train_net(args.network, args.train_path, args.num_class, args.batch_size, args.data_shape, [args.mean_r, args.mean_g, args.mean_b], args.resume, args.finetune, args.pretrained, args.epoch, args.prefix, ctx, args.begin_epoch, args.end_epoch, args.frequent, args.learning_rate, args.momentum, args.weight_decay, args.lr_refactor_step, args.lr_refactor_ratio, val_path=args.val_path, num_example=args.num_example, class_names=class_names, label_pad_width=args.label_width, freeze_layer_pattern=args.freeze_pattern, iter_monitor=args.monitor, monitor_pattern=args.monitor_pattern, log_file=args.log_file, nms_thresh=args.nms_thresh, force_nms=args.force_nms, ovp_thresh=args.overlap_thresh, use_difficult=args.use_difficult, voc07_metric=args.use_voc07_metric)
type=float, default=0.9, help='ratio to refactor learning rate') parser.add_argument('--log', dest='log_file', type=str, default="train.log", help='save training log to file') parser.add_argument( '--monitor', dest='monitor', type=int, default=0, help='log network parameters every N iters if larger than 0') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')] ctx = mx.cpu() if not ctx else ctx train_net(args.network, args.dataset, args.image_set, args.year, args.devkit_path, args.batch_size, args.data_shape, [args.mean_r, args.mean_g, args.mean_b], args.resume, args.finetune, args.pretrained, args.epoch, args.prefix, ctx, args.begin_epoch, args.end_epoch, args.frequent, args.learning_rate, args.momentum, args.weight_decay, args.val_image_set, args.val_year, args.lr_refactor_epoch, args.lr_refactor_ratio, args.monitor, args.log_file)
help='weight decay') parser.add_argument('--mean-r', dest='mean_r', type=float, default=123, help='red mean value') parser.add_argument('--mean-g', dest='mean_g', type=float, default=117, help='green mean value') parser.add_argument('--mean-b', dest='mean_b', type=float, default=104, help='blue mean value') parser.add_argument('--lr-epoch', dest='lr_refactor_epoch', type=int, default=50, help='refactor learning rate every N epoch') parser.add_argument('--lr-ratio', dest='lr_refactor_ratio', type=float, default=0.9, help='ratio to refactor learning rate') parser.add_argument('--log', dest='log_file', type=str, default="train.log", help='save training log to file') parser.add_argument('--monitor', dest='monitor', type=int, default=0, help='log network parameters every N iters if larger than 0') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')] ctx = mx.cpu() if not ctx else ctx train_net(args.network, args.dataset, args.image_set, args.year, args.devkit_path, args.batch_size, args.data_shape, [args.mean_r, args.mean_g, args.mean_b], args.resume, args.finetune, args.pretrained, args.epoch, args.prefix, ctx, args.begin_epoch, args.end_epoch, args.frequent, args.learning_rate, args.momentum, args.weight_decay, args.val_image_set, args.val_year, args.lr_refactor_epoch, args.lr_refactor_ratio, args.monitor, args.log_file)
return class_names if __name__ == '__main__': args = parse_args() # context list ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()] ctx = [mx.cpu()] if not ctx else ctx # class names if applicable class_names = parse_class_names(args) # start training train_net(args.network, args.train_path, args.num_class, args.batch_size, args.data_shape, [args.mean_r, args.mean_g, args.mean_b], args.resume, args.finetune, args.pretrained, args.epoch, args.prefix, ctx, args.begin_epoch, args.end_epoch, args.frequent, args.learning_rate, args.momentum, args.weight_decay, args.lr_refactor_step, args.lr_refactor_ratio, val_path=args.val_path, num_example=args.num_example, class_names=class_names, label_pad_width=args.label_width, freeze_layer_pattern=args.freeze_pattern, iter_monitor=args.monitor, monitor_pattern=args.monitor_pattern, log_file=args.log_file, nms_thresh=args.nms_thresh, force_nms=args.force_nms, ovp_thresh=args.overlap_thresh, use_difficult=args.use_difficult, voc07_metric=args.use_voc07_metric)
from train.train_net import train_net #import tools.find_mxnet import mxnet as mx import os import sys import opt if __name__ == '__main__': train_net()