Models converted from `saved_model` to `tfjs_graph_model` loose output signature information

[vladmandic]

Models converted from saved_model to tfjs_graph_model loose output signature information.

This is not specific to any single model, it's a generic converter issue.

a) Saved model from https://tfhub.dev/tensorflow/efficientdet/d0/1?tf-hub-format=compressed

console.log(await tf.node.getMetaGraphsFromSavedModel(modelPath));

outputs: {
  detection_anchor_indices: { dtype: 'float32', name: 'StatefulPartitionedCall:0', shape: [Array] },
  detection_boxes: { dtype: 'float32', name: 'StatefulPartitionedCall:1', shape: [Array] },
  detection_classes: { dtype: 'float32', name: 'StatefulPartitionedCall:2', shape: [Array] },
  detection_multiclass_scores: { dtype: 'float32', name: 'StatefulPartitionedCall:3', shape: [Array] },
  detection_scores: { dtype: 'float32', name: 'StatefulPartitionedCall:4', shape: [Array] },
  num_detections: { dtype: 'float32', name: 'StatefulPartitionedCall:5', shape: [Array] },
  raw_detection_boxes: { dtype: 'float32', name: 'StatefulPartitionedCall:6', shape: [Array] },
  raw_detection_scores: { dtype: 'float32', name: 'StatefulPartitionedCall:7', shape: [Array] }
}

b) Same model converted to TFJS Graph model using tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model . graph

const model = await tf.loadGraphModel(`file://${path.join(__dirname, modelPath)}`);
console.log(model.executor._signature); // can also use model.outputs, but that has even less info

outputs: {
  'Identity_6:0': { name: 'Identity_6:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '49104' }, { size: '4' }, [length]: 3 ] } },
  'Identity_1:0': { name: 'Identity_1:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, { size: '4' }, [length]: 3 ] } },
  'Identity_3:0': { name: 'Identity_3:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, { size: '90' }, [length]: 3 ] } },
  'Identity_2:0': { name: 'Identity_2:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity_5:0': { name: 'Identity_5:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, [length]: 1 ] } },
  'Identity_7:0': { name: 'Identity_7:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '49104' }, { size: '90' }, [length]: 3 ] } },
  'Identity_4:0': { name: 'Identity_4:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity:0': { name: 'Identity:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } }
}

This is cosmetic as all outputs are still present, but makes converted models extremely difficult to use.

Environment: Ubuntu 20.04 with NodeJS 14.11.0 and TFJS 2.4.0

[pyu10055]

@vladmandic Thank you for reporting this issue. There is no good way to track the original signature to the optimized nodes. Have you tried the tfhub model conversion? I think the signature will be preserved that way.

[vladmandic]

@pyu10055

Currently less than half of models posted on https://tfhub.dev/ have JS links (e.g. none of EfficientNet or EfficientDet or anything trained on OpenImages or ...), but they all have links to a saved model.

Or is there a way to trigger TFHub model conversion that I'm missing?

[pyu10055]

yes, you can convert the TFHub module directly using the converter.

tensorflowjs_converter \
    --input_format=tf_hub \
    'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/1' \
    /mobilenet/web_model

[vladmandic]

@pyu10055 I always considered that the same as downloading saved_model and then running TFJS converter on it. Just did I quick try and it produces tfjs_graph_model that is identical (and has the same signature) - meaning no real signature.

tensorflowjs_converter --input_format tf_hub --signature_name serving_default https://tfhub.dev/tensorflow/efficientdet/d0/1 .

{
  'Identity_5:0': { name: 'Identity_5:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, [length]: 1 ] } },
  'Identity_7:0': { name: 'Identity_7:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '49104' }, { size: '90' }, [length]: 3 ] } },
  'Identity_2:0': { name: 'Identity_2:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity_6:0': { name: 'Identity_6:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '49104' }, { size: '4' }, [length]: 3 ] } },
  'Identity:0': { name: 'Identity:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity_4:0': { name: 'Identity_4:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity_1:0': { name: 'Identity_1:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, { size: '4' }, [length]: 3 ] } },
  'Identity_3:0': { name: 'Identity_3:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, { size: '90' }, [length]: 3 ] } }
}

[pyu10055]

@vladmandic Thank you for trying, we will try to find a way that allows us to track the original signature name to the graph nodes after optimization. Will post any findings here.

