def main(args):
backbone_type = 'resnet50_irse_mx'
model_type = model_dict[args.model_name]['weights']
model_pth = pj(model_dict[args.model_name]['path'], model_type)
model_pth = os.path.abspath(model_pth)
if torch.cuda.is_available():
use_device = 'cuda'
else:
use_device = 'cpu'
infer = Inference(backbone_type=backbone_type,
ckpt_fpath=model_pth,
device=use_device)
datapath = args.data
db_feature = {}
with torch.no_grad():
img_cv = cv2.imread('/Users/marschen/Ucloud/Data/debug.png')
# img_face = face_format(img_cv, 112)
# # cv2.imwrite('test.jpg',img_face)
# img_face = face_normalize(img_face)
# img_face = np.transpose(img_face,[2,0,1])
# print(img_face.shape)
# img_face = torch.FloatTensor(img_face)
# img_face = img_face.unsqueeze(dim=0)
# probe_feat = infer.execute3(img_face).reshape(1,-1)#[B,F]
probe_feat = infer.execute(img_cv).reshape(1, -1)
np.save(pj('/Users/marschen/Ucloud/Data', 'debug.npy'), probe_feat)
def main():
backbone_type = 'resnet50_irse_mx'
model_type = 'model_5932.pth'
model_pth = pj(
'./model_output/insight_face_res50irsemx_cosface_emore_dist/',
model_type)
batch_size = 64
if torch.cuda.is_available():
use_device = 'cuda'
else:
use_device = 'cpu'
infer = Inference(backbone_type=backbone_type,
ckpt_fpath=model_pth,
device=use_device)
# datapath = '/data/yefei/data/ucloud_elavator_face/'
datapath = '/data/yefei/data/0107_0114_result/'
# dataset_test = BasicLoaderV2(imgs_dir=datapath,extstr='.jpg')
dataset_test = BasicLoader(imgs_dir=datapath, extstr='.jpg')
dataloader_test = DataLoader(dataset=dataset_test,
num_workers=4,
batch_size=batch_size,
shuffle=False)
db_feature = {}
with torch.no_grad():
for cnt, batch in enumerate(dataloader_test):
# person_name = '34_技术服务部-韩晨红'
bimgs = batch['image'] #B,C,H,W
bfdname = batch['name']
if cnt % 100 == 0:
print('Executing %1.3f...' % (cnt / len(dataloader_test)))
else:
pass
cur_b_size = bimgs.shape[0]
probe_feat = infer.execute3(bimgs).reshape(cur_b_size, -1) #[B,F]
for idx, imgname in enumerate(bfdname):
db_feature[imgname] = probe_feat[idx].reshape(-1, 1)
#Below is for BasicLoaderV2
# print(probe_feat.shape)
# for idx,person_name in enumerate(bfdname):
#
# if person_name in db_feature.keys():
# db_feature[person_name]['probe'].append(probe_feat[idx,:].reshape(-1,1))
# else:
# db_feature[person_name] = {'probe':[probe_feat[idx,:].reshape(-1,1)]}
save_obj(db_feature, pj(datapath, backbone_type + '_batch_' + model_type))
print('Done')
def main():
backbone_type = 'resnet50_irse_mx'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist/best_model4tupu/1591242318_tupu_0.981.pth'
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
test_obj = CompareFace()
r_img_fpath = ''
t_img_fpath = ''
test_obj(model, r_img_fpath, t_img_fpath)
def _load_models(self):
####################### load_model #######################
backbone_type = 'resnet50_irse_mx'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist'#/model_1176.pth'
res50_model = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath).model
backbone_type = 'resnet50_irse_mx'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_dist' # /model_1176.pth'
res50ga_model = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath).model
# backbone_type = 'resnet101_irse_mx'
# ckpt_fpath = '/data/output/insight_face_res101irsemx_cosface_emore_dist'
# res101_model = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath).model
mx101_model = MXInference()
return dict(
res50=res50_model,
