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author | Jan Eilers <jan.eilers@arm.com> | 2021-02-02 13:18:09 +0000 |
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committer | Jan Eilers <jan.eilers@arm.com> | 2021-02-08 09:23:48 +0000 |
commit | 53ca2e5bba4fcef8285acc1bed534ea2bc8fb3d0 (patch) | |
tree | 490d3c67c43b0984a91a4a217e649acd672bc07f /src/armnnCaffeParser/CaffeSupport.md | |
parent | 72a9929d1ae37a9c32a0c51eb8491e65c3d1add2 (diff) | |
download | armnn-53ca2e5bba4fcef8285acc1bed534ea2bc8fb3d0.tar.gz |
IVGCVSW-5605 Doxygen: Update parser section
* Removes support.md files from all parsers. Lists of supported
operators are now kept in doxygen only
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: I137e03fdd9f41751624bdd0dd25e2db5ef4ef94f
Diffstat (limited to 'src/armnnCaffeParser/CaffeSupport.md')
-rw-r--r-- | src/armnnCaffeParser/CaffeSupport.md | 40 |
1 files changed, 0 insertions, 40 deletions
diff --git a/src/armnnCaffeParser/CaffeSupport.md b/src/armnnCaffeParser/CaffeSupport.md deleted file mode 100644 index 3501a78ae8..0000000000 --- a/src/armnnCaffeParser/CaffeSupport.md +++ /dev/null @@ -1,40 +0,0 @@ -# Caffe layers supported by the Arm NN SDK -This reference guide provides a list of Caffe layers the Arm NN SDK currently supports. - -Although some other neural networks might work, Arm tests the Arm NN SDK with Caffe implementations of the following neural networks: - -- AlexNet. -- Cifar10. -- Inception-BN. -- Resnet_50, Resnet_101 and Resnet_152. -- VGG_CNN_S, VGG_16 and VGG_19. -- Yolov1_tiny. -- Lenet. -- MobileNetv1. -- SqueezeNet v1.0 and SqueezeNet v1.1 - -The Arm NN SDK supports the following machine learning layers for Caffe networks: - - -- Argmax, excluding the top_k and out_max_val parameters. -- BatchNorm, in inference mode. -- Convolution, excluding Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters. -- Deconvolution, excluding the Dilation Size, Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters. - - Caffe doesn't support depthwise convolution, the equivalent layer is implemented through the notion of groups. ArmNN supports groups this way: - - when group=1, it is a normal conv2d - - when group=#input_channels, we can replace it by a depthwise convolution - - when group>1 && group<#input_channels, we need to split the input into the given number of groups, apply a separate convolution and then merge the results -- Concat, along the channel dimension only. -- Dropout, in inference mode. -- Eltwise, excluding the coeff parameter. -- Inner Product, excluding the Weight Filler, Bias Filler, Engine, and Axis parameters. -- Input. -- LRN, excluding the Engine parameter. -- Pooling, excluding the Stochastic Pooling and Engine parameters. -- ReLU. -- Scale. -- Softmax, excluding the Axis and Engine parameters. -- Split. - -More machine learning layers will be supported in future releases. |