def start():
#vgg16预训练权重所在路径
setting = provider.Settings(
mean_value=[104, 117, 124],
img_dot_path=
"/media/gzs/baidu_star_2018/image/stage1/dot_den/dot/train/",
img_box_path=
"/media/gzs/baidu_star_2018/image/stage1/box_den/box/train/",
img_dot_den_path=
"/media/gzs/baidu_star_2018/image/stage1/dot_den/dot/train_den/",
img_box_den_path=
"/media/gzs/baidu_star_2018/image/stage1/box_den/box/train_den/")
main(data_args=setting, use_cuda=False, num_passes=25, lr=1e-6)
# 创建训练器
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
extra_layers=[detect_out],
update_equation=optimizer)
# 定义数据层之间的关系
feeding = {'image': 0, 'bbox': 1}
# 生成要训练的数据
reader = paddle.batch(data_provider.test(data_args, eval_file_list),
batch_size=batch_size)
# 获取测试结果
result = trainer.test(reader=reader, feeding=feeding)
# 打印模型的测试信息
print "TestCost: %f, Detection mAP=%g" % \
(result.cost, result.metrics['detection_evaluator'])
if __name__ == "__main__":
paddle.init(use_gpu=True, trainer_count=2)
# 设置数据参数
data_args = data_provider.Settings(data_dir='../data',
label_file='../data/label_list',
resize_h=cfg.IMG_HEIGHT,
resize_w=cfg.IMG_WIDTH,
mean_value=[104, 117, 124])
# 开始评估
eval(eval_file_list='../data/test.txt',
batch_size=4,
data_args=data_args,
model_path='../models/params_pass.tar.gz')
