def get_resnet101_train(args):
from net.symbol_resnet import get_resnet_train
if not args.pretrained:
args.pretrained = 'model/resnet-101-0000.params'
if not args.save_prefix:
args.save_prefix = 'model/resnet101'
args.img_pixel_means = (0.0, 0.0, 0.0)
args.img_pixel_stds = (1.0, 1.0, 1.0)
args.net_fixed_params = ['conv0', 'stage1', 'gamma', 'beta']
args.rpn_feat_stride = 16
args.rcnn_feat_stride = 16
args.rcnn_pooled_size = (14, 14)
return get_resnet_train(anchor_scales=args.rpn_anchor_scales,
anchor_ratios=args.rpn_anchor_ratios,
rpn_feature_stride=args.rpn_feat_stride,
rpn_pre_topk=args.rpn_pre_nms_topk,
rpn_post_topk=args.rpn_post_nms_topk,
rpn_nms_thresh=args.rpn_nms_thresh,
rpn_min_size=args.rpn_min_size,
rpn_batch_rois=args.rpn_batch_rois,
num_classes=args.rcnn_num_classes,
rcnn_feature_stride=args.rcnn_feat_stride,
rcnn_pooled_size=args.rcnn_pooled_size,
rcnn_batch_size=args.rcnn_batch_size,
rcnn_batch_rois=args.rcnn_batch_rois,
rcnn_fg_fraction=args.rcnn_fg_fraction,
rcnn_fg_overlap=args.rcnn_fg_overlap,
rcnn_bbox_stds=args.rcnn_bbox_stds,
units=(3, 4, 23, 3),
filter_list=(256, 512, 1024, 2048))
def get_resnet101_train(args):
from net.symbol_resnet import get_resnet_train
if not args.pretrained:
args.pretrained = 'model/resnet-101-0000.params'
if not args.save_prefix:
args.save_prefix = 'model/resnet101'
args.img_pixel_means = (0.0, 0.0, 0.0)
args.img_pixel_stds = (1.0, 1.0, 1.0)
args.net_fixed_params = ['conv0', 'stage1', 'gamma', 'beta']
args.rpn_feat_stride = 16
args.rcnn_feat_stride = 16
args.rcnn_pooled_size = (14, 14)
return get_resnet_train(anchor_scales=args.rpn_anchor_scales, anchor_ratios=args.rpn_anchor_ratios,
rpn_feature_stride=args.rpn_feat_stride, rpn_pre_topk=args.rpn_pre_nms_topk,
rpn_post_topk=args.rpn_post_nms_topk, rpn_nms_thresh=args.rpn_nms_thresh,
rpn_min_size=args.rpn_min_size, rpn_batch_rois=args.rpn_batch_rois,
num_classes=args.rcnn_num_classes, rcnn_feature_stride=args.rcnn_feat_stride,
rcnn_pooled_size=args.rcnn_pooled_size, rcnn_batch_size=args.rcnn_batch_size,
rcnn_batch_rois=args.rcnn_batch_rois, rcnn_fg_fraction=args.rcnn_fg_fraction,
rcnn_fg_overlap=args.rcnn_fg_overlap, rcnn_bbox_stds=args.rcnn_bbox_stds,
units=(3, 4, 23, 3), filter_list=(256, 512, 1024, 2048))