net = FPN(
FPN_Resnet(resnet_type=101,
feat_strdie=(4, 8, 16, 32, 64),
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS), imdb.classes)
cfg.TRAIN.INIT_WAY = 'resnet'
else:
raise NotImplementedError
learn_dict = {
'disp_interval': cfg.TRAIN.DISPLAY,
'use_tensorboard': cfg.TRAIN.USE_TENSORBOARD,
'use_valid': cfg.TRAIN.USE_VALID,
'evaluate': cfg.TRAIN.EVALUATE,
'begin_eval_point': cfg.TRAIN.BEGIN_EVAL_POINT,
'save_point_interval': cfg.TRAIN.SAVE_POINT_INTERVAL,
'lr_decay_steps': cfg.TRAIN.STEPSIZE
}
resume = args.resume
train_net(net,
imdb,
valimdb,
roidb,
valroidb,
model_dir,
learn_dict,
resume,
pretrained_model=args.weight,
max_iters=args.max_iters)
tb_dir = get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
_, valroidb = combined_roidb(args.imdbval_name)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
# load network
if args.net == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res152':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152)
else:
raise NotImplementedError
train_net(net,
imdb,
roidb,
valroidb,
output_dir,
tb_dir,
pretrained_model=args.weight,
max_iters=args.max_iters)
print(im)
'''
weights_filename = 'default'
output_dir = get_output_dir(imdb, weights_filename)
print('Output will be saved to `{:s}`'.format(output_dir))
# tb_dir = get_output_tb_dir(imdb,weights_filename)
net = vgg16()
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
valimdb, valroidb = get_roidb('voc_2007_test') #获取测试集数据
#测试验证集数据
#im=cv2.imread(valroidb[6000]['image']
#print(im)
print('{:d} validatio n roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
#截止目前测试均通过
#在源码基础上去除了很多冗余的东西。。但开始报错了
train_net(net,
imdb,
roidb,
valroidb,
output_dir,
pretrained_model=pretrained_model,
max_iters=40000)
# load network