Tensorflow JS Converter Can't Find Expected FusedBatchNorm Input

[ByrdOfAFeather]

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow.js): Yes. I have used the following script to convert a graph model to a .pb format as I could not find official documentation on how to do this:

import tensorflow as tf

meta_path = 'test.meta' # Your .meta file
output_node_names = ['pose/locref_pred/block4/BiasAdd', 'pose/part_pred/block4/BiasAdd']    # Output nodes

with tf.compat.v1.Session() as sess:
    # Restore the graph
    saver = tf.compat.v1.train.import_meta_graph(meta_path)

    # Load weights
    saver.restore(sess,tf.compat.v1.train.latest_checkpoint('checkpoint_folder'))

    # Freeze the graph
    frozen_graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
        sess,
        sess.graph_def,
        output_node_names, variable_names_blacklist=[n.name for n in sess.graph_def.node if "FusedBatchNorm" in n.name])

    # Save the frozen graph
    with open('_graph.pb', 'wb') as f:
      f.write(frozen_graph_def.SerializeToString())

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Kubuntu 21.04
• TensorFlow.js installed from (npm or script link): Installed from pip
• Tensorflow.js Converter Version: 3.9.0

Describe the current behavior When using this command : tensorflowjs_converter --input_format=tf_frozen_model --output_node_names='pose/locref_pred/block4/BiasAdd','pose/part_pred/block4/BiasAdd' output_graph.pb output --skip_op_check

I get

2021-10-04 11:07:07.150484: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-10-04 11:07:13.137106: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1137] Optimization results for grappler item: graph_to_optimize
  debug_stripper: debug_stripper did nothing. time = 0.369ms.
  model_pruner: Graph size after: 1130 nodes (-530), 1201 edges (-530), time = 29.391ms.
  constant_folding: Graph size after: 1088 nodes (-42), 1147 edges (-54), time = 105.765ms.
  arithmetic_optimizer: Graph size after: 1088 nodes (0), 1147 edges (0), time = 52.367ms.
  dependency_optimizer: Graph size after: 1064 nodes (-24), 1123 edges (-24), time = 23.379ms.
  model_pruner: Graph size after: 1064 nodes (0), 1123 edges (0), time = 10.789ms.
  constant_folding: Graph size after: 1064 nodes (0), 1123 edges (0), time = 42.342ms.
  arithmetic_optimizer: Graph size after: 1064 nodes (0), 1123 edges (0), time = 41.601ms.
  dependency_optimizer: Graph size after: 1064 nodes (0), 1123 edges (0), time = 26.46ms.
  debug_stripper: debug_stripper did nothing. time = 2.391ms.
  model_pruner: Graph size after: 1064 nodes (0), 1123 edges (0), time = 15.009ms.
  constant_folding: Graph size after: 1064 nodes (0), 1123 edges (0), time = 40.475ms.
  arithmetic_optimizer: Graph size after: 1064 nodes (0), 1123 edges (0), time = 33.552ms.
  dependency_optimizer: Graph size after: 1064 nodes (0), 1123 edges (0), time = 20.708ms.
  model_pruner: Graph size after: 1064 nodes (0), 1123 edges (0), time = 12.907ms.
  constant_folding: Graph size after: 1064 nodes (0), 1123 edges (0), time = 44.195ms.
  arithmetic_optimizer: Graph size after: 1064 nodes (0), 1123 edges (0), time = 37.797ms.
  dependency_optimizer: Graph size after: 1064 nodes (0), 1123 edges (0), time = 25.408ms.

