weight_decay = cfg.TRAIN.WEIGHT_DECAY
disp_interval = cfg.TRAIN.DISPLAY
log_interval = cfg.TRAIN.LOG_IMAGE_ITERS
# load data
imdb = get_imdb(imdb_name)
rdl_roidb.prepare_roidb(imdb)
roidb = imdb.roidb
data_layer = RoIDataLayer(roidb, imdb.num_classes)
# load net
net = RFCN(classes=imdb.classes, debug=_DEBUG)
#init_modules = [net.rpn.conv1, net.rpn.score_conv, net.rpn.bbox_conv, net.fc6, net.fc7, net.score_fc, net.bbox_fc]
#network.weights_normal_init(init_modules, dev=0.01)
network.weights_normal_init(net, dev=0.01)
network.load_pretrained_npy(net, pretrained_model)
if resume:
pretrained_model_file = 'models/saved_model3/faster_rcnn_resnet101_20000.h5'
network.load_net(pretrained_model_file, net)
start_step = 20000
print 'Resume training...'
net.cuda()
net.train()
params = list(net.parameters())
# optimizer = torch.optim.Adam(params[-8:], lr=lr)
optimizer = torch.optim.SGD(params[8:],
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
disp_interval = cfg.TRAIN.DISPLAY log_interval = cfg.TRAIN.LOG_IMAGE_ITERS # load data imdb = get_imdb(imdb_name) rdl_roidb.prepare_roidb(imdb) roidb = imdb.roidb data_layer = RoIDataLayer(roidb, imdb.num_classes) # load net net = FasterRCNN(classes=imdb.classes, debug=_DEBUG) network.weights_normal_init(net, dev=0.01) snap_shot_model = "models/saved_model3/resnet_faster_rcnn_10000.h5" # network.load_net(snap_shot_model, net) network.load_pretrained_npy(net, pretrained_model, backbone=backbone) # model_file = '/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5' # model_file = 'models/saved_model3/faster_rcnn_60000.h5' # network.load_net(model_file, net) # exp_name = 'vgg16_02-19_13-24' # start_step = 60001 # lr /= 10. # network.weights_normal_init([net.bbox_fc, net.score_fc, net.fc6, net.fc7], dev=0.01) net.cuda() net.train() params = list(net.parameters()) # optimizer = torch.optim.Adam(params[-8:], lr=lr) optimizer = torch.optim.SGD(params[8:],
