Layer name mismatch in mxnet pretrained model's .json and weights.

[gauravsharma-97]

Objective:

Trying to convert the "i3d-resnet50-v1-kinetics400" pretrained mxnet model to caffe.

System used: Ubuntu 18.04, Python3

Problem:

While trying to load weights after converting the .json to caffe model, I saw that the names for layers in .json and .params files do not match. They can be viewed below.

Objective:

Trying to convert the "i3d-resnet50-v1-kinetics400" pretrained mxnet model to caffe.

System used: Ubuntu 18.04, Python3

Problem:

While trying to load weights after converting the .json to caffe model, I saw that the names for layers in .json and .params files do not match. They can be viewed below.

Eg. First layer names in .json and .params are conv0_weight and first_stage.0.weight respectively.

I do not understand why this happened since the parameters and json are taken from same model.

Can anyone suggest how to handle this except checking all the layers names manually?

Steps followed:

1. Loaded the model.

>>>from gluoncv.model_zoo import get_model
>>>from gluoncv.utils import export_block
>>>import mxnet
>>>import json

>>>model = get_model(name="i3d_resnet50_v1_kinetics400", nclass=400, pretrained=True, num_segments=1, num_crop=1)

2. Converted the model to json.

>>>js = model.to_json()
>>>js
{
  "nodes": [
    {
      "op": "null", 
      "name": "data", 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "conv0_weight", 
      "attrs": {
        "__dtype__": "0", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64, 3, 5, 7, 7)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "Convolution", 
      "name": "conv0_fwd", 
      "attrs": {
        "dilate": "(1, 1, 1)", 
        "kernel": "(5, 7, 7)", 
        "layout": "NCDHW", 
        "no_bias": "True", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(2, 3, 3)", 
        "stride": "(2, 2, 2)"
      }, 
      "inputs": [[0, 0, 0], [1, 0, 0]]
    }, 
    {
      "op": "null", 
      "name": "batchnorm0_gamma", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "ones", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "batchnorm0_beta", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "zeros", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "batchnorm0_running_mean", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "zeros", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "batchnorm0_running_var", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "ones", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "BatchNorm", 
      "name": "batchnorm0_fwd", 
      "attrs": {
        "axis": "1", 
        "eps": "1e-05", 
        "fix_gamma": "False", 
        "momentum": "0.9", 
        "use_global_stats": "False"
      }, 
      "inputs": [[2, 0, 0], [3, 0, 0], [4, 0, 0], [5, 0, 1], [6, 0, 1]]
    }, 
    {
      "op": "Activation", 
      "name": "relu0_fwd", 
      "attrs": {"act_type": "relu"}, 
      "inputs": [[7, 0, 0]]
    }, 
    {
      "op": "Pooling", 
      "name": "pool0_fwd", 
      "attrs": {
        "global_pool": "False", 
        "kernel": "(1, 3, 3)", 
        "layout": "NCDHW", 
        "pad": "(0, 1, 1)", 
        "pool_type": "max", 
        "pooling_convention": "valid", 
        "stride": "(2, 2, 2)"
      }, 
      "inputs": [[8, 0, 0]]
    }, 
    {
      "op": "null", 
      "name": "layer1_0_conv0_weight", 
      "attrs": {
        "__dtype__": "0", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64, 64, 3, 1, 1)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "Convolution", 
      "name": "layer1_0_conv0_fwd", 
      "attrs": {
        "dilate": "(1, 1, 1)", 
        "kernel": "(3, 1, 1)", 
        "layout": "NCDHW", 
        "no_bias": "True", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(1, 0, 0)", 
        "stride": "(1, 1, 1)"
      }, 
      "inputs": [[9, 0, 0], [10, 0, 0]]
    }, 
    {
      "op": "null", 
      "name": "layer1_0_batchnorm0_gamma", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "ones", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "layer1_0_batchnorm0_beta", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "zeros", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "layer1_0_batchnorm0_running_mean", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "zeros", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
      }, 
      "inputs": []
    }, 
    {
      "op": "null", 
      "name": "layer1_0_batchnorm0_running_var", 
      "attrs": {
        "__dtype__": "0", 
        "__init__": "ones", 
        "__lr_mult__": "1.0", 
        "__shape__": "(64,)", 
        "__storage_type__": "0", 
        "__wd_mult__": "1.0"
        .
        .
        . Continued