WARNING:tensorflow:Didn't find expected Conv2D or DepthwiseConv2dNative input to 'resnet_v1_101/block4/unit_1/bottleneck_v1/conv2/BatchNorm/FusedBatchNormV3'
WARNING:tensorflow:Didn't find expected Conv2D or DepthwiseConv2dNative input to 'resnet_v1_101/block4/unit_2/bottleneck_v1/conv2/BatchNorm/FusedBatchNormV3'
WARNING:tensorflow:Didn't find expected Conv2D or DepthwiseConv2dNative input to 'resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/BatchNorm/FusedBatchNormV3'

The warnings here appear to be problematic. When loading the files into a browser, I get

tf.loadGraphModel('http://127.0.0.1:5000/download/model.json') Promise {<pending>} util.ts:105 Uncaught (in promise) Error: Based on the provided shape, [3,3,512,512], the tensor should have 2359296 values but has 2300067

Describe the expected behavior

Either the warnings should not exist (i.e. the FusedBatchNorm is converted properly), or the model should be able to load in the browser without the batch norm layers

Standalone code to reproduce the issue https://github.com/ByrdOfAFeather/tfjs_bug

[rthadur]

@ByrdOfAFeather you mentioned you are using tfjs-converter 3.9 but in your code there is reference to older tfjs version https://github.com/ByrdOfAFeather/tfjs_bug/blob/master/templates/val_model.html#L6 , is this intended ?

[rthadur]

and also Frozen models are not supported in latest versions. Thank you

[ByrdOfAFeather]

@ByrdOfAFeather you mentioned you are using tfjs-converter 3.9 but in your code there is reference to older tfjs version https://github.com/ByrdOfAFeather/tfjs_bug/blob/master/templates/val_model.html#L6 , is this intended ?

Yes so this is my bad - I was referring to my python install of tensorflowjs and didn't check the version I was using on the web, I just tried it with tfjs@3.9.0 and receive the same error when trying to load in the model.

and also Frozen models are not supported in latest versions. Thank you

If this is the case, is there anyway to take a snapshot.meta, snapshot.index, shapshot.data and use it with tensorflowjs? I've being trying to find a way to convert these into a Tensorflow 2 style model (they were generated with tensorflow.compat.v1) but can't seem to find a way to do this anywhere.

[rthadur]

I have no idea , sorry. @pyu10055 ++ for any help . Thank you

[pyu10055]

@ByrdOfAFeather I tried your model, the loading did not generate any error for me. But I cannot execute the model since it contains no input placeholder nodes, can you confirm that you have selected the correct output nodes for freezing the model?

And the warning message during conversion just to indicate that there are no associated convolution nodes for the FusedBatchnorm node, so it cannot be fused further, it should not affect the inference, it is just part of the model optimization we do during conversion.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta http-equiv="X-UA-Compatible" content="ie=edge">
    <title>Document</title>
</head>
<body>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <script>
        window.addEventListener("DOMContentLoaded", main);

        const MODEL_URL = "./model.json";

        async function main() {
            const model = await tf.loadGraphModel(MODEL_URL);
            console.log(model);
        }
    </script>
</body>
</html>

[ByrdOfAFeather]

@ByrdOfAFeather I tried your model, the loading did not generate any error for me. But I cannot execute the model since it contains no input placeholder nodes, can you confirm that you have selected the correct output nodes for freezing the model?

And the warning message during conversion just to indicate that there are no associated convolution nodes for the FusedBatchnorm node, so it cannot be fused further, it should not affect the inference, it is just part of the model optimization we do during conversion.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta http-equiv="X-UA-Compatible" content="ie=edge">
    <title>Document</title>
</head>
<body>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <script>
        window.addEventListener("DOMContentLoaded", main);

        const MODEL_URL = "./model.json";

        async function main() {
            const model = await tf.loadGraphModel(MODEL_URL);
            console.log(model);
        }
    </script>
</body>
</html>

@pyu10055 I am still not able to load the model, even with this code. However, it seems that you are loading the model.json from local disk? When changing this code to load the model from the /download/model.json it fails. I don't have a nodejs dev, environment setup right now, so I can't directly test loading from disk. Could you confirm that when loading from the endpoint it fails? Is it possible that when loading from the disk it's not looking at the saved weights for some reason and just loading the skeleton of the model?

I am almost certain these are the proper outputs of the model (looking at it in tensorboard graphs seems to indicate as much at least). This is a pre-trained resnet model with a slight modification to have two outputs, so perhaps you could comment if you have experience with that sort of setup in tensorflow. The only layers after these are going to be loss layers, which aren't relevant to the prediction, right? As well, shouldn't the converter warn if there were no input nodes?

