def run_test_metric(num_imgs_list, net, sess, filename=None):
imdb = get_imdb('voc_2007_test')
if not filename:
filename = 'default/res101_faster_rcnn_iter_110000'
with timer('run_test_metric - mAP calc'):
f = io.BytesIO()
with stdout_redirector(f): #this stops some meaningless info on stdout
if len(num_imgs_list) == 1:
num_imgs = num_imgs_list[0]
if num_imgs == len(imdb.image_index):
mAP = test_net(sess,
net,
imdb,
filename,
max_per_image=100)
mAP_dict = {num_imgs: mAP}
else:
sampler = VOCImgSampler()
sample_images = sampler.get_imgs(num_imgs)
sample_names_dict = {num_imgs: sample_images}
mAP_dict = test_net_with_sample(
sess,
net,
imdb,
filename,
sample_images,
max_per_image=100,
sample_names_dict=sample_names_dict)
else:
sampler = VOCImgSampler()
sample_names_dict = sampler.get_nested_img_lists(num_imgs_list)
largest_num_imgs = (sorted(num_imgs_list))[-1]
sample_images = sample_names_dict[largest_num_imgs]
mAP_dict = test_net_with_sample(
sess,
net,
imdb,
filename,
sample_images,
max_per_image=100,
sample_names_dict=sample_names_dict)
return mAP_dict
def run_test_metric(self, num_imgs):
imdb = get_imdb('voc_2007_test')
filename = 'default/res101_faster_rcnn_iter_110000'
if num_imgs == len(imdb.image_index):
mAP = test_net(self._sess,
self._net_sep,
imdb,
filename,
max_per_image=100)
else:
sampler = VOCImgSampler()
sample_images = sampler.get_imgs(num_imgs)
mAP = test_net_with_sample(self._sess,
self._net_sep,
imdb,
filename,
sample_images,
max_per_image=100)
return mAP
net = mobilenetv1()
elif args.net == 'SGRN':
net = resnetGCN(num_layers=101)
else:
raise NotImplementedError
# load model
net.create_architecture(imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.eval()
if not torch.cuda.is_available():
net._device = 'cpu'
net.to(net._device)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
net.load_state_dict(
torch.load(args.model, map_location=lambda storage, loc: storage))
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
print('Loaded.')
# print(('Loading model check point from {:s}').format(model))
# net.load_state_dict(
# torch.load(model, map_location=lambda storage, loc: storage))
print('Loaded.')
test_net(net, imdb, filename, max_per_image=100) #args.max_per_image)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
filename,
max_per_image=args.max_per_image,
eccv14=args.eccv14)
sess.close()
net = resnet_C3C4(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_C3-R-cbam':
net = resnet_C3Rcbam(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_R-cbam':
net = resnet_Rcbam(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
else:
raise NotImplementedError
# load model
net.create_architecture(sess, "TEST", imdb.num_classes, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
#variables=tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
#pdb.set_trace()
#for v in variables:
#print('Varibles: %s' % v.name)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename, max_per_image=args.max_per_image,thresh=0)
sess.close()
#if im_name[0]=='T':
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for data/demo/' + testset + '/' + im_name)
im_rgb = im_name
demo(sess, net, im_name, im_rgb, testset)
########## calculate MAP
if map_or_not == 'true':
conf_threshs = [0.5]
test_file = open('test_result_conv4.txt', 'w')
for conf_thresh in conf_threshs:
pdb.set_trace()
test_net(sess,
net,
imdb,
filename,
test_file,
testset,
method,
max_per_image=100,
thresh=conf_thresh)
test_file.write('\n')
test_file.close()
if os.path.exists(
'/home/yangfan/low_visibility_object_recognition_system_V2.0/dual_modal_faster_rcnn/data/EELABdevkit/annotations_cache/imagesetfile_annots.pkl'
):
os.remove(
'/home/yangfan/low_visibility_object_recognition_system_V2.0/dual_modal_faster_rcnn/data/EELABdevkit/annotations_cache/imagesetfile_annots.pkl'
)
plt.show()
net = memory.mobilenetv1_memory()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdb.num_classes,
imdb.num_predicates,
tag='default')
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
roidb,
filename,
args.visualize,
iter_test=iter_test,
mode='all')
sess.close()
return features, (question_vectors, answer_vectors, candidate_vectors)
else:
return features, (question_vectors, answer_vectors)
