"""Helper for evaluation on the Labeled Faces in the Wild dataset 
"""

# MIT License
# 
# Copyright (c) 2016 David Sandberg
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import argparse
import sys
import numpy as np
from scipy import misc
from sklearn.model_selection import KFold
from scipy import interpolate
import sklearn
import cv2
import math
import datetime
import pickle
from sklearn.decomposition import PCA
import mxnet as mx
from mxnet import ndarray as nd


class LFold:
  def __init__(self, n_splits = 2, shuffle = False):
    self.n_splits = n_splits
    if self.n_splits>1:
      self.k_fold = KFold(n_splits = n_splits, shuffle = shuffle)

  def split(self, indices):
    if self.n_splits>1:
      return self.k_fold.split(indices)
    else:
      return [(indices, indices)]


def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
    
    tprs = np.zeros((nrof_folds,nrof_thresholds))
    fprs = np.zeros((nrof_folds,nrof_thresholds))
    accuracy = np.zeros((nrof_folds))
    indices = np.arange(nrof_pairs)
    #print('pca', pca)
    
    if pca==0:
      diff = np.subtract(embeddings1, embeddings2)
      dist = np.sum(np.square(diff),1)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
        #print('train_set', train_set)
        #print('test_set', test_set)
        if pca>0:
          print('doing pca on', fold_idx)
          embed1_train = embeddings1[train_set]
          embed2_train = embeddings2[train_set]
          _embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 )
          #print(_embed_train.shape)
          pca_model = PCA(n_components=pca)
          pca_model.fit(_embed_train)
          embed1 = pca_model.transform(embeddings1)
          embed2 = pca_model.transform(embeddings2)
          embed1 = sklearn.preprocessing.normalize(embed1)
          embed2 = sklearn.preprocessing.normalize(embed2)
          #print(embed1.shape, embed2.shape)
          diff = np.subtract(embed1, embed2)
          dist = np.sum(np.square(diff),1)
        
        # Find the best threshold for the fold
        acc_train = np.zeros((nrof_thresholds))
        for threshold_idx, threshold in enumerate(thresholds):
            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
        best_threshold_index = np.argmax(acc_train)
        #print('threshold', thresholds[best_threshold_index])
        for threshold_idx, threshold in enumerate(thresholds):
            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])
        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])
          
    tpr = np.mean(tprs,0)
    fpr = np.mean(fprs,0)
    return tpr, fpr, accuracy

def calculate_accuracy(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    tp = np.sum(np.logical_and(predict_issame, actual_issame))
    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
  
    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)
    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)
    acc = float(tp+tn)/dist.size
    return tpr, fpr, acc


  
def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
    
    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)
    
    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff),1)
    indices = np.arange(nrof_pairs)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
      
        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train)>=far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0
    
        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean


def calculate_val_far(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    n_same = np.sum(actual_issame)
    n_diff = np.sum(np.logical_not(actual_issame))
    #print(true_accept, false_accept)
    #print(n_same, n_diff)
    val = float(true_accept) / float(n_same)
    far = float(false_accept) / float(n_diff)
    return val, far

def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):
    # Calculate evaluation metrics
    thresholds = np.arange(0, 4, 0.01)
    embeddings1 = embeddings[0::2]
    embeddings2 = embeddings[1::2]
    tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,
        np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca)
    thresholds = np.arange(0, 4, 0.001)
    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
    return tpr, fpr, accuracy, val, val_std, far

def load_bin(path, image_size):
  try:
    with open(path, 'rb') as f:
      bins, issame_list = pickle.load(f) #py2
  except UnicodeDecodeError as e:
    with open(path, 'rb') as f:
      bins, issame_list = pickle.load(f, encoding='bytes') #py3
  data_list = []
  for flip in [0,1]:
    data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))
    data_list.append(data)
  for i in range(len(issame_list)*2):
    _bin = bins[i]
    img = mx.image.imdecode(_bin)
    if img.shape[1]!=image_size[0]:
      img = mx.image.resize_short(img, image_size[0])
    img = nd.transpose(img, axes=(2, 0, 1))
    for flip in [0,1]:
      if flip==1:
        img = mx.ndarray.flip(data=img, axis=2)
      data_list[flip][i][:] = img
    if i%1000==0:
      print('loading bin', i)
  print(data_list[0].shape)
  return (data_list, issame_list)

