Crowd counting aims to estimate the number of people in images. Although promising progresses have been made with the prevalence of deep Convolutional Neural Networks, there still remains a challenging task due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a learnable spatial attention module which can get the spatial relations to diminish the negative impact of backgrounds. Besides, a dense hybrid dilated convolution module is also brought up to preserve information derived from varied scales. With these two modules, our network can deal with the problem caused by scale variance and background interference. To demonstrate the effectiveness of our method, we compare it with state-of-the-art algorithms on three representative crowd counting benchmarks (ShanghaiTech UCF-QNRF,UCF_CC_50). Experimental results show that our proposed network can achieve significant improvements on all the three datasets.