if __name__ == '__main__':
args = parse_args()
print('Called with args: ')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
#if args.set_cfgs is not None:
# cfg_from_list(args.set_cfgs)
print("Using config:")
print(cfg)
output_dir = get_output_dir(args.dataset_name, args.tag)
print('Output will be saved to `{:s}`'.format(output_dir))
tb_dir = get_output_tb_dir(args.dataset_name, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
test_reader = tf_reader('/data/mm/dataset/activityNet/test.tfrecords',
cfg['TEST'])
net = Network(test_reader.dataset.output_types,
test_reader.dataset.output_shapes)
test_net(net, test_reader, output_dir, tb_dir)
#model = None
model = '/home/vador/Documents/project/AI/drl-rpn-tf-video/output-weights/voc_2007_train/output/default/voc_2007_train/vgg16_drl_rpn_iter_1.ckpt'
#model = '/home/vador/Documents/project/AI/drl-rpn-tf-video/drl-rpn-voc2007-2012-trainval/vgg16_drl_rpn_iter_110000.ckpt'
weight = 'weights3d'
if model:
filename = os.path.splitext(os.path.basename(model))[0]
else:
filename = weight
with tf.Session(config=tfconfig) as sess:
# load model
if model:
print(('Loading model check point from {:s}').format(model))
# Why the following line is needed ?? if i remove it, some var are not initialized ! WHY ?
#sess.run(tf.global_variables_initializer())
saver = tf.train.Saver() #import_meta_graph(model + '.meta')
saver.restore(sess, model)
for var in tf.global_variables():
if var.name == 'h_relu_weights_video:0':
print(var.name)
print(var.shape)
else:
print(('Loading initial weights from {:s}').format(weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename)
def launch_test(self, conf, hash_model):
'''
'''
args = {}
args['cfg_file'] = conf.frcnn_cfg
args['weight'] = conf.starting_weights
args['model'] = hash_model
args['imdb_name'] = conf.valid_set
args['comp_mode'] = False
args['tag'] = conf.frcnn_tag
args['net'] = conf.frcnn_net
args['set_cfgs'] = None
args['max_per_image'] = 5
print('Called with args:')
print(args)
if args['cfg_file'] is not None:
cfg_from_file(argsargs['cfg_file'])
if args['set_cfgs'] is not None:
cfg_from_list(args['set_cfgs'])
print('Using config:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the inialization weights
if args['model']:
filename = os.path.splitext(os.path.basename(args['model']))[0]
else:
filename = os.path.splitext(os.path.basename(args['weight']))[0]
tag = args['tag']
tag = tag if tag else 'default'
filename = tag + '/' + filename
# TODO This is really bad but it works, I'm sincerely sorry
conf_copy = copy.deepcopy(conf)
conf_copy.train_set = conf_copy.valid_set
imdb = get_imdb(args['imdb_name'], conf_copy)
print(args['imdb_name'])
imdb.competition_mode(args['comp_mode'])
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args['net'] == 'vgg16':
net = vgg16(batch_size=1)
elif args['net'] == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
elif args['net'] == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
elif args['net'] == 'res152':
net = resnetv1(batch_size=1, num_layers=152)
elif args['net'] == 'mobile':
net = mobilenetv1(batch_size=1)
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args['model']:
print(
('Loading model check point from {:s}').format(args['model']))
saver = tf.train.Saver()
saver.restore(sess, args['model'])
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args['weight']))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
filename,
max_per_image=args['max_per_image'])
sess.close()
raise NotImplementedError
elif(cfg.NET_TYPE == 'lidar'):
net = lidarnet(num_layers=101)
# load model
if(cfg.NET_TYPE == 'lidar'):
#TODO: Magic numbers, need to sort this out to flow through 3d anchor gen properly
net.create_architecture(
db.num_classes,
tag='default',
anchor_scales=cfg.LIDAR.ANCHOR_SCALES,
anchor_ratios=cfg.LIDAR.ANCHOR_ANGLES)
elif(cfg.NET_TYPE == 'image'):
net.create_architecture(
db.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.eval()
print(('Loading initial weights from {:s}').format(args.weights_file))
#file_dir = os.path.join(cfg.DATA_DIR,args.db_name,'weights',args.weights_file)
params = torch.load(args.weights_file, map_location=lambda storage, loc: storage)
net.load_state_dict(params)
print('Loaded.')