def test(data_set, mx_model, batch_size, nfolds=10, data_extra = None, label_shape = None):
  print('testing verification..')
  data_list = data_set[0]
  issame_list = data_set[1]
  model = mx_model
  embeddings_list = []
  if data_extra is not None:
    _data_extra = nd.array(data_extra)
  time_consumed = 0.0
  if label_shape is None:
    _label = nd.ones( (batch_size,) )
  else:
    _label = nd.ones( label_shape )
  for i in range( len(data_list) ):
    data = data_list[i]
    embeddings = None
    ba = 0
    while ba<data.shape[0]:
      bb = min(ba+batch_size, data.shape[0])
      count = bb-ba
      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)
      #print(_data.shape, _label.shape)
      time0 = datetime.datetime.now()
      if data_extra is None:
        db = mx.io.DataBatch(data=(_data,), label=(_label,))
      else:
        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))
      model.forward(db, is_train=False)
      net_out = model.get_outputs()
      #_arg, _aux = model.get_params()
      #__arg = {}
      #for k,v in _arg.iteritems():
      #  __arg[k] = v.as_in_context(_ctx)
      #_arg = __arg
      #_arg["data"] = _data.as_in_context(_ctx)
      #_arg["softmax_label"] = _label.as_in_context(_ctx)
      #for k,v in _arg.iteritems():
      #  print(k,v.context)
      #exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req="null", aux_states=_aux)
      #exe.forward(is_train=False)
      #net_out = exe.outputs
      _embeddings = net_out[0].asnumpy()
      time_now = datetime.datetime.now()
      diff = time_now - time0
      time_consumed+=diff.total_seconds()
      #print(_embeddings.shape)
      if embeddings is None:
        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )
      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]
      ba = bb
    embeddings_list.append(embeddings)

  _xnorm = 0.0
  _xnorm_cnt = 0
  for embed in embeddings_list:
    for i in range(embed.shape[0]):
      _em = embed[i]
      _norm=np.linalg.norm(_em)
      #print(_em.shape, _norm)
      _xnorm+=_norm
      _xnorm_cnt+=1
  _xnorm /= _xnorm_cnt

  embeddings = embeddings_list[0].copy()
  embeddings = sklearn.preprocessing.normalize(embeddings)
  acc1 = 0.0
  std1 = 0.0
  #_, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)
  #acc1, std1 = np.mean(accuracy), np.std(accuracy)

  #print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
  #embeddings = np.concatenate(embeddings_list, axis=1)
  embeddings = embeddings_list[0] + embeddings_list[1]
  embeddings = sklearn.preprocessing.normalize(embeddings)
  print(embeddings.shape)
  print('infer time', time_consumed)
  _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)
  acc2, std2 = np.mean(accuracy), np.std(accuracy)
  return acc1, std1, acc2, std2, _xnorm, embeddings_list

def test_badcase(data_set, mx_model, batch_size, name='', data_extra = None, label_shape = None):
  print('testing verification badcase..')
  data_list = data_set[0]
  issame_list = data_set[1]
  model = mx_model
  embeddings_list = []
  if data_extra is not None:
    _data_extra = nd.array(data_extra)
  time_consumed = 0.0
  if label_shape is None:
    _label = nd.ones( (batch_size,) )
  else:
    _label = nd.ones( label_shape )
  for i in range( len(data_list) ):
    data = data_list[i]
    embeddings = None
    ba = 0
    while ba<data.shape[0]:
      bb = min(ba+batch_size, data.shape[0])
      count = bb-ba
      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)
      #print(_data.shape, _label.shape)
      time0 = datetime.datetime.now()
      if data_extra is None:
        db = mx.io.DataBatch(data=(_data,), label=(_label,))
      else:
        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))
      model.forward(db, is_train=False)
      net_out = model.get_outputs()
      _embeddings = net_out[0].asnumpy()
      time_now = datetime.datetime.now()
      diff = time_now - time0
      time_consumed+=diff.total_seconds()
      if embeddings is None:
        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )
      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]
      ba = bb
    embeddings_list.append(embeddings)
  embeddings = embeddings_list[0] + embeddings_list[1]
  embeddings = sklearn.preprocessing.normalize(embeddings)
  thresholds = np.arange(0, 4, 0.01)
  actual_issame = np.asarray(issame_list)
  nrof_folds = 10
  embeddings1 = embeddings[0::2]
  embeddings2 = embeddings[1::2]
  assert(embeddings1.shape[0] == embeddings2.shape[0])
  assert(embeddings1.shape[1] == embeddings2.shape[1])
  nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
  nrof_thresholds = len(thresholds)
  k_fold = LFold(n_splits=nrof_folds, shuffle=False)
  
  tprs = np.zeros((nrof_folds,nrof_thresholds))
  fprs = np.zeros((nrof_folds,nrof_thresholds))
  accuracy = np.zeros((nrof_folds))
  indices = np.arange(nrof_pairs)
  
