def _check_if_continue(self, iter, max_iters, snapshot_add):
img_start_idx = cfg.DRL_RPN_TRAIN.IMG_START_IDX
if iter > img_start_idx:
return iter, max_iters, snapshot_add, False
if iter < img_start_idx:
print("iter %d < img_start_idx %d -- continuing" %
(iter, img_start_idx))
iter += 1
return iter, max_iters, snapshot_add, True
if iter == img_start_idx:
print("Adjusting stepsize, train-det-start etcetera")
snapshot_add = img_start_idx
max_iters -= img_start_idx
iter = 0
cfg_from_list(['DRL_RPN_TRAIN.IMG_START_IDX', -1])
cfg_from_list([
'DRL_RPN_TRAIN.DET_START',
cfg.DRL_RPN_TRAIN.DET_START - img_start_idx
])
cfg_from_list([
'DRL_RPN_TRAIN.STEPSIZE',
cfg.DRL_RPN_TRAIN.STEPSIZE - img_start_idx
])
cfg_from_list(
['TRAIN.STEPSIZE', [cfg.TRAIN.STEPSIZE[0] - img_start_idx]])
cfg_from_list([
'DRL_RPN_TRAIN.POST_SS',
[cfg.DRL_RPN_TRAIN.POST_SS[0] - img_start_idx]
])
print("Done adjusting stepsize, train-det-start etcetera")
return iter, max_iters, snapshot_add, False
def __init__(self, args):
self.imdb_name = args.imdb_name
self.net_name = args.net_name
self.tag = args.tag
self.iters = args.iters
# Config
cfg_file = osp.join(mrcn_dir,
'experiments/cfgs/%s.yml' % self.net_name)
cfg_list = [
'ANCHOR_SCALES', [4, 8, 16, 32], 'ANCHOR_RATIOS', [0.5, 1, 2]
]
if cfg_file is not None: cfg_from_file(cfg_file)
if cfg_list is not None: cfg_from_list(cfg_list)
print('Using config:')
pprint.pprint(cfg)
# load imdb
self.imdb = get_imdb(get_imdb_name(self.imdb_name)['TEST_IMDB'])
# Load network
self.net = self.load_net()
self._scale = None
def main(args):
opt = vars(args)
# initialize
opt['dataset_splitBy'] = opt['dataset'] + '_' + opt['splitBy']
checkpoint_dir = osp.join(opt['checkpoint_path'], opt['dataset_splitBy'])
if not osp.isdir(checkpoint_dir): os.makedirs(checkpoint_dir)
# set random seed
torch.manual_seed(opt['seed'])
random.seed(opt['seed'])
# set up loader
data_json = osp.join('cache/prepro', opt['dataset_splitBy'], 'data.json')
data_h5 = osp.join('cache/prepro', opt['dataset_splitBy'], 'data.h5')
loader = GtMRCNLoader(data_json, data_h5)
# set up model
opt['vocab_size'] = loader.vocab_size
opt['C4_feat_dim'] = 1024
net = resnetv1(opt, batch_size=opt['batch_size'],
num_layers=101) # determine batch size in opt.py
# output directory where the models are saved
output_dir = osp.join(opt['dataset_splitBy'],
'output_{}'.format(opt['output_postfix']))
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = osp.join(opt['dataset_splitBy'],
'tb_{}'.format(opt['output_postfix']))
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
cfg.TRAIN.USE_FLIPPED = orgflip
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)
#train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
train_net(
net,
loader,
output_dir,
tb_dir,
pretrained_model=
'pyutils/mask-faster-rcnn/output/res101/coco_2014_train_minus_refer_valtest+coco_2014_valminusminival/notime/res101_mask_rcnn_iter_1250000.pth',
