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151 lines
5.2 KiB
151 lines
5.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "CropOp.h"
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#include "hl_base.h"
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namespace paddle {
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__global__ void KeCrop(real* outputs,
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const real* inputs,
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int inC,
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int inH,
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int inW,
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int cropC,
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int cropH,
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int cropW,
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int outC,
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int outH,
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int outW,
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int nthreads) {
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const int idx = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx < nthreads) {
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const int w = idx % outW;
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const int h = (idx / outW) % outH;
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const int c = (idx / outW / outH) % outC;
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const int n = idx / outW / outH / outC;
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const int off = ((n * inC + c + cropC) * inH + h + cropH) * inW + cropW + w;
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outputs[idx] = inputs[off];
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}
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}
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template <>
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void Crop<DEVICE_TYPE_GPU>(real* outputs,
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const real* inputs,
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const TensorShape inShape,
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const TensorShape outShape,
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const FuncConfig& conf) {
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std::vector<uint32_t> crop_corner =
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conf.get<std::vector<uint32_t>>("crop_corner");
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int cropC = crop_corner[1];
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int cropH = crop_corner[2];
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int cropW = crop_corner[3];
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int num = inShape[0];
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int inC = inShape[1];
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int inH = inShape[2];
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int inW = inShape[3];
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int outC = outShape[1];
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int outH = outShape[2];
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int outW = outShape[3];
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size_t nth = num * outC * outH * outW;
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int blockSize = 1024;
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int gridSize = (nth + blockSize - 1) / blockSize;
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KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(outputs,
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inputs,
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inC,
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inH,
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inW,
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cropC,
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cropH,
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cropW,
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outC,
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outH,
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outW,
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nth);
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CHECK_SYNC("Crop");
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}
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__global__ void KeCropDiff(const real* inGrad,
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real* outGrad,
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int inC,
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int inH,
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int inW,
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int cropC,
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int cropH,
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int cropW,
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int outC,
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int outH,
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int outW,
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int nthreads) {
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const int idx = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx < nthreads) {
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const int w = idx % inW;
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const int h = (idx / inW) % inH;
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const int c = (idx / inW / inH) % inC;
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const int n = idx / inW / inH / inC;
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const int off =
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((n * outC + c + cropC) * outH + h + cropH) * outW + cropW + w;
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outGrad[off] += inGrad[idx];
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}
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}
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template <>
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void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
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real* outGrad,
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const TensorShape inShape,
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const TensorShape outShape,
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const FuncConfig& conf) {
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std::vector<uint32_t> crop_corner =
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conf.get<std::vector<uint32_t>>("crop_corner");
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int cropC = crop_corner[1];
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int cropH = crop_corner[2];
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int cropW = crop_corner[3];
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int num = outShape[0];
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int outC = outShape[1];
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int outH = outShape[2];
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int outW = outShape[3];
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int inC = inShape[1];
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int inH = inShape[2];
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int inW = inShape[3];
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size_t nth = num * inC * inH * inW;
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int blockSize = 1024;
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int gridSize = (nth + blockSize - 1) / blockSize;
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KeCropDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(inGrad,
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outGrad,
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inC,
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inH,
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inW,
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cropC,
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cropH,
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cropW,
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outC,
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outH,
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outW,
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nth);
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CHECK_SYNC("CropGrad");
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}
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} // namespace paddle
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