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Merge pull request #11719 from alalek:update_autobuffer_api

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Alexander Alekhin
2018-07-05 10:01:15 +00:00
parent ccd2370bb7 b09a4a98d4
commit 0bb2c115aa
93 changed files with 379 additions and 351 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+1 -1
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@@ -586,7 +586,7 @@ public:
float* data_out0_ = output_->ptr<float>();
size_t rowbufsz = (size_t)karea*BLK_SIZE_CN*BLK_SIZE;
AutoBuffer<float> rowbuf0_(rowbufsz + valign);
float* rowbuf0 = alignPtr((float*)rowbuf0_, (int)(valign*sizeof(float)));
float* rowbuf0 = alignPtr(rowbuf0_.data(), (int)(valign*sizeof(float)));
// we clear the buffer once; ultimately, it lets us to avoid
// tail processing after running the unrolled/vectorized loop.
modules/dnn/src/layers/fully_connected_layer.cpp
+1 -1
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@@ -182,7 +182,7 @@ public:
size_t stripeEnd = r.end == nstripes ? total : std::min(r.end*stripeSize, total);
size_t wstep = weights->step1();
AutoBuffer<float> srcbuf(vecsize_aligned + valign);
float* sptr = alignPtr((float*)srcbuf, (int)(valign*sizeof(float)));
float* sptr = alignPtr(srcbuf.data(), (int)(valign*sizeof(float)));
for( k = vecsize; k < vecsize_aligned; k++ )
sptr[k] = 0.f;
modules/dnn/src/layers/lrn_layer.cpp
+1 -1
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@@ -211,7 +211,7 @@ public:
int k, channels = channels_, ksize = ksize_;
AutoBuffer<float> buf_((channels + ksize + 1)*2);
float* acc = (float*)buf_;
float* acc = buf_.data();
float* buf = acc + channels + ksize + 1;
for( k = 0; k <= ksize; k++ )
buf[-k-1] = buf[channels + k] = 0.f;
modules/dnn/src/torch/torch_importer.cpp
+3 -3
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@@ -370,8 +370,8 @@ struct TorchImporter
int ndims = readInt();
AutoBuffer<int64, 4> sizes(ndims);
AutoBuffer<int64, 4> steps(ndims);
THFile_readLongRaw(file, sizes, ndims);
THFile_readLongRaw(file, steps, ndims);
THFile_readLongRaw(file, sizes.data(), ndims);
THFile_readLongRaw(file, steps.data(), ndims);
long offset = readLong() - 1;
//read Storage
@@ -411,7 +411,7 @@ struct TorchImporter
}
//allocate Blob
Mat srcMat(ndims, (int*)isizes, typeTensor , storages[indexStorage].ptr() + offset*CV_ELEM_SIZE(typeTensor), (size_t*)ssteps);
Mat srcMat(ndims, isizes.data(), typeTensor , storages[indexStorage].ptr() + offset*CV_ELEM_SIZE(typeTensor), ssteps.data());
int dstType = CV_32F;
Mat blob;