{"push_id":2724223330,"size":4,"distinct_size":4,"ref":"refs/heads/export-D8852180","head":"b5b13b74a01764a415a2ede1dd5afcfa8d69e73f","before":"9c886b766be2a8b94812a0520112ae9babe3b4fe","commits":[{"sha":"ad74006ffa22c79285e21c7777e9d386607ebbea","author":{"name":"Will Feng","email":"1afd6515b09d0b3eb7e8e4008edd2316b575fbe3@fb.com"},"message":"Pass THDRequest as void* pointer to THDRequest_free (#9398)\n\nSummary:\nThis fixes https://github.com/pytorch/pytorch/issues/9054.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9398\n\nReviewed By: ezyang\n\nDifferential Revision: D8827778\n\nPulled By: yf225\n\nfbshipit-source-id: 862287802cb69c6ac71ff4df19cadb89b1face1d","distinct":true,"url":"https://api.github.com/repos/hl475/pytorch/commits/ad74006ffa22c79285e21c7777e9d386607ebbea"},{"sha":"9235ff53f12ed3e049f9df9eb6f74d6f5a161b71","author":{"name":"Viswanath Sivakumar","email":"7b259d596e4fbcfd0ecf4f86558b31b169a35bb9@fb.com"},"message":"Clip horizontal bounding boxes during rotated detection for backward compatibility (#9403)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9403\n\nIn BBoxTransform and GenerateProposal ops, clip_boxes makes sure the bbox fits\nwithin the images. For rotated boxes, this doesn't always make sense as there\ncould be multiple ways to clip a rotated box within an image boundary.\nMoreover, clipping to a horizontal box means we leave out pixels of interest\npotentially. Therefore, we clip only boxes with angle almost equal to 0 (with a\nspecified `angle_thresh` tolerance).\n\nReviewed By: pjh5\n\nDifferential Revision: D8828588\n\nfbshipit-source-id: 39c1eafdb5d39d383780faa0a47e76149145e50c","distinct":true,"url":"https://api.github.com/repos/hl475/pytorch/commits/9235ff53f12ed3e049f9df9eb6f74d6f5a161b71"},{"sha":"e8b8c3895e39a269fdb4d7f583db22ac7fdf3413","author":{"name":"Gu, Jinghui","email":"87e9f383c80b541ce865cadee2cc85ad05fa7602@intel.com"},"message":"Enable Conv fusion optimizations in optimizeForIdeep (#9255)\n\nSummary:\nEnable fusion for IDEEP in optimizeForIdeep\nincluding Conv+ReLU, Conv+Sum, Conv+Sum+ReLU, Conv+BN\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9255\n\nReviewed By: bddppq\n\nDifferential Revision: D8809030\n\nPulled By: yinghai\n\nfbshipit-source-id: af30bad3b96cb965bd26a4dfa810370faec4bb88","distinct":true,"url":"https://api.github.com/repos/hl475/pytorch/commits/e8b8c3895e39a269fdb4d7f583db22ac7fdf3413"},{"sha":"b5b13b74a01764a415a2ede1dd5afcfa8d69e73f","author":{"name":"Huamin Li","email":"bcde2bd88fb2f2ec5fe270dda3e68159f13b3d8f@fb.com"},"message":"Add Support for count_include_pad in AveragePool in Caffe2 ONNX Backend (#9458)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9458\n\nThe goal is to support count_include_pad in Caffe2 ONNX backend. This commit contains the first step - support 4-D tensor cases.\nAveragePool with count_include_pad can be expressed as PadImage + AveragePool.\n\nDifferential Revision: D8852180\n\nfbshipit-source-id: 0deb864c11b55a3a1c51f94e5bacddb72727147e","distinct":true,"url":"https://api.github.com/repos/hl475/pytorch/commits/b5b13b74a01764a415a2ede1dd5afcfa8d69e73f"}]}
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