if not torch.cuda.is_available():
net._device = 'cpu'
net.to(net._device)
#TODO: Fix stupid output directory bullshit
test_net(net, db, args.out_dir, max_dets=args.max_num_dets, mode='val',thresh=0.5,draw_det=True,eval_det=True)
def testing(imdbval_name, classes, cfg_file, model, weights, tag, net,
max_per_image):
__sets = {}
for split in ['train', 'val', 'trainval', 'test']:
name = imdbval_name.split('_')[0] + '_{}'.format(split)
__sets[name] = (lambda split=split: dataset(split, classes,
name.split('_')[0]))
if cfg_file is not None:
cfg_from_file(cfg_file)
print('Using config:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the inialization weights
if model:
filename = os.path.splitext(os.path.basename(model))[0]
else:
filename = os.path.splitext(os.path.basename(weights))[0]
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(imdbval_name, __sets)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if net == 'vgg16':
net = vgg16(batch_size=1)
elif net == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
elif net == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
elif net == 'res152':
net = resnetv1(batch_size=1, num_layers=152)
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if model:
print(('Loading model check point from {:s}').format(model))
saver = tf.train.Saver()
saver.restore(sess, model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(weights))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename, max_per_image=max_per_image)
sess.close()
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
filename,
max_per_image=args.max_per_image,
roidb=roidb)
# ids = [262145, 131074, 131075, 393221, 393223, 393224, 524297, 393227]
#for imgs in ids:
# demo.demo(sess, net, imgs)
#plt.show()
sess.close()
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture(imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.eval()
if not torch.cuda.is_available():
net._device = 'cpu'
net.to(net._device)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
net.load_state_dict(
torch.load(args.model, map_location=lambda storage, loc: storage))
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
print('Loaded.')
test_net(net, imdb, roidb, filename, max_per_image=args.max_per_image)
net = memory.resnetv1_memory(num_layers=101)
elif net_base == 'res152':
net = memory.resnetv1_memory(num_layers=152)
elif net_base == 'mobile':
net = memory.mobilenetv1_memory()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST", imdb.num_classes, tag='default')
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
imdb.roidb,
filename,
args.visualize,
iter_test=iter_test)
sess.close()
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture(imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.eval()
if not torch.cuda.is_available():
net._device = 'cpu'
net.to(net._device)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
net.load_state_dict(
torch.load(args.model, map_location=lambda storage, loc: storage))
# net.load_state_dict(
# torch.load('output/vgg16/coco_2017_val/default/vgg16_faster_rcnn_iter_20000.pth',
# map_location=lambda storage, loc: storage))
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
print('Loaded.')
test_net(net, imdb, filename, max_per_image=args.max_per_image, thresh=.20)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture(imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.eval()
net.cuda()
if cfg.TEST.CLEAN_PRE_RESULT:
if args.model:
print(('Loading model check point from {:s}').format(args.model))
model_dict = torch.load(args.model)
print(model_dict.keys())
print(model_dict['vgg.classifier.0.weight'].shape)
net.load_state_dict(model_dict)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
print('Loaded.')
test_net(net,
imdb,
filename,
max_per_image=args.max_per_image,
clean_pre_result=cfg.TEST.CLEAN_PRE_RESULT)
sess = tf.Session(config=tfconfig)
net = resnetv1(num_layers=101)
# load model
net.create_architecture(sess, "TEST", imdb.num_classes, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
test_net(sess, net, imdb, filename, max_per_image=args.max_per_image)
