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[DOCS] Code block update for master (#18437)

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* code-block-1

* Update Convert_Model_From_Paddle.md

* code-block force

* fix

* fix-2

* Update troubleshooting-steps.md

* code-block-2

* Update README.md
This commit is contained in:
Maciej Smyk
2023-07-11 10:43:54 +02:00
committed by GitHub
parent 900163c484
commit 0148076ed7
235 changed files with 1609 additions and 1146 deletions
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docs/ops/convolution/BinaryConvolution_1.md
+2 -1
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@@ -103,7 +103,8 @@ Computation algorithm for mode *xnor-popcount*:
2D Convolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="BinaryConvolution" ...>
<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1" mode="xnor-popcount" pad_value="0" auto_pad="explicit"/>
docs/ops/convolution/ConvolutionBackpropData_1.md
+10 -5
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@@ -20,7 +20,8 @@ ConvolutionBackpropData accepts the same set of attributes as a regular Convolut
When output shape is specified as an input tensor ``output_shape`` then it specifies only spatial dimensions. No batch or channel dimension should be passed along with spatial dimensions. If ``output_shape`` is omitted, then ``pads_begin``, ``pads_end`` or ``auto_pad`` are used to determine output spatial shape ``[O_z, O_y, O_x]`` by input spatial shape ``[I_z, I_y, I_x]`` in the following way:
.. code-block:: cpp
.. code-block:: xml
:force:
if auto_pads != None:
pads_begin[i] = 0
@@ -32,7 +33,8 @@ where ``K_i`` filter kernel dimension along spatial axis ``i``.
If ``output_shape`` is specified, ``pads_begin`` and ``pads_end`` are ignored, and ``auto_pad`` defines how to distribute padding amount around the tensor. In this case pads are determined based on the next formulas to correctly align input and output tensors:
.. code-block:: cpp
.. code-block:: xml
:force:
total_padding[i] = stride[i] * (X_i - 1) + ((K_i - 1) * dilations[i] + 1) - output_shape[i] + output_padding[i]
if auto_pads != SAME_UPPER:
@@ -119,7 +121,8 @@ If ``output_shape`` is specified, ``pads_begin`` and ``pads_end`` are ignored, a
*Example 1: 2D ConvolutionBackpropData*
.. code-block:: cpp
.. code-block:: xml
:force:
<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2" output_padding="0,0" auto_pad="explicit"/>
@@ -149,7 +152,8 @@ If ``output_shape`` is specified, ``pads_begin`` and ``pads_end`` are ignored, a
*Example 2: 2D ConvolutionBackpropData with output_padding*
.. code-block:: cpp
.. code-block:: xml
:force:
<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="0,0" pads_end="0,0" strides="3,3" output_padding="2,2" auto_pad="explicit"/>
@@ -179,7 +183,8 @@ If ``output_shape`` is specified, ``pads_begin`` and ``pads_end`` are ignored, a
*Example 3: 2D ConvolutionBackpropData with output_shape input*
.. code-block:: cpp
.. code-block:: xml
:force:
<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="1,1" output_padding="0,0" auto_pad="valid"/>
docs/ops/convolution/Convolution_1.md
+6 -3
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@@ -113,7 +113,8 @@ The receptive field in each layer is calculated using the formulas:
1D Convolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="Convolution" ...>
<data dilations="1" pads_begin="0" pads_end="0" strides="2" auto_pad="valid"/>
@@ -141,7 +142,8 @@ The receptive field in each layer is calculated using the formulas:
2D Convolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="Convolution" ...>
<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1" auto_pad="explicit"/>
@@ -171,7 +173,8 @@ The receptive field in each layer is calculated using the formulas:
3D Convolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="Convolution" ...>
<data dilations="2,2,2" pads_begin="0,0,0" pads_end="0,0,0" strides="3,3,3" auto_pad="explicit"/>
docs/ops/convolution/DeformableConvolution_1.md
+4 -2
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@@ -109,7 +109,8 @@ Where
2D DeformableConvolution (deformable_group=1)
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="DeformableConvolution" ...>
<data dilations="1,1" pads_begin="0,0" pads_end="0,0" strides="1,1" auto_pad="explicit" group="1" deformable_group="1"/>
@@ -145,7 +146,8 @@ Where
2D DeformableConvolution (deformable_group=4)
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="DeformableConvolution" ...>
<data dilations="1,1" pads_begin="0,0" pads_end="0,0" strides="1,1" auto_pad="explicit" group="1" deformable_group="4"/>
docs/ops/convolution/DeformableConvolution_8.md
+2 -1
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@@ -121,7 +121,8 @@ Where
2D DeformableConvolution (deformable_group=1)
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="DeformableConvolution" ...>
<data dilations="1,1" pads_begin="0,0" pads_end="0,0" strides="1,1" auto_pad="explicit" group="1" deformable_group="1"/>
docs/ops/convolution/GroupConvolutionBackpropData_1.md
+6 -3
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@@ -109,7 +109,8 @@ is derived from the kernel shape.
1D GroupConvolutionBackpropData
.. code-block:: cpp
.. code-block:: xml
:force:
<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
<data dilations="1" pads_begin="1" pads_end="1" strides="2"/>
@@ -138,7 +139,8 @@ is derived from the kernel shape.
2D GroupConvolutionBackpropData
.. code-block:: cpp
.. code-block:: xml
:force:
<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
@@ -170,7 +172,8 @@ is derived from the kernel shape.
3D GroupConvolutionBackpropData
.. code-block:: cpp
.. code-block:: xml
:force:
<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
<data dilations="1,1,1" pads_begin="1,1,1" pads_end="1,1,1" strides="2,2,2"/>
docs/ops/convolution/GroupConvolution_1.md
+6 -3
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@@ -96,7 +96,8 @@ as in regular convolution and concatenates the results. More thorough explanatio
1D GroupConvolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="GroupConvolution" ...>
<data dilations="1" pads_begin="2" pads_end="2" strides="1" auto_pad="explicit"/>
@@ -124,7 +125,8 @@ as in regular convolution and concatenates the results. More thorough explanatio
2D GroupConvolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="GroupConvolution" ...>
<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1" auto_pad="explicit"/>
@@ -155,7 +157,8 @@ as in regular convolution and concatenates the results. More thorough explanatio
3D GroupConvolution
.. code-block:: cpp
.. code-block:: xml
:force:
<layer type="GroupConvolution" ...>
<data dilations="1,1,1" pads_begin="2,2,2" pads_end="2,2,2" strides="1,1,1" auto_pad="explicit"/>