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):