{"action":"created","comment":{"url":"https://api.github.com/repos/baidu/Paddle/pulls/comments/85761499","pull_request_review_id":6436910,"id":85761499,"diff_hunk":"@@ -532,6 +532,138 @@ void hl_CMRNorm_backward(size_t frameCnt, const real* inV,\n   CHECK_SYNC(\"hl_CMRNorm_backward\");\n }\n \n+__global__ void KeBilinearInterpFw(const size_t nthreads,\n+                                   const real* in,\n+                                   const size_t inImgH,\n+                                   const size_t inImgW,\n+                                   const size_t inputH,\n+                                   const size_t inputW,\n+                                   real* out,\n+                                   const size_t outImgH,\n+                                   const size_t outImgW,\n+                                   const size_t outputH,\n+                                   const size_t outputW,\n+                                   const size_t numChannels,\n+                                   const real ratioH,\n+                                   const real ratioW) {\n+  int tid = blockIdx.x * blockDim.x + threadIdx.x;\n+  if(tid < nthreads) {\n+    int outIdH = tid / (outputW / numChannels);\n+    int outIdW = tid % (outputW / numChannels);\n+\n+    int inIdH = ratioH * (outIdW / outImgW);\n+    int hId = (inIdH < inImgH - 1) ? 1 : 0;\n+    real hlambda = ratioH * (outIdW / outImgW) - inIdH;\n+\n+    int inIdW = ratioW * (tid % outImgW);\n+    int wId = (inIdW < inImgW - 1) ? 1 : 0;\n+    real wlambda = ratioW * (tid % outImgW) - inIdW;\n+\n+    const real* inPos = &in[outIdH * inputW + inIdH * inImgW + inIdW];\n+    real* outPos = &out[outIdH * outputW + outIdW];\n+    for (int c = 0; c < numChannels; ++c) {\n+      // bilinear interpolation\n+      outPos[0] = (1.f - hlambda) *\n+        ((1.f - wlambda) * inPos[0] + wlambda * inPos[wId]) + \n+        hlambda * ((1.f - wlambda) * inPos[hId * inImgW] +\n+        wlambda * inPos[hId * inImgW + wId]);\n+      inPos += inImgH * inImgW;\n+      outPos += outImgH * outImgW;\n+    }\n+  }\n+}\n+\n+void hl_bilinear_forward(const real* inData,\n+                         const size_t inImgH,\n+                         const size_t inImgW,\n+                         const size_t inputH,\n+                         const size_t inputW,\n+                         real* outData,\n+                         const size_t outImgH,\n+                         const size_t outImgW,\n+                         const size_t outputH,\n+                         const size_t outputW,\n+                         const size_t numChannels) {\n+  int threadNum = outputH * outImgH * outImgW;\n+  int blocks = (threadNum + 1024 - 1) / 1024;\n+\n+  real ratioH = (outImgH > 1) ?\n+      static_cast<float>(inImgH - 1) / (outImgH - 1) : 0.f;\n+  real ratioW = (outImgW > 1) ?\n+      static_cast<float>(inImgW - 1) / (outImgW - 1) : 0.f;\n+\n+  KeBilinearInterpFw<<< blocks, 1024, 0, STREAM_DEFAULT>>>(\n+    threadNum, inData, inImgH, inImgW, inputH, inputW, outData,\n+    outImgH, outImgW, outputH, outputW, numChannels, ratioH, ratioW);\n+  CHECK_SYNC(\"hl_bilinear_forward failed\");\n+}\n+\n+__global__ void KeBilinearInterpBw(const size_t nthreads,\n+                                   real* in,\n+                                   const size_t inImgH,\n+                                   const size_t inImgW,\n+                                   const size_t inputH,\n+                                   const size_t inputW,\n+                                   const real* out,\n+                                   const size_t outImgH,\n+                                   const size_t outImgW,\n+                                   const size_t outputH,\n+                                   const size_t outputW,\n+                                   const size_t numChannels,\n+                                   const real ratioH,\n+                                   const real ratioW) {\n+  int tid = blockIdx.x * blockDim.x + threadIdx.x;\n+\n+  if(tid < nthreads) {\n+    int outIdH = tid / (outputW / numChannels);\n+    int outIdW = tid % (outputW / numChannels);\n+\n+    int inIdH = ratioH * (outIdW / outImgW);\n+    int hId = (inIdH < inImgH - 1) ? 