init - 初始化项目

doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown

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+# How to schedule your network for Halide backend {#tutorial_dnn_halide_scheduling}
+
+@tableofcontents
+
+@prev_tutorial{tutorial_dnn_halide}
+@next_tutorial{tutorial_dnn_android}
+
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 3.3 |
+
+## Introduction
+Halide code is the same for every device we use. But for achieving the satisfied
+efficiency we should schedule computations properly. In this tutorial we describe
+the ways to schedule your networks using Halide backend in OpenCV deep learning module.
+
+For better understanding of Halide scheduling you might want to read tutorials @ http://halide-lang.org/tutorials.
+
+If it's your first meeting with Halide in OpenCV, we recommend to start from @ref tutorial_dnn_halide.
+
+## Configuration files
+You can schedule computations of Halide pipeline by writing textual configuration files.
+It means that you can easily vectorize, parallelize and manage loops order of
+layers computation. Pass path to file with scheduling directives for specific
+device into ```cv::dnn::Net::setHalideScheduler``` before the first ```cv::dnn::Net::forward``` call.
+
+Scheduling configuration files represented as YAML files where each node is a
+scheduled function or a scheduling directive.
+@code
+relu1:
+  reorder: [x, c, y]
+  split: { y: 2, c: 8 }
+  parallel: [yo, co]
+  unroll: yi
+  vectorize: { x: 4 }
+conv1_constant_exterior:
+  compute_at: { relu1: yi }
+@endcode
+
+Considered use variables `n` for batch dimension, `c` for channels,
+`y` for rows and `x` for columns. For variables after split are used names
+with the same prefix but `o` and `i` suffixes for outer and inner variables
+correspondingly. In example, for variable `x` in range `[0, 10)` directive
+`split: { x: 2 }` gives new ones `xo` in range `[0, 5)` and `xi` in range `[0, 2)`.
+Variable name `x` is no longer available in the same scheduling node.
+
+You can find scheduling examples at [opencv_extra/testdata/dnn](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
+and use it for schedule your networks.
+
+## Layers fusing
+Thanks to layers fusing we can schedule only the top layers of fused sets.
+Because for every output value we use the fused formula.
+In example, if you have three layers Convolution + Scale + ReLU one by one,
+@code
+conv(x, y, c, n) = sum(...) + bias(c);
+scale(x, y, c, n) = conv(x, y, c, n) * weights(c);
+relu(x, y, c, n) = max(scale(x, y, c, n), 0);
+@endcode
+
+fused function is something like
+@code
+relu(x, y, c, n) = max((sum(...) + bias(c)) * weights(c), 0);
+@endcode
+
+So only function called `relu` require scheduling.
+
+## Scheduling patterns
+Sometimes networks built using blocked structure that means some layer are
+identical or quite similar. If you want to apply the same scheduling for
+different layers accurate to tiling or vectorization factors, define scheduling
+patterns in section `patterns` at the beginning of scheduling file.
+Also, your patters may use some parametric variables.
+@code
+# At the beginning of the file
+patterns:
+  fully_connected:
+    split: { c: c_split }
+    fuse: { src: [x, y, co], dst: block }
+    parallel: block
+    vectorize: { ci: c_split }
+# Somewhere below
+fc8:
+  pattern: fully_connected
+  params: { c_split: 8 }
+@endcode
+
+## Automatic scheduling
+You can let DNN to schedule layers automatically. Just skip call of ```cv::dnn::Net::setHalideScheduler```. Sometimes it might be even more efficient than manual scheduling.
+But if specific layers require be scheduled manually, you would be able to
+mix both manual and automatic scheduling ways. Write scheduling file
+and skip layers that you want to be scheduled automatically.