IVGCVSW-5598 ArmNN Doxygen doc needs update
Signed-off-by: James Ward <james.ward@arm.com>
Change-Id: Iac19640fec7aabdfcbb88a0856d4fce3a15d3f27
diff --git a/docs/01_parsers.dox b/docs/01_parsers.dox
index 1c52c4a..1af2503 100644
--- a/docs/01_parsers.dox
+++ b/docs/01_parsers.dox
@@ -44,6 +44,7 @@
- Yolov1_tiny.
- Lenet.
- MobileNetv1.
+- SqueezeNet v1.0 and SqueezeNet v1.1
## The Arm NN SDK supports the following machine learning layers for Caffe networks:
@@ -157,6 +158,7 @@
- CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
+- DEQUANTIZE
- DIV
- EXP
- FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE
@@ -171,10 +173,12 @@
- NEG
- PACK
- PAD
+- QUANTIZE
- RELU
- RELU6
- RESHAPE
- RESIZE_BILINEAR
+- RESIZE_NEAREST_NEIGHBOR
- SLICE
- SOFTMAX
- SPACE_TO_BATCH
@@ -199,6 +203,15 @@
- [Quantized SSD MobileNet](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz)
- DeepSpeech v1 converted from [TensorFlow model](https://github.com/mozilla/DeepSpeech/releases/tag/v0.4.1)
- DeepSpeaker
+- [DeepLab v3+](https://www.tensorflow.org/lite/models/segmentation/overview)
+- FSRCNN
+- EfficientNet-lite
+- RDN converted from [TensorFlow model](https://github.com/hengchuan/RDN-TensorFlow)
+- Quantized RDN (CpuRef)
+- [Quantized Inception v3](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz)
+- [Quantized Inception v4](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) (CpuRef)
+- Quantized ResNet v2 50 (CpuRef)
+- Quantized Yolo v3 (CpuRef)
More machine learning operators will be supported in future releases.
<br/><br/><br/><br/>
@@ -251,6 +264,8 @@
- See the TensorFlow [squeeze documentation](https://www.tensorflow.org/api_docs/python/tf/squeeze) for more information.
- tanh
- See the TensorFlow [tanh documentation](https://www.tensorflow.org/api_docs/python/tf/tanh) for more information.
+- transpose
+ - See the TensorFlow [transpose documentation](https://www.tensorflow.org/api_docs/python/tf/transpose) for more information.
## Partially supported
@@ -283,6 +298,8 @@
- The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [minimum operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/minimum) for more information.
- multiply
- The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [multiply documentation](https://www.tensorflow.org/api_docs/python/tf/multiply) for more information.
+- pack/stack
+ - See the TensorFlow [stack documentation](https://www.tensorflow.org/api_docs/python/tf/stack) for more information.
- pad
- Only supports tf.pad function with mode = 'CONSTANT' and constant_values = 0. See the TensorFlow [pad documentation](https://www.tensorflow.org/api_docs/python/tf/pad) for more information.
- realdiv
@@ -295,9 +312,12 @@
- The parser only supports 2D inputs and does not support selecting the `softmax` dimension. See the TensorFlow [softmax documentation](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) for more information.
- split
- Arm NN supports split along the channel dimension for data formats NHWC and NCHW.
+- strided_slice
+ - See the TensorFlow [strided_slice documentation](https://www.tensorflow.org/api_docs/python/tf/strided_slice) for more information.
- subtract
- The parser does not support all forms of broadcasting [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [subtract documentation](https://www.tensorflow.org/api_docs/python/tf/math/subtract) for more information.
+
## Tested networks
Arm tests these operators with the following TensorFlow fp32 neural networks: