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onnx_ijbc.py
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onnx_ijbc.py
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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
import torch
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from torch.utils.data import DataLoader
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
@torch.no_grad()
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))
return torch.from_numpy(output)
@torch.no_grad()
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 collate_fn(data):
return torch.cat(data, dim=0)
data_loader = DataLoader(
dataset, batch_size=128, drop_last=False, num_workers=4, collate_fn=collate_fn, )
num_iter = 0
for batch in data_loader:
batch = batch.numpy()
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))
result_dir = args.model_root
save_path = os.path.join(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.target))
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='/train_tmp/IJB_release/IJBC', type=str, help='')
parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
main(parser.parse_args())