max_iters=args.max_iters)
def __init__(self, args):
self.imdb_name = args.imdb_name
self.net_name = args.net_name
self.tag = args.tag
self.iters = args.iters
# Config
cfg_file = osp.join(mrcn_dir, 'experiments/cfgs/%s.yml' % self.net_name)
cfg_list = ['ANCHOR_SCALES', [4,8,16,32], 'ANCHOR_RATIOS', [0.5,1,2]]
if cfg_file is not None: cfg_from_file(cfg_file)
if cfg_list is not None: cfg_from_list(cfg_list)
print('Using config:')
pprint.pprint(cfg)
# Load network
self.num_classes = 81 # hard code this
self.net = self.load_net()
def my_main(imdb_name, network, cfg_file, set_cfgs, tag, max_iters, im_names,
score_thresh, clip_bbox):
# Clip bboxes after bbox reg to image boundary
cfg_from_list(['TEST.BBOX_CLIP', str(clip_bbox)])
# Already set everything here, so the path can be determined correctly
if cfg_file:
cfg_from_file(cfg_file)
if set_cfgs:
cfg_from_list(set_cfgs)
model_dir = osp.abspath(
osp.join(cfg.ROOT_DIR, 'output', 'frcnn', cfg.EXP_DIR, imdb_name, tag))
model = osp.join(
model_dir,
cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(max_iters) + '.pth')
output_dir = osp.join(model_dir, 'demo')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
args = {
'imdb_name': imdb_name,
'net': network,
'cfg_file': None,
'set_cfgs': None,
'tag': tag,
'output_dir': output_dir,
'model': model,
'im_names': im_names,
'score_thresh': score_thresh
}
print('Called with args:')
print(args)
frcnn_demo(args)
def evaluate(args):
opt = vars(args)
# make other options
opt['dataset_splitBy'] = opt['dataset'] + '_' + opt['splitBy']
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:')
pprint.pprint(cfg)
# set up loader
data_json = osp.join('cache/prepro', opt['dataset_splitBy'], 'data.json')
data_h5 = osp.join('cache/prepro', opt['dataset_splitBy'], 'data.h5')
loader = GtMRCNLoader(data_json, data_h5)
# set up model
opt['vocab_size'] = loader.vocab_size
opt['C4_feat_dim'] = 1024
net = resnetv1(opt, batch_size=1, num_layers=101)
net.create_architecture(81,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
sfile = osp.join(opt['dataset_splitBy'],
'output_{}'.format(opt['output_postfix']),
'res101_mask_rcnn_iter_{}.pth'.format(opt['model_iter']))
print('Restoring model snapshots from {:s}'.format(sfile))
saved_state_dict = torch.load(str(sfile))
count_1 = 0
new_params = net.state_dict().copy()
for name, param in new_params.items():
#print(name, param.size(), saved_state_dict[name].size())
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
#print('---- copy ----')
else:
print(name, '----')
count_1 += 1
print('size not match:', count_1)
net.load_state_dict(new_params)
net.eval()
net.cuda()
split = opt['split']
crit = None
acc, eval_seg_iou_list, seg_correct, seg_total, cum_I, cum_U, num_sent = eval_split(
loader, net, crit, split, opt)
print('Comprehension on %s\'s %s (%s sents) is %.2f%%' % \
(opt['dataset_splitBy'], split, num_sent, acc*100.))