  diff = np.subtract(embeddings1, embeddings2)
  dist = np.sum(np.square(diff),1)
  data = data_list[0]

  pouts = []
  nouts = []
  
  for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
       
      # Find the best threshold for the fold
      acc_train = np.zeros((nrof_thresholds))
      #print(train_set)
      #print(train_set.__class__)
      for threshold_idx, threshold in enumerate(thresholds):
          p2 = dist[train_set]
          p3 = actual_issame[train_set]
          _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, p2, p3)
      best_threshold_index = np.argmax(acc_train)
      for threshold_idx, threshold in enumerate(thresholds):
          tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])
      _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])
      best_threshold = thresholds[best_threshold_index]
      for iid in test_set:
        ida = iid*2
        idb = ida+1
        asame = actual_issame[iid]
        _dist = dist[iid]
        violate = _dist - best_threshold
        if not asame:
          violate *= -1.0
        if violate>0.0:
          imga = data[ida].asnumpy().transpose( (1,2,0) )[...,::-1] #to bgr
          imgb = data[idb].asnumpy().transpose( (1,2,0) )[...,::-1]
          #print(imga.shape, imgb.shape, violate, asame, _dist)
          if asame:
            pouts.append( (imga, imgb, _dist, best_threshold, ida) )
          else:
            nouts.append( (imga, imgb, _dist, best_threshold, ida) )

        
  tpr = np.mean(tprs,0)
  fpr = np.mean(fprs,0)
  acc = np.mean(accuracy)
  pouts = sorted(pouts, key = lambda x: x[2], reverse=True)
  nouts = sorted(nouts, key = lambda x: x[2], reverse=False)
  print(len(pouts), len(nouts))
  print('acc', acc)
  gap = 10
  image_shape = (112,224,3)
  out_dir = "./badcases"
  if not os.path.exists(out_dir):
    os.makedirs(out_dir)
  if len(nouts)>0:
    threshold = nouts[0][3]
  else:
    threshold = pouts[-1][3]
  
  for item in [(pouts, 'positive(false_negative).png'), (nouts, 'negative(false_positive).png')]:
    cols = 4
    rows = 8000
    outs = item[0]
    if len(outs)==0:
      continue
    #if len(outs)==9:
    #  cols = 3
    #  rows = 3

    _rows = int(math.ceil(len(outs)/cols))
    rows = min(rows, _rows)
    hack = {}

    if name.startswith('cfp') and item[1].startswith('pos'):
      hack = {0:'manual/238_13.jpg.jpg', 6:'manual/088_14.jpg.jpg', 10:'manual/470_14.jpg.jpg', 25:'manual/238_13.jpg.jpg', 28:'manual/143_11.jpg.jpg'}

    filename = item[1]
    if len(name)>0:
      filename = name+"_"+filename
    filename = os.path.join(out_dir, filename)
    img = np.zeros( (image_shape[0]*rows+20, image_shape[1]*cols+(cols-1)*gap, 3), dtype=np.uint8 )
    img[:,:,:] = 255
    text_color = (0,0,153)
    text_color = (255,178,102)
    text_color = (153,255,51)
    for outi, out in enumerate(outs):
      row = outi//cols
      col = outi%cols
      if row==rows:
        break
      imga = out[0].copy()
      imgb = out[1].copy()
      if outi in hack:
        idx = out[4]
        print('noise idx',idx)
        aa = hack[outi]
        imgb = cv2.imread(aa)
        #if aa==1:
        #  imgb = cv2.transpose(imgb)
        #  imgb = cv2.flip(imgb, 1)
        #elif aa==3:
        #  imgb = cv2.transpose(imgb)
        #  imgb = cv2.flip(imgb, 0)
        #else:
        #  for ii in range(2):
        #    imgb = cv2.transpose(imgb)
        #    imgb = cv2.flip(imgb, 1)
      dist = out[2]
      _img = np.concatenate( (imga, imgb), axis=1 )
      k = "%.3f"%dist
      #print(k)
      font = cv2.FONT_HERSHEY_SIMPLEX
      cv2.putText(_img,k,(80,image_shape[0]//2+7), font, 0.6, text_color, 2)
      #_filename = filename+"_%d.png"%outi
      #cv2.imwrite(_filename, _img)
      img[row*image_shape[0]:(row+1)*image_shape[0], (col*image_shape[1]+gap*col):((col+1)*image_shape[1]+gap*col),:] = _img
    #threshold = outs[0][3]
    font = cv2.FONT_HERSHEY_SIMPLEX
    k = "threshold: %.3f"%threshold
    cv2.putText(img,k,(img.shape[1]//2-70,img.shape[0]-5), font, 0.6, text_color, 2)
    cv2.imwrite(filename, img)