res50ga=res50ga_model,
# res101=res101_model,
mx101=mx101_model)
def main():
backbone_type = 'resnet101_irse_mx'
ckpt_fpath = '/data/output/insight_face_res101irsemx_cosface_emore_dist'
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
register_dir = '/data/FaceRecog/tupu_data/ucloud_staff_faces_update0602'
save_dir = '/data/FaceRecog/results/image_cache_0602_mtcnn_crop'
test_obj = InferenceVisual(model=model,
register_dir=register_dir,
save_dir=save_dir)
test_img_dir = '/data/FaceRecog/tupu_data/image_cache_0602_mtcnn_crop'
test_obj(test_img_dir)
def main():
backbone_type = 'resnet50_irse_mx'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist/best_model4tupu/1591242318_tupu_0.981.pth'
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
test_obj = CompareFace()
test_img_dir_list = [
# '/data/FaceRecog/tupu_data/ucloud_collections_update0601_label/592_产品架构部-丁永涛',
# '/data/FaceRecog/tupu_data/ucloud_collections_update0601_label/766_综合采购部-张崇',
# '/data/FaceRecog/tupu_data/ucloud_collections_update0601_label/808_企业文化部-邱雯云',
'/data/FaceRecog/tupu_data/ucloud_collections_update0601_label/422_技术服务二部-王云峰',
'/data/FaceRecog/tupu_data/ucloud_collections_update0601_label/606_技术服务一部-王立鹏',
]
for test_img_dir in test_img_dir_list:
test_obj(model, test_img_dir)
def main():
backbone_type = 'resnet50_irse_mx'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_dist'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1755.pth'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1804.pth'
data_dir = '/data/FaceRecog/tupu_data/valid_labeled_faces'
# data_dir = '/data/FaceRecog/tupu_data/valid_ucloud_eval_faces_update20200528'
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
eval = EvalTupu(data_dir=data_dir)
with torch.no_grad():
eval(model)
def main():
backbone_type = 'resnet50_irse_mx'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1755.pth'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1804.pth'
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
test_obj = EvalTriplet(
data_dir='/data/FaceRecog/tupu_data/zhongsheng_labeled_update0603',
visual_save_dir=
'/data/FaceRecog/results/zhongsheng_labeled_test_results')
# test_obj(model)
test_obj.calc_balanced_acc(model)
def main():
backbone_type = 'resnet50_irse_mx'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1755.pth'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1804.pth'
ckpt_fpath = '/Users/marschen/Ucloud/Project/FaceRecog/model_output/insight_face_res50irsemx_cosface_emore_dist/model_1240.pth'
infer = Inference(backbone_type=backbone_type,
ckpt_fpath=ckpt_fpath,
device='cpu')
model = infer.model
eval = EvalHegui()
with torch.no_grad():
eval(model)
def main():
backbone_type = 'resnet50_irse_mx'
ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_emore_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_dist'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1755.pth'
# ckpt_fpath = '/data/output/insight_face_res50irsemx_cosface_glintasia_full/model_1804.pth'
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
val_dataset = Dataset(data_dir='/data/data/glintasia',
shuffle=False,
eval=True)
eval = EvalGlintaisa(val_dataset=val_dataset)
with torch.no_grad():
eval(model)
def search_best_model():
backbone_type = 'resnet50_irse_v2'
# ckpt_dir = '/data/output/insight_face_res50irsev2_emore_dist'
ckpt_dir = '/data/output/insight_face_res50irsev2_emore_dist/best_model4tupu'