3. Saving weights of the model using save_parameters().

>>> model.save_parameters("params_file")
>>> import mxnet.ndarray as nd
>>> tmp = nd.load("params_file.params")
>>> tmp.keys()
dict_keys(['first_stage.0.weight', 'first_stage.1.gamma', 'first_stage.1.beta', 'first_stage.1.running_mean', 'first_stage.1.running_var', 'res_layers.0.0.bottleneck.0.weight', 'res_layers.0.0.bottleneck.1.gamma', 'res_layers.0.0.bottleneck.1.beta', 'res_layers.0.0.bottleneck.1.running_mean', 'res_layers.0.0.bottleneck.1.running_var', 'res_layers.0.0.bottleneck.3.weight', 'res_layers.0.0.bottleneck.4.gamma', 'res_layers.0.0.bottleneck.4.beta', 'res_layers.0.0.bottleneck.4.running_mean', 'res_layers.0.0.bottleneck.4.running_var', 'res_layers.0.0.bottleneck.6.weight', 'res_layers.0.0.bottleneck.7.gamma', 'res_layers.0.0.bottleneck.7.beta', 'res_layers.0.0.bottleneck.7.running_mean', 'res_layers.0.0.bottleneck.7.running_var', 'res_layers.0.0.conv1.weight', 'res_layers.0.0.conv2.weight', 'res_layers.0.0.bn1.gamma', 'res_layers.0.0.bn1.beta', 'res_layers.0.0.bn1.running_mean', 'res_layers.0.0.bn1.running_var', 'res_layers.0.0.bn2.gamma', 'res_layers.0.0.bn2.beta', 'res_layers.0.0.bn2.running_mean', 'res_layers.0.0.bn2.running_var', 'res_layers.0.0.conv3.weight', 'res_layers.0.0.bn3.gamma', 'res_layers.0.0.bn3.beta', 'res_layers.0.0.bn3.running_mean', 'res_layers.0.0.bn3.running_var', 'res_layers.0.0.downsample.0.weight', 'res_layers.0.0.downsample.1.gamma', 'res_layers.0.0.downsample.1.beta', 'res_layers.0.0.downsample.1.running_mean', 'res_layers.0.0.downsample.1.running_var', 'res_layers.0.1.bottleneck.0.weight', 'res_layers.0.1.bottleneck.1.gamma', 'res_layers.0.1.bottleneck.1.beta', 'res_layers.0.1.bottleneck.1.running_mean', 'res_layers.0.1.bottleneck.1.running_var', 'res_layers.0.1.bottleneck.3.weight', 'res_layers.0.1.bottleneck.4.gamma', 'res_layers.0.1.bottleneck.4.beta', 'res_layers.0.1.bottleneck.4.running_mean', 'res_layers.0.1.bottleneck.4.running_var', 'res_layers.0.1.bottleneck.6.weight', 'res_layers.0.1.bottleneck.7.gamma', 'res_layers.0.1.bottleneck.7.beta', 'res_layers.0.1.bottleneck.7.running_mean', 'res_layers.0.1.bottleneck.7.running_var', 'res_layers.0.1.conv1.weight', 'res_layers.0.1.conv2.weight', 'res_layers.0.1.bn1.gamma', 'res_layers.0.1.bn1.beta', 'res_layers.0.1.bn1.running_mean', 'res_layers.0.1.bn1.running_var', 'res_layers.0.1.bn2.gamma', 'res_layers.0.1.bn2.beta', 'res_layers.0.1.bn2.running_mean', 'res_layers.0.1.bn2.running_var', 'res_layers.0.1.conv3.weight', 'res_layers.0.1.bn3.gamma', 'res_layers.0.1.bn3.beta', 'res_layers.0.1.bn3.running_mean', 