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#Tensorflow permute mnist update
#Tensorflow permute mnist code

#Tensorflow permute mnist update

Update the Mask_RCNN model from NHWC to NCHW:ġ) Set default image data format : ```python import keras.backend as K K.set_image_data_format('channels_first') ``` 2) change all BN layers from NHWC to NCHW: ```python In function: indentity_block, conv_block, fpn_classifier_graph, build_fpn_mask_graph uff (provided with TensorRT binary release package).graphsurgeon (provided with TensorRT binary release package).

**Usage for MaskRCNN conversion scripts** Dependency: uff with nodes whose input tensor has more than one index dimension (this will be the next TensorRT release).

The UFF Parser cannot handle the inputs with more than one index dimension for convolution, deconvolution and softmax correctly.the CPU version tensorflow's NCHW convolution would introduce the nodes graph like: ``` transpose_NCHW2NHWC -> Convolution_NHWC -> transpose_NHWC2NCHW ``` Therefore we recommend installing tensorflow-gpu.

#Tensorflow permute mnist code

So in most cases, we should reverse the input list when connect the broken graph using (other_node.name)(The example code is in config.py): Generally, the inputs should be the feature map input and inputs of node should be the kernel weights.

the convolution, deconvolution and dense layer's input orders matter.

So to get rid of this situation, we need to add "tf_node:1" and "tf_node:2" to plugin map dict (even they are not really nodes) ``` input: "tf_node" input: "tf_node:1" input: "tf_node:2" ``` although above inputs come from a same node "tf_node" but the graphsurgeon would regard them as output from 3 different nodes.

graphsurgeon cannot handle situation like:.

We should map all the tf nodes which are not supported by TensorRT directly to plugin node.

Using tensorflow's graph_utils, graph_io API to convert keras model to.

Generally, most layers could work well directly in NHWC -> NCHW conversion except Reshape, Flatten, Dense and Softmax applied to feature map

If the model is trained in NHWC, we should make sure NCHW architecture can consume the pretrained weights.

Build up the NCHW inference architecture:.

This is the general guideline to convert a keras model to.