Spaces:
Running
on
A10G
Running
on
A10G
File size: 10,321 Bytes
a22eb82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 |
import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
SRC = np.array(
[
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.5493, 92.3655],
[62.7299, 92.2041]]
, dtype=np.float32)
SRC[:, 0] += 8.0
class AlignedDataSet(mx.gluon.data.Dataset):
def __init__(self, root, lines, align=True):
self.lines = lines
self.root = root
self.align = align
def __len__(self):
return len(self.lines)
def __getitem__(self, idx):
each_line = self.lines[idx]
name_lmk_score = each_line.strip().split(' ')
name = os.path.join(self.root, name_lmk_score[0])
img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB)
landmark5 = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32).reshape((5, 2))
st = skimage.transform.SimilarityTransform()
st.estimate(landmark5, SRC)
img = cv2.warpAffine(img, st.params[0:2, :], (112, 112), borderValue=0.0)
img_1 = np.expand_dims(img, 0)
img_2 = np.expand_dims(np.fliplr(img), 0)
output = np.concatenate((img_1, img_2), axis=0).astype(np.float32)
output = np.transpose(output, (0, 3, 1, 2))
output = mx.nd.array(output)
return output
def extract(model_root, dataset):
model = ArcFaceORT(model_path=model_root)
model.check()
feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim))
def batchify_fn(data):
return mx.nd.concat(*data, dim=0)
data_loader = mx.gluon.data.DataLoader(
dataset, 128, last_batch='keep', num_workers=4,
thread_pool=True, prefetch=16, batchify_fn=batchify_fn)
num_iter = 0
for batch in data_loader:
batch = batch.asnumpy()
batch = (batch - model.input_mean) / model.input_std
feat = model.session.run(model.output_names, {model.input_name: batch})[0]
feat = np.reshape(feat, (-1, model.feat_dim * 2))
feat_mat[128 * num_iter: 128 * num_iter + feat.shape[0], :] = feat
num_iter += 1
if num_iter % 50 == 0:
print(num_iter)
return feat_mat
def read_template_media_list(path):
ijb_meta = pd.read_csv(path, sep=' ', header=None).values
templates = ijb_meta[:, 1].astype(np.int)
medias = ijb_meta[:, 2].astype(np.int)
return templates, medias
def read_template_pair_list(path):
pairs = pd.read_csv(path, sep=' ', header=None).values
t1 = pairs[:, 0].astype(np.int)
t2 = pairs[:, 1].astype(np.int)
label = pairs[:, 2].astype(np.int)
return t1, t2, label
def read_image_feature(path):
with open(path, 'rb') as fid:
img_feats = pickle.load(fid)
return img_feats
def image2template_feature(img_feats=None,
templates=None,
medias=None):
unique_templates = np.unique(templates)
template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
for count_template, uqt in enumerate(unique_templates):
(ind_t,) = np.where(templates == uqt)
face_norm_feats = img_feats[ind_t]
face_medias = medias[ind_t]
unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)
media_norm_feats = []
for u, ct in zip(unique_medias, unique_media_counts):
(ind_m,) = np.where(face_medias == u)
if ct == 1:
media_norm_feats += [face_norm_feats[ind_m]]
else: # image features from the same video will be aggregated into one feature
media_norm_feats += [np.mean(face_norm_feats[ind_m], axis=0, keepdims=True), ]
media_norm_feats = np.array(media_norm_feats)
template_feats[count_template] = np.sum(media_norm_feats, axis=0)
if count_template % 2000 == 0:
print('Finish Calculating {} template features.'.format(
count_template))
template_norm_feats = normalize(template_feats)
return template_norm_feats, unique_templates
def verification(template_norm_feats=None,
unique_templates=None,
p1=None,
p2=None):
template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
for count_template, uqt in enumerate(unique_templates):
template2id[uqt] = count_template
score = np.zeros((len(p1),))
total_pairs = np.array(range(len(p1)))
batchsize = 100000
sublists = [total_pairs[i: i + batchsize] for i in range(0, len(p1), batchsize)]
total_sublists = len(sublists)
for c, s in enumerate(sublists):
feat1 = template_norm_feats[template2id[p1[s]]]
feat2 = template_norm_feats[template2id[p2[s]]]
similarity_score = np.sum(feat1 * feat2, -1)
score[s] = similarity_score.flatten()
if c % 10 == 0:
print('Finish {}/{} pairs.'.format(c, total_sublists))
return score
def verification2(template_norm_feats=None,
unique_templates=None,
p1=None,
p2=None):
template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
for count_template, uqt in enumerate(unique_templates):
template2id[uqt] = count_template
score = np.zeros((len(p1),)) # save cosine distance between pairs
total_pairs = np.array(range(len(p1)))
batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation
sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]
total_sublists = len(sublists)
for c, s in enumerate(sublists):
feat1 = template_norm_feats[template2id[p1[s]]]
feat2 = template_norm_feats[template2id[p2[s]]]
similarity_score = np.sum(feat1 * feat2, -1)
score[s] = similarity_score.flatten()
if c % 10 == 0:
print('Finish {}/{} pairs.'.format(c, total_sublists))
return score
def main(args):
use_norm_score = True # if Ture, TestMode(N1)
use_detector_score = True # if Ture, TestMode(D1)
use_flip_test = True # if Ture, TestMode(F1)
assert args.target == 'IJBC' or args.target == 'IJBB'
start = timeit.default_timer()
templates, medias = read_template_media_list(
os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower()))
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
start = timeit.default_timer()
p1, p2, label = read_template_pair_list(
os.path.join('%s/meta' % args.image_path,
'%s_template_pair_label.txt' % args.target.lower()))
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
start = timeit.default_timer()
img_path = '%s/loose_crop' % args.image_path
img_list_path = '%s/meta/%s_name_5pts_score.txt' % (args.image_path, args.target.lower())
img_list = open(img_list_path)
files = img_list.readlines()
dataset = AlignedDataSet(root=img_path, lines=files, align=True)
img_feats = extract(args.model_root, dataset)
faceness_scores = []
for each_line in files:
name_lmk_score = each_line.split()
faceness_scores.append(name_lmk_score[-1])
faceness_scores = np.array(faceness_scores).astype(np.float32)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))
start = timeit.default_timer()
if use_flip_test:
img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:]
else:
img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]
if use_norm_score:
img_input_feats = img_input_feats
else:
img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True))
if use_detector_score:
print(img_input_feats.shape, faceness_scores.shape)
img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
else:
img_input_feats = img_input_feats
template_norm_feats, unique_templates = image2template_feature(
img_input_feats, templates, medias)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
start = timeit.default_timer()
score = verification(template_norm_feats, unique_templates, p1, p2)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
save_path = os.path.join(args.result_dir, "{}_result".format(args.target))
if not os.path.exists(save_path):
os.makedirs(save_path)
score_save_file = os.path.join(save_path, "{}.npy".format(args.model_root))
np.save(score_save_file, score)
files = [score_save_file]
methods = []
scores = []
for file in files:
methods.append(os.path.basename(file))
scores.append(np.load(file))
methods = np.array(methods)
scores = dict(zip(methods, scores))
x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
tpr_fpr_table = prettytable.PrettyTable(['Methods'] + [str(x) for x in x_labels])
for method in methods:
fpr, tpr, _ = roc_curve(label, scores[method])
fpr = np.flipud(fpr)
tpr = np.flipud(tpr)
tpr_fpr_row = []
tpr_fpr_row.append("%s-%s" % (method, args.target))
for fpr_iter in np.arange(len(x_labels)):
_, min_index = min(
list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
tpr_fpr_table.add_row(tpr_fpr_row)
print(tpr_fpr_table)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='do ijb test')
# general
parser.add_argument('--model-root', default='', help='path to load model.')
parser.add_argument('--image-path', default='', type=str, help='')
parser.add_argument('--result-dir', default='.', type=str, help='')
parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
main(parser.parse_args())
|