# write to results.txt
f = open('experiments/det_results.txt', 'a')
f.write('[%s][%s], id[%s]\'s acc is %.2f%%\n' % \
(opt['dataset_splitBy'], opt['split'], opt['id'], acc*100.0))
# print
print('Segmentation results on [%s][%s]' % (opt['dataset_splitBy'], split))
results_str = ''
for n_eval_iou in range(len(eval_seg_iou_list)):
results_str += ' precision@%s = %.2f\n' % \
(str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]*100./seg_total)
results_str += ' overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
print(results_str)
# save results
#save_dir = osp.join('cache/results', opt['dataset_splitBy'], 'masks')
#if not osp.isdir(save_dir):
# os.makedirs(save_dir)
#results['iou'] = cum_I*1./cum_U
#assert 'rle' in results['predictions'][0]
#with open(osp.join(save_dir, args.id+'_'+split+'.json'), 'w') as f:
# json.dump(results, f)
# write to results.txt
f = open('experiments/mask_results.txt', 'a')
f.write('[%s][%s]\'s iou is:\n%s' % \
(opt['dataset_splitBy'], split, results_str))
imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb
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:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# train set
imdb, roidb = combined_roidb(args.imdb_name)
print('{:d} roidb entries'.format(len(roidb)))
# Set class names in config file based on IMDB
class_names = imdb.classes
cfg_from_list(['CLASS_NAMES', [class_names]])
# Update config depending on if class-specific history used or not
if not args.use_hist:
def main(args):
opt = vars(args)
# initialize
opt['dataset_splitBy'] = opt['dataset'] + '_' + opt['splitBy']
checkpoint_dir = osp.join(opt['checkpoint_path'], opt['dataset_splitBy'])
if not osp.isdir(checkpoint_dir): os.makedirs(checkpoint_dir)
# set random seed
torch.manual_seed(opt['seed'])
random.seed(opt['seed'])
# set up loader
data_json = osp.join('cache/prepro', opt['dataset_splitBy'], 'data.json')
data_h5 = osp.join('cache/prepro', opt['dataset_splitBy'], 'data.h5')
loader = CycleLoader(data_json, data_h5) ####
# set up model
opt['vocab_size']= loader.vocab_size
#opt['fc7_dim'] = loader.fc7_dim
#opt['pool5_dim'] = loader.pool5_dim
#opt['num_atts'] = loader.num_atts
#model = JointMatching(opt)
opt['C4_feat_dim'] = 1024
opt['use_att'] = utils.if_use_att(opt['caption_model'])
opt['seq_length'] = loader.label_length
#### can change to restore opt from info.pkl
#infos = {}
#histories = {}
if opt['start_from']is not None:
# open old infos and check if models are compatible
with open(os.path.join(opt['dataset_splitBy'], opt['start_from'], 'infos-best.pkl')) as f:
infos = cPickle.load(f)
saved_model_opt = infos['opt']
need_be_same = ['caption_model', 'rnn_type', 'rnn_size', 'num_layers']
for checkme in need_be_same:
assert vars(saved_model_opt)[checkme] == opt[checkme], "Command line argument and saved model disagree on '%s'" % checkme
#if os.path.isfile(os.path.join(opt['dataset_splitBy'], opt['start_from'], 'histories.pkl')):
# with open(os.path.join(opt['dataset_splitBy'], opt['start_from'], 'histories.pkl')) as f:
# histories = cPickle.load(f)
net = resnetv1(opt, batch_size=opt['batch_size'], num_layers=101) #### determine batch size in opt.py
# output directory where the models are saved
output_dir = osp.join(opt['dataset_splitBy'], 'output_{}'.format(opt['output_postfix']))
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = osp.join(opt['dataset_splitBy'], 'tb_{}'.format(opt['output_postfix']))
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
#_, valroidb = combined_roidb('coco_2014_minival')
#_, valroidb = combined_roidb('refcoco_test')
#print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
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)
#train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
train_net(net, loader, output_dir, tb_dir,
pretrained_model='pyutils/mask-faster-rcnn/output/res101/coco_2014_train_minus_refer_valtest+coco_2014_valminusminival/notime/res101_mask_rcnn_iter_1250000.pth',
max_iters=args.max_iters)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors)
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:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the initialization 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
sys.exit(1)
args = parser.parse_args()
return args
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:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the initialization 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
def launch_train(self, conf):
'''
'''
args = {}
args['cfg_file'] = conf.frcnn_cfg
args['weight'] = conf.starting_weights
args['imdb_name'] = conf.train_set
args['imdbval_name'] = conf.valid_set
args['max_iters'] = conf.iters
args['tag'] = conf.frcnn_tag
args['net'] = conf.frcnn_net
args['set_cfgs'] = None
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:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# train set
imdb, roidb = combined_roidb(args['imdb_name'], conf)
print('{:d} roidb entries'.format(len(roidb)))
# output directory where the models are saved
output_dir = conf.backup_folder #get_output_dir(imdb, args.tag)
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = conf.backup_folder # get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
_, valroidb = combined_roidb(args['imdbval_name'], conf)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
if args['net'] == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args['net'] == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args['net'] == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
# load network
elif args['net'] == 'res152':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152)
elif args['net'] == 'mobile':
net = mobilenetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH)
else:
raise NotImplementedError
train_net(net,
imdb,
roidb,
valroidb,
output_dir,
tb_dir,
pretrained_model=args['weight'],
max_iters=args['max_iters'])
for r in roidbs[1:]:
roidb.extend(r)
tmp = get_imdb(imdb_names.split('+')[1])
imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb
if __name__ == '__main__':
args = parse_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)
np.random.seed(cfg.RNG_SEED)
# train set
imdb, roidb = combined_roidb(args.imdb_name)
# Set class names in config file based on IMDB
class_names = imdb.classes
cfg_from_list(['CLASS_NAMES', [class_names]])
if args.alpha:
cfg_from_list(['LRP_HAI.ALPHA', True])
# Update config to match start of training detector
cfg_from_list(['LRP_HAI_TRAIN.DET_START', args.det_start])
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# setup matlpoltib to use without display
import matplotlib
matplotlib.use('Agg')
import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
this_dir = osp.dirname(__file__)
root_path = osp.join(this_dir, '..', '..')
