In　clinical　practice,　automatic　polyp　segmentation　from　colonoscopy　images　is　an　effective　assistant　manner　in　the　early　detection　and　prevention　of　colorectal　cancer.　This　paper　proposed　a　new　deep　model　for　accurate　polyp　segmentation　based　on　an　encoder-decoder　framework.　ResNet50　is　adopted　as　the　encoder,　and　three　functional　modules　are　introduced　to　improve　the　performance.　Firstly,　a　hybrid　channel-spatial　attention　module　is　introduced　to　reweight　the　encoder　features　spatially　and　channel-wise,　enhancing　the　critical　features　for　the　segmentation　task　while　suppressing　irrelevant　ones.　Secondly,　a　global　context　pyramid　feature　extraction　module　and　a　series　of　global　context　flows　are　proposed　to　extract　and　deliver　the　global　context　information.　The　former　captures　the　multi-scale　and　multi-receptive-field　global　context　information,　while　the　latter　explicitly　transmits　the　global　context　information　to　each　decoder　level.　Finally,　a　feature　fusion　module　is　designed　to　effectively　incorporate　the　high-level　features,　low-level　features,　and　global　context　information,　considering　the　gaps　between　different　features.　These　modules　help　the　model　fully　exploit　the　global　context　information　to　deduce　the　complete　polyp　regions.　Extensive　experiments　are　conducted　on　five　public　colorectal　polyp　datasets.　The　results　demonstrate　that　the　proposed　network　has　powerful　learning　and　generalization　capability,　significantly　improving　segmentation　accuracy　and　outperforming　state-of-the-art　methods.