1 : 0;\n+    real hlambda = ratioH * (outIdW / outImgW) - inIdH;\n+\n+    int inIdW = ratioW * (tid % outImgW);\n+    int wId = (inIdW < inImgW - 1) ? 1 : 0;\n+    real wlambda = ratioW * (tid % outImgW) - inIdW;\n+\n+    const real* outPos = &out[outIdH * outputW + outIdW];\n+    real* inPos = &in[outIdH * inputW + inIdH * inImgW + inIdW];\n+    for (int c = 0; c < numChannels; ++c) {","path":"paddle/cuda/src/hl_cuda_cnn.cu","position":109,"original_position":109,"commit_id":"460320a41c24c6f1150bd4d9a9e499474ca28935","original_commit_id":"460320a41c24c6f1150bd4d9a9e499474ca28935","user":{"login":"qingqing01","id":7845005,"avatar_url":"https://avatars.githubusercontent.com/u/7845005?v=3","gravatar_id":"","url":"https://api.github.com/users/qingqing01","html_url":"https://github.com/qingqing01","followers_url":"https://api.github.com/users/qingqing01/followers","following_url":"https://api.github.com/users/qingqing01/following{/other_user}","gists_url":"https://api.github.com/users/qingqing01/gists{/gist_id}","starred_url":"https://api.github.com/users/qingqing01/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/qingqing01/subscriptions","organizations_url":"https://api.github.com/users/qingqing01/orgs","repos_url":"https://api.github.com/users/qingqing01/repos","events_url":"https://api.github.com/users/qingqing01/events{/privacy}","received_events_url":"https://api.github.com/users/qingqing01/received_events","type":"User","site_admin":false},"body":"同forward， channels之间也是并行的吧，可否也展开并行计算，具体性能可以测下。","created_at":"2016-10-31T15:22:33Z","updated_at":"2016-10-31T15:22:49Z","html_url":"https://github.com/baidu/Paddle/pull/287#discussion_r85761499","pull_request_url":"https://api.github.com/repos/baidu/Paddle/pulls/287","_links":{"self":{"href":"https://api.github.com/repos/baidu/Paddle/pulls/comments/85761499"},"html":{"href":"https://github.com/baidu/Paddle/pull/287#discussion_r85761499"},"pull_request":{"href":"https://api.github.com/repos/baidu/Paddle/pulls/287"}}},"pull_request":{"url":"https://api.github.com/repos/baidu/Paddle/pulls/287","id":91507975,"html_url":"https://github.com/baidu/Paddle/pull/287","diff_url":"https://github.com/baidu/Paddle/pull/287.diff","patch_url":"https://github.com/baidu/Paddle/pull/287.patch","issue_url":"https://api.github.com/repos/baidu/Paddle/issues/287","number":287,"state":"open","locked":false,"title":"Add bilinear interpolation layer","user":{"login":"gangliao","id":3071933,"avatar_url":"https://avatars.githubusercontent.com/u/3071933?v=3","gravatar_id":"","url":"https://api.github.com/users/gangliao","html_url":"https://github.com/gangliao","followers_url":"https://api.github.com/users/gangliao/followers","following_url":"https://api.github.com/users/gangliao/following{/other_user}","gists_url":"https://api.github.com/users/gangliao/gists{/gist_id}","starred_url":"https://api.github.com/users/gangliao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gangliao/subscriptions","organizations_url":"https://api.github.com/users/gangliao/orgs","repos_url":"https://api.github.com/users/gangliao/repos","events_url":"https://api.github.com/users/gangliao/events{/privacy}","received_events_url":"https://api.github.com/users/gangliao/received_events","type":"User","site_admin":false},"body":"Support bi-linear interpolation operation. 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