def train_main(data_dir, model_dir, train_steps, input_yaml): cfg_from_file(input_yaml) 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='data/pretrain_model/VGG_imagenet.npy', max_iters=int(cfg.TRAIN.max_steps), restore=bool(int(cfg.TRAIN.restore)))
from lib.fast_rcnn.train import get_training_roidb, train_net from lib.fast_rcnn.config import cfg_from_file, get_output_dir, get_log_dir from lib.datasets.factory import get_imdb from lib.networks.factory import get_network from lib.fast_rcnn.config import cfg if __name__ == '__main__': cfg_from_file('ctpn/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='data/pretrain/VGG_imagenet.npy', max_iters=180000,
from lib.fast_rcnn.train import get_training_roidb, train_net from lib.fast_rcnn.config import cfg_from_file, get_output_dir, get_log_dir from lib.datasets.factory import get_imdb from lib.networks.factory import get_network from lib.fast_rcnn.config import cfg if __name__ == '__main__': cfg_from_file('ctpn/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='data/pretrain_model/VGG_imagenet.npy', max_iters=int(cfg.TRAIN.max_steps), restore=bool(int(cfg.TRAIN.restore)))