'res_layers.0.1.bn3.running_var', 'res_layers.0.2.bottleneck.0.weight', 'res_layers.0.2.bottleneck.1.gamma', 'res_layers.0.2.bottleneck.1.beta', 'res_layers.0.2.bottleneck.1.running_mean', 'res_layers.0.2.bottleneck.1.running_var', 'res_layers.0.2.bottleneck.3.weight', 'res_layers.0.2.bottleneck.4.gamma', 'res_layers.0.2.bottleneck.4.beta', 'res_layers.0.2.bottleneck.4.running_mean', 'res_layers.0.2.bottleneck.4.running_var', 'res_layers.0.2.bottleneck.6.weight', 'res_layers.0.2.bottleneck.7.gamma', 'res_layers.0.2.bottleneck.7.beta', 'res_layers.0.2.bottleneck.7.running_mean', 'res_layers.0.2.bottleneck.7.running_var', 'res_layers.0.2.conv1.weight', 'res_layers.0.2.conv2.weight', 'res_layers.0.2.bn1.gamma', 'res_layers.0.2.bn1.beta', 'res_layers.0.2.bn1.running_mean', 'res_layers.0.2.bn1.running_var', 'res_layers.0.2.bn2.gamma', 'res_layers.0.2.bn2.beta', 'res_layers.0.2.bn2.running_mean', 'res_layers.0.2.bn2.running_var', 'res_layers.0.2.conv3.weight', 'res_layers.0.2.bn3.gamma', 'res_layers.0.2.bn3.beta', 'res_layers.0.2.bn3.running_mean', 'res_layers.0.2.bn3.running_var', 'res_layers.1.0.bottleneck.0.weight', 'res_layers.1.0.bottleneck.1.gamma', 'res_layers.1.0.bottleneck.1.beta', 'res_layers.1.0.bottleneck.1.running_mean', 'res_layers.1.0.bottleneck.1.running_var', 'res_layers.1.0.bottleneck.3.weight', 'res_layers.1.0.bottleneck.4.gamma', 'res_layers.1.0.bottleneck.4.beta', 'res_layers.1.0.bottleneck.4.running_mean', 'res_layers.1.0.bottleneck.4.running_var', 'res_layers.1.0.bottleneck.6.weight', 'res_layers.1.0.bottleneck.7.gamma', 'res_layers.1.0.bottleneck.7.beta', 'res_layers.1.0.bottleneck.7.running_mean', 'res_layers.1.0.bottleneck.7.running_var', 'res_layers.1.0.conv1.weight', 'res_layers.1.0.conv2.weight', 'res_layers.1.0.bn1.gamma', 'res_layers.1.0.bn1.beta', 'res_layers.1.0.bn1.running_mean', 'res_layers.1.0.bn1.running_var', 'res_layers.1.0.bn2.gamma', 'res_layers.1.0.bn2.beta', 'res_layers.1.0.bn2.running_mean', 'res_layers.1.0.bn2.running_var', 'res_layers.1.0.conv3.weight', 'res_layers.1.0.bn3.gamma', 'res_layers.1.0.bn3.beta', 'res_layers.1.0.bn3.running_mean', 'res_layers.1.0.bn3.running_var', 'res_layers.1.0.downsample.0.weight', 'res_layers.1.0.downsample.1.gamma', 'res_layers.1.0.downsample.1.beta', 'res_layers.1.0.downsample.1.running_mean', 'res_layers.1.0.downsample.1.running_var', 'res_layers.1.1.bottleneck.0.weight', 'res_layers.1.1.bottleneck.1.gamma', 'res_layers.1.1.bottleneck.1.beta', 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>>> 

[leezu]

You need to use .export () API to jointly export the json and parameters.