[pyu10055]

@ByrdOfAFeather maybe you can share screenshot of the network tab in the chrome developer tools while you loading the model. It is likely that you failed to download the binary files. If you have python3, you can use python -m http.server to start a web work, but all your files needs to be under the directory where you run the command.

[ByrdOfAFeather]

@pyu10055

All files seem to be downloaded successfully, ignore the two errors, one happened before I opened the networking tab, I just used loadGraph again in the code provided in the github linked in the original post.

[pyu10055]

@ByrdOfAFeather looks like all of your model binary file is of size 178B, they should be 4MB, it is likely caused by some permission error, you should check the response header, or share screenshot of the header here.

[ByrdOfAFeather]

Unfortunately, it still seems that is not the issue :[ the headers look normal to me @pyu10055

[pyu10055]

@ByrdOfAFeather looks like your model.json is downloaded correctly, and you can try directly test the url of the weight bin file in the browser address bar, see what content you are getting.

[ByrdOfAFeather]

@pyu10055 Here is the output when I try to download, for example, the first weight bin

(also while I have the chance thank you for your dedication on this issue).

{"format": "graph-model", "generatedBy": "2.6.0", "convertedBy": "TensorFlow.js Converter v3.9.0", "signature": {"outputs": {"pose/part_pred/block4/BiasAdd": {"name": "pose/part_pred/block4/BiasAdd"}, "pose/locref_pred/block4/BiasAdd": {"name": "pose/locref_pred/block4/BiasAdd"}}}, "modelTopology": {"node": [{"name": "pose/locref_pred/block4/strided_slice/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/strided_slice/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/strided_slice/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/strided_slice_1/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/strided_slice_1/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/locref_pred/block4/strided_slice_1/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/locref_pred/block4/mul/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/strided_slice_2/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/locref_pred/block4/strided_slice_2/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/locref_pred/block4/strided_slice_2/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/mul_1/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/stack/3", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/locref_pred/block4/weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "8"}, {"size": "2048"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "pose/locref_pred/block4/biases", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "8"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "pose/part_pred/block4/strided_slice/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/part_pred/block4/strided_slice/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/part_pred/block4/strided_slice/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/part_pred/block4/strided_slice_1/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/part_pred/block4/strided_slice_1/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/part_pred/block4/strided_slice_1/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/part_pred/block4/mul/y", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}}}, {"name": "pose/part_pred/block4/strided_slice_2/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/part_pred/block4/strided_slice_2/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/part_pred/block4/strided_slice_2/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "pose/part_pred/block4/mul_1/y", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}}}, {"name": "pose/part_pred/block4/stack/3", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "pose/part_pred/block4/weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "4"}, {"size": "2048"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/SpaceToBatchND/block_shape", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "2"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_3/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_3/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_3/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_4", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "2"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_5/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_5/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_5/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "512"}, {"size": "512"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/BatchToSpaceND/block_shape", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "2"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/crops/0/0", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_6/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_6/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/strided_slice_6/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/required_space_to_batch_paddings/crops/1/0", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/Conv2D/strided_slice/stack", "op": "Const", "attr": {"dtype": {"type": 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[pyu10055]

@ByrdOfAFeather this is the model.json file, not the weight files. Weight files are binary. Please check your weight files, usually their size are 4MB.

[ByrdOfAFeather]

@ByrdOfAFeather this is the model.json file, not the weight files. Weight files are binary. Please check your weight files, usually their size are 4MB.

I see, there was an issue in the repository I submitted for the bug. I fixed it and can now load the model. Now I also see that there are no placeholder elements. It is unclear to me how to solve this, I believe I have selected the correct output nodes.

[ByrdOfAFeather]

I went back and used an export function in the software that I was using rather than using my own export function, I now can load the model and have input nodes. https://github.com/DeepLabCut/DeepLabCut/blob/ce681b4073ab1a0bd5823f892ebe0f9c47a0f7f6/deeplabcut/pose_estimation_tensorflow/export.py#L234 For future reference.

Thanks for helping - I'm going to go ahead and close since this seems to be resolved.

[google-ml-butler[bot]]

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