def load_tf_model():
# load config file
sttime = time.time()
cfg.TEST.checkpoints_path = './ctpn/checkpoints'
# init session
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print('Loading network {:s}... '.format("VGGnet_test"))
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
print('Restoring from {}...'.format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
logging.info("加载tf模型", time.time() - sttime)
return sess, net
def load_tf_model(GPU_MEM_USAGE):
# load config file
cfg.TEST.checkpoints_path = './ctpn/checkpoints'
# init session
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.TEST.RUN_ON
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=GPU_MEM_USAGE)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print('Loading network {:s}... '.format("VGGnet_test"))
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
try:
print('Restoring from {}...'.format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
return sess, net
def __init__(self):
cfg_from_file(os.getcwd() + '/ctpn/ctpn/text.yml')
# init session
self.config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=self.config)
# load network
self.net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state("ctpn/checkpoints/")
# ckpt=tf.train.get_checkpoint_state("output/ctpn_end2end/voc_2007_trainval/")
print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(self.sess, ckpt.model_checkpoint_path)
print('done', end=' ')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
print(' done.')
def __init__(self, sess):
super(Detect, self).__init__()
cfg_from_file('ctpn/ctpn/text.yml')
# init session
# config = tf.ConfigProto(allow_soft_placement=True)
# sess = tf.Session(config=config)
# sess = tf.Session()
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")))
saver = tf.train.Saver()
ctpn_model_path = 'ctpn/checkpoints/VGGnet_fast_rcnn_iter_50000.ckpt'
print('Restoring from {}...'.format(ctpn_model_path))
saver.restore(sess, ctpn_model_path)
print('Done\n')
self.sess = sess
self.net = net
def __init__(self,first = True):
if not first:
tf.get_variable_scope().reuse_variables()
# cfg_from_file(os.getcwd() + '/ctpn/ctpn/text.yml')
cfg_from_file(os.path.join(os.path.dirname(os.path.abspath(__file__)),'text.yml'))
# init session
self.config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=self.config)
# load network
self.net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
#ckpt = tf.train.get_checkpoint_state("ctpn/checkpoints/")
ckpt = tf.train.get_checkpoint_state("G:/DeepLearningProjects/Web_SceneRecognition/ScenceRecognition_master/ctpn/checkpoints/")
# ckpt=tf.train.get_checkpoint_state("output/ctpn_end2end/voc_2007_trainval/")
print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(self.sess, ckpt.model_checkpoint_path)
print('done', end=' ')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
print(' done.')
from ctpn.lib.fast_rcnn.train import get_training_roidb, train_net
from ctpn.lib.fast_rcnn.config import cfg_from_file, get_output_dir, get_log_dir
from ctpn.lib.datasets.factory import get_imdb
from ctpn.lib.networks.factory import get_network
from ctpn.lib.fast_rcnn.config import cfg
if __name__ == '__main__':
cfg_from_file('text.yml')
print('Using config:')
pprint.pprint(cfg)
imdb = get_imdb('voc_2007_trainval')
print('Loaded dataset `{:s}` for training'.format(imdb.name))
roidb = get_training_roidb(imdb)
output_dir = get_output_dir(imdb, None)
log_dir = get_log_dir(imdb)
print('Output will be saved to `{:s}`'.format(output_dir))
print('Logs will be saved to `{:s}`'.format(log_dir))
device_name = '/gpu:0'
print(device_name)
network = get_network('VGGnet_train')
train_net(network, imdb, roidb,
output_dir=output_dir,
log_dir=log_dir,
pretrained_model=os.path.join('../data/pretrain_model/VGG_imagenet.npy'),
max_iters=int(cfg.TRAIN.max_steps),
restore=bool(int(cfg.TRAIN.restore)))
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
if __name__ == '__main__':
if os.path.exists("data/results/"):
shutil.rmtree("data/results/")
os.makedirs("data/results/")
cfg_from_file('ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
print('Restoring from {}...'.format(ckpt.model_checkpoint_path),
end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
