"""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
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
import face_image
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):
bins, issame_list = pickle.load(open(path, 'rb'))
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 xrange(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 xrange( 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 xrange(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 xrange( 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 xrange(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 xrange( 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()
prop = face_image.load_property(args.data_dir)
image_size = prop.image_size
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 xrange(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)
