sym.sqrt(body)
        body = body*m
        gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
        body = mx.sym.broadcast_mul(gt_one_hot, body)
        fc7 = fc7-body

  elif args.loss_type==3:
    s = args.margin_s
    m = args.margin_m
    assert args.margin==2 or args.margin==4
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
    zy = mx.sym.pick(fc7, gt_label, axis=1)
    cos_t = zy/s
    if args.margin_verbose>0:
      margin_symbols.append(mx.symbol.mean(cos_t))
    if m>1.0:
      t = mx.sym.arccos(cos_t)
      t = t*m
      body = mx.sym.cos(t)
      new_zy = body*s
      if args.margin_verbose>0:
        new_cos_t = new_zy/s
        margin_symbols.append(mx.symbol.mean(new_cos_t))
      diff = new_zy - zy
      diff = mx.sym.expand_dims(diff, 1)
      gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
      body = mx.sym.broadcast_mul(gt_one_hot, diff)
      fc7 = fc7+body

    #threshold = math.cos(args.margin_m)
    #cond_v = cos_t - threshold
    #cond = mx.symbol.Activation(data=cond_v, act_type='relu')
    #body = cos_t
    #for i in xrange(args.margin//2):
    #  body = body*body
    #  body = body*2-1
    #new_zy = body*s
    #zy_keep = zy
    #new_zy = mx.sym.where(cond, new_zy, zy_keep)
    #if args.margin_verbose>0:
    #  new_cos_t = new_zy/s
    #  margin_symbols.append(mx.symbol.mean(new_cos_t))
    #diff = new_zy - zy
    #diff = mx.sym.expand_dims(diff, 1)
    #gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
    #body = mx.sym.broadcast_mul(gt_one_hot, diff)
    #fc7 = fc7+body
  elif args.loss_type==4:
    s = args.margin_s
    m = args.margin_m
    assert s>0.0
    assert m>=0.0
    assert m<(math.pi/2)
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
    zy = mx.sym.pick(fc7, gt_label, axis=1)
    cos_t = zy/s
    if args.margin_verbose>0:
      margin_symbols.append(mx.symbol.mean(cos_t))
    if args.output_c2c==0:
      cos_m = math.cos(m)
      sin_m = math.sin(m)
      mm = math.sin(math.pi-m)*m
      #threshold = 0.0
      threshold = math.cos(math.pi-m)
      if args.easy_margin:
        cond = mx.symbol.Activation(data=cos_t, act_type='relu')
      else:
        cond_v = cos_t - threshold
        cond = mx.symbol.Activation(data=cond_v, act_type='relu')
      body = cos_t*cos_t
      body = 1.0-body
      sin_t = mx.sym.sqrt(body)
      new_zy = cos_t*cos_m
      b = sin_t*sin_m
      new_zy = new_zy - b
      new_zy = new_zy*s
      if args.easy_margin:
        zy_keep = zy
      else:
        zy_keep = zy - s*mm
      new_zy = mx.sym.where(cond, new_zy, zy_keep)
    else:
      #set c2c as cosm^2 in data.py
      cos_m = mx.sym.sqrt(c2c_label)
      sin_m = 1.0-c2c_label
      sin_m = mx.sym.sqrt(sin_m)
      body = cos_t*cos_t
      body = 1.0-body
      sin_t = mx.sym.sqrt(body)
      new_zy = cos_t*cos_m
      b = sin_t*sin_m
      new_zy = new_zy - b
      new_zy = new_zy*s

    if args.margin_verbose>0:
      new_cos_t = new_zy/s
      margin_symbols.append(mx.symbol.mean(new_cos_t))
    diff = new_zy - zy
    diff = mx.sym.expand_dims(diff, 1)
    gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
    body = mx.sym.broadcast_mul(gt_one_hot, diff)
    fc7 = fc7+body
  elif args.loss_type==5:
    s = args.margin_s
    m = args.margin_m
    assert s>0.0
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
    if args.margin_a!=1.0 or args.margin_m!=0.0 or args.margin_b!=0.0:
      if args.margin_a==1.0 and args.margin_m==0.0:
        s_m = s*args.margin_b
        gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
        fc7 = fc7-gt_one_hot
      else:
        zy = mx.sym.pick(fc7, gt_label, axis=1)
        cos_t = zy/s
        t = mx.sym.arccos(cos_t)
        if args.margin_a!=1.0:
          t = t*args.margin_a
        if args.margin_m>0.0:
          t = t+args.margin_m
        body = mx.sym.cos(t)
        if args.margin_b>0.0:
          body = body - args.margin_b
        new_zy = body*s
        diff = new_zy - zy
        diff = mx.sym.expand_dims(diff, 1)
        gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
        body = mx.sym.broadcast_mul(gt_one_hot, diff)
        fc7 = fc7+body
  elif args.loss_type==6:
    s = args.margin_s
    m = args.margin_m
    assert s>0.0
    assert m>=0.0
    assert m<(math.pi/2)
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
    zy = mx.sym.pick(fc7, gt_label, axis=1)
    cos_t = zy/s
    t = mx.sym.arccos(cos_t)
    if args.margin_verbose>0:
      margin_symbols.append(mx.symbol.mean(t))
    t_min = mx.sym.min(t)
    ta = mx.sym.broadcast_div(t_min, t)

    a1 = args.margin_a
    r1 = ta-a1
    r1 = mx.symbol.Activation(data=r1, act_type='relu')
    r1 = r1+a1

    r2 = mx.symbol.zeros(shape=(args.per_batch_size,))