[vladmandic]

@pyu10055

no need to track them during optimization, just re-map them correctly as a last step before writing model.json

graph_model output nodes are named using Identity_{index} where index is in exactly the same order as output nodes of saved_moded

so in this example,

Identity:0 => detection_anchor_indices
Identity_1 => detection_boxes
Identity_2 => detection_classes
...

example saved_model output nodes:

outputs: {
  detection_anchor_indices: { dtype: 'float32', name: 'StatefulPartitionedCall:0', shape: [Array] },
  detection_boxes: { dtype: 'float32', name: 'StatefulPartitionedCall:1', shape: [Array] },
  detection_classes: { dtype: 'float32', name: 'StatefulPartitionedCall:2', shape: [Array] },
  detection_multiclass_scores: { dtype: 'float32', name: 'StatefulPartitionedCall:3', shape: [Array] },
  detection_scores: { dtype: 'float32', name: 'StatefulPartitionedCall:4', shape: [Array] },
  num_detections: { dtype: 'float32', name: 'StatefulPartitionedCall:5', shape: [Array] },
  raw_detection_boxes: { dtype: 'float32', name: 'StatefulPartitionedCall:6', shape: [Array] },
  raw_detection_scores: { dtype: 'float32', name: 'StatefulPartitionedCall:7', shape: [Array] }
}

example graph_model output nodes:

outputs: {
  'Identity_6:0': { name: 'Identity_6:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '49104' }, { size: '4' }, [length]: 3 ] } },
  'Identity_1:0': { name: 'Identity_1:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, { size: '4' }, [length]: 3 ] } },
  'Identity_3:0': { name: 'Identity_3:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, { size: '90' }, [length]: 3 ] } },
  'Identity_2:0': { name: 'Identity_2:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity_5:0': { name: 'Identity_5:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, [length]: 1 ] } },
  'Identity_7:0': { name: 'Identity_7:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '49104' }, { size: '90' }, [length]: 3 ] } },
  'Identity_4:0': { name: 'Identity_4:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } },
  'Identity:0': { name: 'Identity:0', dtype: 'DT_FLOAT', tensorShape: { dim: [ { size: '1' }, { size: '100' }, [length]: 2 ] } }
}

[rohanmuplara]

@vladmandic @pyu10055 any comments on this conversion for inputs. see issues https://github.com/tensorflow/tfjs/issues/4861

[vladmandic]

@rohanmuplara yup, it's the same issue and it's a really annoying one

i work around it by manually editing resulting model.json to include correct output nodes that i get from my script that analyzes original saved model and resulting graph model (some converted models are even worse and include no signature at all and then you have to look at executor workflow to see them)
https://github.com/vladmandic/node-detector-test/blob/main/signature.js

[rohanmuplara]

@vladmandic can you describe a little more about what the executor versus signature stuff is. Also, can you explain how this code above edits the script in a way that is helfpul. To me, it just seems you are just iterating the input and just outputting it back out

[vladmandic]

signature is a signature part of the model.json, but some models do not contain properly filled out signature section, so next option is to look at actual model execution via executor.

yes, it's just iterating and outputing - but it allows for matching of saved vs graph model input/output nodes. with a touch more work, it could rewrite actual model.json signature part - i'll do that soon.

[rohanmuplara]

Thanks makes sense. So you are saying you are going to manually copy the saved_model from the saved json files so the order stays the same. Another issue is I had is from the signature of the inputs are different than the names defined in the code but the model still works when I pass in the old names. https://share.descript.com/view/OX82xb6lY7q

[vladmandic]

@rohanmuplara that's exactly why signature part exists - so tensor names can be associated with logical names without changing the model.

[rohanmuplara]

I have two follow up questions.

1. I only see these signature names in the input.json but when I call predict it doesn't seem to work with the signature names and only works with original names used when defining the model initially. I don't even see them in the json file. https://share.descript.com/view/0yeqwBdDSKk
2. When we define the signature functions, how do we match them up to the corresponding ones in the input. ie if two inputs tensors have the same shape, how do you know which signature refers to which.

On Sun, Mar 28, 2021 at 4:18 AM Vladimir Mandic @.***> wrote:

@rohanmuplara https://github.com/rohanmuplara that's exactly why signature part exists - so tensor names can be associated with logical names without changing the model.

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[vladmandic]

This is IMO as there are no good docs :(

• ":0" at the end of a name refers to first-instance-of, it's not an actual name
  So "input_1:0" maps to first instance of "input_1" in topology
• Input must be matched with a Placeholder op in topology or it cannot be used
• You can't pass logical names to model itself as predict() or execute() do not look at signature Signature can be used to map tensors before/after inference
  This does limit the usage of logical names
• If two tensors (input or output) have same shape, there is no way to differentiate them at this time. It's a mess
  That's main reason why I'm planning to write a helper program that basically
  rewrites Graph model's model.json according to signature from Saved model after conversion

See this example:

{
  "signature": {
      "inputs": { "input_1:0": {"name":"input_1:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"3"},{"size":"416"},{"size":"416"}]}}},
  },
  "modelTopology": {
    "node": [
      {"name":"input_1","op":"Placeholder","attr":{"shape":{"shape":{"dim":[{"size":"1"},{"size":"3"},{"size":"416"},{"size":"416"}]}},"dtype":{"type":"DT_FLOAT"}}},
    ]
  }
}

I really wish TFJS team fixed tensorflowjs_converter tool...

[rohanmuplara]

Makes sense. thanks so much.

[rohanmuplara]

hey @vladmandic https://github.com/tensorflow/tfjs/issues/4861 great suggestion by tfjs team if you add names to input layers it seems to work. I also think most of these problems are on the regular tensorflow serializer side actually.

[vladmandic]

@rohanmuplara good stuff, but doesn't help when i'm converting a pretrained model.

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