Convert a Tensorflow Object Detection SavedModel to a Web Model For TensorflowJS

## Convert Tensorflow SavedModel to WebModel for TF-JS
#Currently within the Tensorflow ecosystem there is a ton of confusing similar words.
#Maybe this is because of my own understanding and is not due to their naming.
#Regardless I struggled making this work, so I #figured I would write a Gist.

# Firstly we are trying to convert a previously created Tensorflow frozen graph or checkpoint files.
# Specifically I wanted to #convert some of the Tensorflow Object Detection API models.

# We already have a SavedModel in the download from the object detection model zoo.
# If you need to get a SavedModel from your own trained Object Detection Model, you will need to export it using the script
# provided by the object_detection module.  The python script is called exporter. It is not that well documented, but if you
# send it a pipeline config along with the correct params it will create a frozen graph as well as a SavedModel.

# Download the model files.
wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz

# Untar the model .
tar -xzvf ssd_mobilenet_v2_coco_2018_03_29.tar.gz
# Change Directory
cd ssd_mobilenet_v2_coco_2018_03_29

pip install tensorflow-gpu # Or just tensorflow for CPU
pip install tensorflowjs

# So now we have a SavedModel in the directory of ssd_mobilenet_v2_coco_2018_03_29/saved_model

# We need to find the output and inputs for the model however. The easiest way to do this with a SavedModel is to use
# the saved_model_cli that is included with the tensorflow pip package.

# this command will give you only the information that matters
saved_model_cli show --dir ssd_mobilenet_v2_coco_2018_03_29/saved_model --tag_set serve --signature_def serving_default
# output of command
#The given SavedModel SignatureDef contains the following input(s):
#inputs['inputs'] tensor_info:
#    dtype: DT_UINT8
#    shape: (-1, -1, -1, 3)
#    name: image_tensor:0
#The given SavedModel SignatureDef contains the following output(s):
#outputs['detection_boxes'] tensor_info:
#    dtype: DT_FLOAT
#    shape: (-1, 100, 4)
#    name: detection_boxes:0
#outputs['detection_classes'] tensor_info:
#    dtype: DT_FLOAT
#    shape: (-1, 100)
#    name: detection_classes:0
#outputs['detection_scores'] tensor_info:
#    dtype: DT_FLOAT
#    shape: (-1, 100)
#    name: detection_scores:0
#outputs['num_detections'] tensor_info:
#    dtype: DT_FLOAT
#    shape: (-1)
#    name: num_detections:0
#Method name is: tensorflow/serving/predict

# this will give you all the information stored within the graph.
saved_model_cli show --dir ssd_mobilenet_v2_coco_2018_03_29/saved_model --all

#output is the same but much longer


# now that we know the node names we can run our tensorflow JS command
current_dir=$(pwd)
tensorflowjs_converter \
    --input_format=tf_saved_model \
    --output_node_names='detection_boxes,detection_scores,num_detections,detection_classes' \
    --saved_model_tags=serve \
    ${current_dir}/ssd_mobilenet_v2_coco_2018_03_29/saved_model \
    ${current_dir}/ssd_mobilenet_v2_coco_2018_03_29/web_model

# after the normal GPU intialization spam your going to see this.


# Converted 0 variables to const ops.
# Unsupported Ops in the model
#   TopKV2, Exit, Split, TensorArrayGatherV3,
#   NonMaxSuppressionV2, Assert, TensorArraySizeV3,
#   Unpack, TensorArrayWriteV3, TensorArrayReadV3,
#   All, LoopCond, Merge, Switch, Enter, Where,
#   NextIteration, TensorArrayV3, TensorArrayScatterV3,
#   Rank, StridedSlice, Size


# on the plus side, the serving model and the output nodes that you have
# now gained can already be used with tensorflow serving.


# Yay us, anyways once these are better supported I will update this gist appropriately.
# Currently I have tested the above procedure on the following models from the Tf-ObjectDetection Model Zoo.
# Ill add more as I continue to test the different models.

# ssd_inception_v2_coco
# faster_rcnn_inception_v2_coco
# ssd_mobilenet_v1_coco