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@ -138,4 +138,84 @@ TEST(Im2ColFunctor, GPU) { TestIm2ColFunctor<DEVICE_TYPE_GPU, float>(); }
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#endif
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template <class T>
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void TestIm2ColMobileFunctor() {
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for (size_t channels : {1, 5, 32}) {
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for (size_t inputHeight : {5, 33, 100}) {
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for (size_t inputWidth : {5, 32, 96}) {
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for (size_t filterHeight : {1, 5}) {
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for (size_t filterWidth : {3, 7}) {
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for (size_t stride : {1, 2}) {
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for (size_t padding : {0, 1}) {
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for (size_t dilation : {1 /*, 3*/}) {
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size_t filterSizeH = (filterHeight - 1) * dilation + 1;
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size_t filterSizeW = (filterWidth - 1) * dilation + 1;
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if (inputHeight + 2 * padding < filterSizeH ||
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inputWidth + 2 * padding < filterSizeW)
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break;
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if (padding >= filterSizeH || padding >= filterSizeW) break;
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size_t outputHeight =
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(inputHeight - filterSizeH + 2 * padding) / stride + 1;
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size_t outputWidth =
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(inputWidth - filterSizeW + 2 * padding) / stride + 1;
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TensorShape imShape =
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TensorShape({channels, inputHeight, inputWidth});
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TensorShape colShape1 = TensorShape({channels,
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filterHeight,
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filterWidth,
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outputHeight,
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outputWidth});
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size_t height = channels * filterHeight * filterWidth;
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size_t width = outputHeight * outputWidth;
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VectorPtr input1 =
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Vector::create(imShape.getElements(), false);
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VectorPtr input2 =
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Vector::create(imShape.getElements(), false);
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MatrixPtr output1 =
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Matrix::create(height, width, false, false);
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MatrixPtr output2 =
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Matrix::create(height, width, false, false);
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input1->uniform(0.001, 1);
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input2->copyFrom(*input1);
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Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, T> im2Col1;
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Im2ColMobileFunctor<T> im2Col2;
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im2Col1(input1->getData(),
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imShape,
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output1->getData(),
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colShape1,
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stride,
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stride,
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padding,
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padding,
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dilation,
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dilation);
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im2Col2(input2->getData(),
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imShape,
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output2->getData(),
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colShape1,
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stride,
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stride,
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padding,
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padding,
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0,
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height,
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0,
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width);
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autotest::TensorCheckEqual(*output1, *output2);
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}
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}
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}
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}
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}
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}
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}
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}
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}
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TEST(Im2ColFunctor, Mobile) { TestIm2ColMobileFunctor<float>(); }
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} // namespace paddle
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