# for var in tf.all_variables():
# print(var.op.name)
# print(sess.run(var))
# np.random.seed(cfg.RNG_SEED)
# """Test a Fast R-CNN network on an image database."""
# num_images = len(imdb.image_index)
# # all detections are collected into:
# # all_boxes[cls][image] = N x 5 array of detections in
# # (x1, y1, x2, y2, score)
# all_boxes = [[[] for _ in range(num_images)]
# for _ in range(imdb.num_classes)]
#
# output_dir = get_output_dir(imdb, filename)
# # im = cv2.imread('/home/aurora/workspaces/data/voc/voc2007/VOC2007/JPEGImages/006258.jpg')
# # im = cv2.imread('/home/aurora/workspaces/PycharmProjects/tensorflow/tf_rfcn/data/VOCdevkit2007/VOC2007/JPEGImages/000001.jpg')
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdbs[0].num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdbs,
filename,
max_per_image=args.max_per_image,
thresh_nms=args.threshold_nms,
thresh_map=args.threshold_map)
sess.close()
tag = 'default/rgh'
model = '/media/rgh/rgh-data/PycharmProjects/cvpr2018/output/vgg16/Lip_320_train/default/' \
+ 'vgg16_faster_rcnn_iter_70000.pth'
cfg.ANCHOR_SCALES = [4, 8, 16, 32]
cfg.ANCHOR_RATIOS = [0.5, 1, 2]
#cfg.HAS_PARSING_LABEL = False
cfg.POOLING_MODE = 'crop'
cfg.FC6_IN_CHANNEL = 512
cfg.TEST.CLEAN_PRE_RESULT = True
print('Using config:')
pprint.pprint(cfg)
imdb = get_imdb(imdb_name)
net = vgg16()
net.create_architecture(imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.eval()
net.cuda()
model_dict = torch.load(model)
print(model_dict.keys())
print(model_dict['vgg.classifier.0.weight'].shape)
net.load_state_dict(model_dict)
test_net(net,
imdb,
tag,
max_per_image=100,
clean_pre_result=cfg.TEST.CLEAN_PRE_RESULT)
if False:
# tensor flow conf
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
session = tf.Session(config=tfconfig)
# load model
net.create_architecture("TEST", imdb.num_classes, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print('Loading model check point from {:s}'.format(args.model))
saver = tf.train.Saver()
saver.restore(session, args.model)
print('Loaded.')
else:
print('Loading initial weights from {:s}'.format(args.weight))
session.run(tf.global_variables_initializer())
print('Loaded.')
model = FasterRCNN(session, net, imdb, max_per_image=args.max_per_image)
else:
model = Yolo(imdb)
test_net(model, imdb, filename)
session.close()
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
filename,
max_per_image=args.max_per_image,
conf_thresh=conf_thresh,
iou_thresh=iou_thresh)
sess.close()
weights_path = os.path.join(folder_path,weights_name)
np.save(weights_path, dic)
print 'The weights are saved in {}'.format(weights_path)
'''
load the new weigts to a new graph,
test the pruned network
'''
with tf.Graph().as_default() as g2:
with tf.Session(config=tfconfig,graph=g2).as_default() as sess:
#load the new graph
net = vgg16(batch_size=1)
net.create_architecture(sess,'TEST',num_classes,tag='default',
anchor_scales = [8,16,32],
filter_num = new_filter_num)
# load the new weights from npy file
weights_dic = np.load(weights_path).item()
for name_scope in weights_dic:
with tf.variable_scope(name_scope,reuse = True):
for name in weights_dic[name_scope]:
var = tf.get_variable(name)
sess.run(var.assign(weights_dic[name_scope][name]['value']))
print 'assign pretrain model to {}/{}'.format(name_scope,name)
# test the new model
imdb = get_imdb('voc_2007_test')
filename = 'demo_pruning'
test_net(sess, net, imdb, filename, max_per_image=100)
net = resnetv1_ssh(num_layers=50)
elif args.backbone == 'res101':
net = resnetv1_ssh(num_layers=101)
elif args.backbone == 'res152':
net = resnetv1_ssh(num_layers=152)
elif args.backbone == 'mobile':
net = mobilenetv1_ssh()
elif args.backbone == 'mobile_v2':
net = mobilenetv2_ssh()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST", imdb.num_classes, tag=tag,
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename, max_per_image=args.max_per_image)
sess.close()
net = vgg16()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST", imdb.num_classes, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
net.create_training_testing_vars()
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename, max_per_image=args.max_per_image, annot_file_tag=wait_name)
sess.close()