# Add src to PYTHONPATH
src_path = osp.join(root_path, 'src')
add_path(src_path)
# Change paths in configs file
import frcnn
from model.config import cfg_from_list
data_path = osp.join(root_path, 'data')
cfg_from_list(['ROOT_DIR', root_path, 'DATA_DIR', data_path])
return args
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)
# cfg_from_file("D:/hoseok/project/pytorch-faster-rcnn/experiments/cfgs/res101.yml")
# cfg_from_file("D:/hoseok/project/pytorch-faster-rcnn/experiments/cfgs/res101_vg.yml")
cfg_from_list(["ANCHOR_SCALES", "[8,16,32]", "ANCHOR_RATIOS", "[0.5,1,2]"])
print('Using config:')
pprint.pprint(cfg)
# model = "D:/hoseok/project/pytorch-faster-rcnn/output/res101/visual_genome_train_diff/FRCNN/res101_faster_rcnn_iter_1200000.pth"
# filename = os.path.splitext(os.path.basename(model))[0]
# if has model, get the name from it
# if does not, then just use the initialization 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'
set_cfgs = [
'ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]',
'TRAIN.STEPSIZE', '[40000]'
]
imdb_name = 'voc_2007_trainval'
imdbval_name = 'voc_2007_test'
max_iters = 20000
net = 'vgg16'
tag = None
max_per_image = 100
weight = '/mnt/data//tf-faster-rcnn-master/data/imagenet_weights/vgg16.ckpt'
if cfg_file is not None:
cfg_from_file(cfg_file)
if set_cfgs is not None:
cfg_from_list(set_cfgs)
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# train set
imdb, roidb = combined_roidb(imdb_name)
print('{:d} roidb entries'.format(len(roidb)))
# output directory where the models are saved
output_dir = get_output_dir(imdb, tag)
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
= run_LRP_HAI(sess, net, blobs, timers, 'test', cfg.LRP_HAI_TEST.BETA,
im_idx, alpha=cfg.LRP_HAI.ALPHA)
return scores, pred_bboxes, timers, _
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:')
# pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the initialization 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
from datasets.factory import get_imdb
from model.test import test_net
import os
# load model for detection
net = vgg16()
net.create_architecture("TEST", tag='default')
net.build_drl_rpn_network(False)
# load database of images
imdb_name = 'voc_2007_test'
imdb = get_imdb(imdb_name)
# Set class names in config file based on IMDB
class_names = imdb.classes
cfg_from_list(['CLASS_NAMES', [class_names]])
# Update config depending on if class-specific history used or not
cfg_from_list(['DRL_RPN.USE_POST',
False]) # THROWS AN ERROR IF SET TO TRUE, WHY ?
# Specify if run drl-RPN in auto mode or a fix number of iterations
cfg_from_list(['DRL_RPN_TEST.NBR_FIX', 0])
# Specify if run drl-RPN in auto mode or a fix number of iterations
cfg_from_list(['DIMS_TIME', 4])
# test the network
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
sys.exit(1)
args = parser.parse_args()
return args
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:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the initialization 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
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()