def dumpR(data_set, mx_model, batch_size, name='', data_extra = None, label_shape = None):
  print('dump verification embedding..')
  data_list = data_set[0]
  issame_list = data_set[1]
  model = mx_model
  embeddings_list = []
  if data_extra is not None:
    _data_extra = nd.array(data_extra)
  time_consumed = 0.0
  if label_shape is None:
    _label = nd.ones( (batch_size,) )
  else:
    _label = nd.ones( label_shape )
  for i in range( len(data_list) ):
    data = data_list[i]
    embeddings = None
    ba = 0
    while ba<data.shape[0]:
      bb = min(ba+batch_size, data.shape[0])
      count = bb-ba
      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)
      #print(_data.shape, _label.shape)
      time0 = datetime.datetime.now()
      if data_extra is None:
        db = mx.io.DataBatch(data=(_data,), label=(_label,))
      else:
        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))
      model.forward(db, is_train=False)
      net_out = model.get_outputs()
      _embeddings = net_out[0].asnumpy()
      time_now = datetime.datetime.now()
      diff = time_now - time0
      time_consumed+=diff.total_seconds()
      if embeddings is None:
        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )
      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]
      ba = bb
    embeddings_list.append(embeddings)
  embeddings = embeddings_list[0] + embeddings_list[1]
  embeddings = sklearn.preprocessing.normalize(embeddings)
  actual_issame = np.asarray(issame_list)
  outname = os.path.join('temp.bin')
  with open(outname, 'wb') as f:
    pickle.dump((embeddings, issame_list), f, protocol=pickle.HIGHEST_PROTOCOL)

if __name__ == '__main__':

  parser = argparse.ArgumentParser(description='do verification')
  # general
  parser.add_argument('--data-dir', default='', help='')
  parser.add_argument('--model', default='../model/softmax,50', help='path to load model.')
  parser.add_argument('--target', default='lfw,cfp_ff,cfp_fp,agedb_30', help='test targets.')
  parser.add_argument('--gpu', default=0, type=int, help='gpu id')
  parser.add_argument('--batch-size', default=32, type=int, help='')
  parser.add_argument('--max', default='', type=str, help='')
  parser.add_argument('--mode', default=0, type=int, help='')
  parser.add_argument('--nfolds', default=10, type=int, help='')
  args = parser.parse_args()
  #sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
  #import face_image
  #prop = face_image.load_property(args.data_dir)
  #image_size = prop.image_size
  image_size = [112,112]
  print('image_size', image_size)
  ctx = mx.gpu(args.gpu)
  nets = []
  vec = args.model.split(',')
  prefix = args.model.split(',')[0]
  epochs = []
  if len(vec)==1:
    pdir = os.path.dirname(prefix)
    for fname in os.listdir(pdir):
      if not fname.endswith('.params'):
        continue
      _file = os.path.join(pdir, fname)
      if _file.startswith(prefix):
        epoch = int(fname.split('.')[0].split('-')[1])
        epochs.append(epoch)
    epochs = sorted(epochs, reverse=True)
    if len(args.max)>0:
      _max = [int(x) for x in args.max.split(',')]
      assert len(_max)==2
      if len(epochs)>_max[1]:
        epochs = epochs[_max[0]:_max[1]]

  else:
    epochs = [int(x) for x in vec[1].split('|')]
  print('model number', len(epochs))
  time0 = datetime.datetime.now()
  for epoch in epochs:
    print('loading',prefix, epoch)
    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
    #arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
    all_layers = sym.get_internals()
    sym = all_layers['fc1_output']
    model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)
    #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
    model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))])
    model.set_params(arg_params, aux_params)
    nets.append(model)
  time_now = datetime.datetime.now()
  diff = time_now - time0
  print('model loading time', diff.total_seconds())

  ver_list = []
  ver_name_list = []
  for name in args.target.split(','):
    path = os.path.join(args.data_dir,name+".bin")
    if os.path.exists(path):
      print('loading.. ', name)
      data_set = load_bin(path, image_size)
      ver_list.append(data_set)
      ver_name_list.append(name)

  if args.mode==0:
    for i in range(len(ver_list)):
      results = []
      for model in nets:
        acc1, std1, acc2, std2, xnorm, embeddings_list = test(ver_list[i], model, args.batch_size, args.nfolds)
        print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
        print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
        print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
        results.append(acc2)
      print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
  elif args.mode==1:
    model = nets[0]
    test_badcase(ver_list[0], model, args.batch_size, args.target)
  else:
    model = nets[0]
    dumpR(ver_list[0], model, args.batch_size, args.target)