data_dir = '/data/FaceRecog/tupu_data/valid_labeled_faces'
test_obj = EvalTupu(data_dir=data_dir)
ckpt_fpath_list = [
os.path.join(ckpt_dir, ckpt_fn) for ckpt_fn in os.listdir(ckpt_dir)
if os.path.isfile(os.path.join(ckpt_dir, ckpt_fn))
and ckpt_fn.endswith('.pth')
]
for ckpt_fpath in tqdm(ckpt_fpath_list):
infer = Inference(backbone_type=backbone_type, ckpt_fpath=ckpt_fpath)
model = infer.model
test_obj(model)
def main(args):
if torch.cuda.is_available():
use_device = 'cuda'
use_cpu = False
else:
use_device = 'cpu'
use_cpu = True
if not args.use_onnx:
print('using pytorch')
backbone_type = 'resnet101_irse_mx'
model_type = model_dict[args.model_name]['weights']
model_pth = pj(model_dict[args.model_name]['path'], model_type)
model_pth = os.path.abspath(model_pth)
infer = Inference(backbone_type=backbone_type,
ckpt_fpath=model_pth,
device=use_device)
else:
print('using onnx')
model_pth = args.onnx_pth
model_pth = os.path.abspath(model_pth)
infer = ONNXModel(onnx_path=model_pth)
weights_faceDet = 'weights/face_det/mobilenet0.25_Final.pth'
weights_faceDet = os.path.abspath(weights_faceDet)
faceDet = RetinaFaceDet('mobile0.25', weights_faceDet , use_cpu=use_cpu, backbone_location='')
datapath = args.data
savepath = args.save
if os.path.isdir(savepath):
pass
else:
os.mkdir(savepath)
FLG_nface = False
if args.nface:
FLG_nface = True
if not os.path.isdir(args.nface):
os.mkdir(args.nface)
imglst = [ c for c in os.listdir(pj(datapath)) if c.lower().endswith('.jpg') ]
db_feature = []
db_label = []
scale = 1.3
with torch.no_grad():
for cnt,imgname in enumerate(imglst):
#decode image name
person_id = imgname
if cnt%10 == 0:
print('Executing %1.3f...'% ((cnt+1)/len(imglst)) , end='\r')
img_cv2 = cv2.imread(pj(datapath,imgname))
img_cv2 = cv2.resize(img_cv2,dsize=None,fx=args.moisac,fy=args.moisac)
img_cv2_det = cv2.resize(img_cv2,fx=args.det_scale,fy=args.det_scale,dsize=None)
dets = faceDet.execute(img_cv2_det, threshold=args.det_threshold, topk=5000,
keep_topk=500,
nms_threshold=0.2)
if dets is None:
continue
if len(dets) <= 0:
continue
max_face = None
max_face_sz = 0
for idx, b in enumerate(dets):
if b[4] < args.det_threshold:
continue
b = list(map(int, b))
"""
expand bbox and rescale into unscale size.
"""
pwidth = int((b[2] - b[0]) * scale / args.det_scale)
pheight = int((b[3] - b[1]) * scale / args.det_scale)
pcx = int((b[2] + b[0]) / 2 / args.det_scale)
pcy = int((b[3] + b[1]) / 2 / args.det_scale)
if pwidth > max_face_sz:
img_face = cv2.getRectSubPix(img_cv2, (pwidth, pheight), (pcx, pcy))
img_face = face_format(img_face, 112)
max_face = img_face.copy()
max_face_sz = pwidth
# cv2.imwrite('test.jpg',max_face)
# break
if max_face is not None:
max_face = cv2.resize(max_face, dsize=None, fx=args.moisacx112, fy=args.moisacx112)
if FLG_nface:
cv2.imwrite( pj(args.nface,imgname) , max_face )
if not args.use_onnx:
probe_feat = infer.execute(max_face).reshape(1,-1)#[B,F]
else:
img_format = _format(max_face)
img_data = _normalize(img_format)
img_data = np.transpose(img_data, axes=[2, 0, 1])
img_data = np.expand_dims(img_data, axis=0)
probe_feat = infer.forward(img_data)
db_feature.append(probe_feat)
db_label.append(person_id)
# end with torch.no_grad():
"""
save data into separate files each uuid to a .npy
"""
print('#'*5,'merging and saving')
db_feature = np.concatenate(db_feature,axis=0)
db_label = np.array(db_label)
save_obj({'X':db_feature,'y':db_label} , pj(savepath,'gallery_%s.pkl'%args.model_name))
print('Done')