    cond = t-1.0
    cond = mx.symbol.Activation(data=cond, act_type='relu')
    r = mx.sym.where(cond, r2, r1)
    var_m = r*m
    t = t+var_m
    body = mx.sym.cos(t)
    new_zy = body*s
    if args.margin_verbose>0:
      #new_cos_t = new_zy/s
      #margin_symbols.append(mx.symbol.mean(new_cos_t))
      margin_symbols.append(mx.symbol.mean(t))
    diff = new_zy - zy
    diff = mx.sym.expand_dims(diff, 1)
    gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
    body = mx.sym.broadcast_mul(gt_one_hot, diff)
    fc7 = fc7+body
  elif args.loss_type==7:
    s = args.margin_s
    m = args.margin_m
    assert s>0.0
    assert m>=0.0
    assert m<(math.pi/2)
    _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
    zy = mx.sym.pick(fc7, gt_label, axis=1)
    cos_t = zy/s
    t = mx.sym.arccos(cos_t)
    if args.margin_verbose>0:
      margin_symbols.append(mx.symbol.mean(t))
    var_m = mx.sym.random.uniform(low=args.margin_b, high=args.margin_m, shape=(1,))
    t = mx.sym.broadcast_add(t,var_m)
    body = mx.sym.cos(t)
    new_zy = body*s
    if args.margin_verbose>0:
      #new_cos_t = new_zy/s
      #margin_symbols.append(mx.symbol.mean(new_cos_t))
      margin_symbols.append(mx.symbol.mean(t))
    diff = new_zy - zy
    diff = mx.sym.expand_dims(diff, 1)
    gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
    body = mx.sym.broadcast_mul(gt_one_hot, diff)
    fc7 = fc7+body
  elif args.loss_type==10: #marginal loss
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
    params = [1.2, 0.3, 1.0]
    n1 = mx.sym.expand_dims(nembedding, axis=1) #N,1,C
    n2 = mx.sym.expand_dims(nembedding, axis=0) #1,N,C
    body = mx.sym.broadcast_sub(n1, n2) #N,N,C
    body = body * body
    body = mx.sym.sum(body, axis=2) # N,N
    #body = mx.sym.sqrt(body)
    body = body - params[0]
    mask = mx.sym.Variable('extra')
    body = body*mask
    body = body+params[1]
    #body = mx.sym.maximum(body, 0.0)
    body = mx.symbol.Activation(data=body, act_type='relu')
    body = mx.sym.sum(body)
    body = body/(args.per_batch_size*args.per_batch_size-args.per_batch_size)
    extra_loss = mx.symbol.MakeLoss(body, grad_scale=params[2])
  elif args.loss_type==11: #npair loss
    params = [0.9, 0.2]
    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
    nembedding = mx.sym.transpose(nembedding)
    nembedding = mx.symbol.reshape(nembedding, (args.emb_size, args.per_identities, args.images_per_identity))
    nembedding = mx.sym.transpose(nembedding, axes=(2,1,0)) #2*id*512
    #nembedding = mx.symbol.reshape(nembedding, (args.emb_size, args.images_per_identity, args.per_identities))
    #nembedding = mx.sym.transpose(nembedding, axes=(1,2,0)) #2*id*512
    n1 = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=1)
    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==13: #triplet loss with angular margin
    m = args.margin_m
    sin_m = math.sin(m)
    cos_m = math.cos(m)
    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 = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)
    an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)

    ap = mx.symbol.arccos(ap)
    an = mx.symbol.arccos(an)
    triplet_loss = mx.symbol.Activation(data = (ap-an+args.margin_m), act_type='relu')

    #body = ap*ap
    #body = 1.0-body
    #body = mx.symbol.sqrt(body)
    #body = body*sin_m
    #ap = ap*cos_m
    #ap = ap-body
    #triplet_loss = mx.symbol.Activation(data = (an-ap), act_type='relu')

    triplet_loss = mx.symbol.mean(triplet_loss)
    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 args.logits_verbose>0:
      logits = mx.symbol.softmax(data = fc7)
      logits = mx.sym.pick(logits, gt_label, axis=1)
      margin_symbols.append(logits)
      #logit_max = mx.sym.max(logits)
      #logit_min = mx.sym.min(logits)
      #margin_symbols.append(logit_max)
      #margin_symbols.append(logit_min)
  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 and args.loss_type!=13:
      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 or args.loss_type==13:
          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 or args.loss_type==13:
      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'
    label_shape = (args.batch_size,)
    if args.output_c2c:
      label_shape = (args.batch_size,2)
    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 and args.loss_type!=13:
      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,
          cutoff               = args.cutoff,
          c2c_threshold        = args.c2c_threshold,
          output_c2c           = args.output_c2c,
          c2c_mode             = args.c2c_mode,
          limit                = args.train_limit,
          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,
            cutoff               = args.cutoff,
            c2c_threshold        = args.c2c_threshold,
            output_c2c           = args.output_c2c,
            c2c_mode             = args.c2c_mode,
            limit                = args.train_limit,
            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
    if args.noise_sgd>0.0:
      print('use noise sgd')
      opt = NoiseSGD(scale = args.noise_sgd, learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
    else:
      opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
    som = 20
    if args.loss_type==12 or args.loss_type==13:
      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, 10, data_extra, label_shape)
        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<=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)
    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)
          #  np.save(lfw_npy, X)
        print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))
      if mbatch<=args.beta_freeze:
        _beta = args.beta
      else:
        move = max(0, mbatch-args.beta_freeze)
        _beta = max(args.beta_min, args.beta*math.pow(1+args.gamma*move, -1.0*args.power))
      #print('beta', _beta)
      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()