MMdnn: MXNet convertorToIR don't parse 'attrs' layer parameters

Platform: Ununtu 16.04

Python version: 3.6

Source framework with version: MXNet 1.0.0 with GPU

Destination framework with version: TensorFlow 1.4.1 with GPU

Pre-trained model path: LResNet50E-IR

Model json-file contain layers in the following form: { "op": "Convolution", "name": "conv0", "attrs": { "kernel": "(3, 3)", "no_bias": "True", "num_filter": "64", "pad": "(1, 1)", "stride": "(1, 1)", "workspace": "256" }, "inputs": [[3, 0, 0], [4, 0, 0]] }

Running scripts: python -m mmdnn.conversion._script.convertToIR -f mxnet -n model-symbol.json -w model-0000.params -d model-descr512 --inputShape 3 112 112

Error:

Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/opt/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/opt/anaconda3/lib/python3.6/site-packages/mmdnn/conversion/_script/convertToIR.py", line 140, in <module>
    _main()
  File "/opt/anaconda3/lib/python3.6/site-packages/mmdnn/conversion/_script/convertToIR.py", line 135, in _main
    ret = _convert(args)
  File "/opt/anaconda3/lib/python3.6/site-packages/mmdnn/conversion/_script/convertToIR.py", line 72, in _convert
    parser.run(args.dstPath)
  File "/opt/anaconda3/lib/python3.6/site-packages/mmdnn/conversion/common/DataStructure/parser.py", line 22, in run
    self.gen_IR()
  File "/opt/anaconda3/lib/python3.6/site-packages/mmdnn/conversion/mxnet/mxnet_parser.py", line 266, in gen_IR
    func(current_node)
  File "/opt/anaconda3/lib/python3.6/site-packages/mmdnn/conversion/mxnet/mxnet_parser.py", line 457, in rename_Convolution
    assert "attr" in source_node.layer or "param" in source_node.layer
AssertionError

About this issue

  • Original URL
  • State: open
  • Created 6 years ago
  • Comments: 43 (17 by maintainers)

Commits related to this issue

Most upvoted comments

Hi @StanislasBertrand , Please refer to this issue

+2
namizzz on Aug 11, 2018

To sum up, I have compared the inference results of converted tensorflow and original mxnet. They are exactly the same. The details go as follows: First, as @belgraviton said,

python -m mmdnn.conversion._script.convertToIR -f mxnet -n model-symbol.json -w model-0000.params -d model-descr512 --inputShape 3 112 112

Here comes the IR structure.

IR network structure is saved as [model-descr512.pb].
IR weights are saved as [model-descr512.npy].

Secondly, just as @xsr-ai mentioned,

python -m mmdnn.conversion._script.IRToCode -f tensorflow --IRModelPath model-descr512.pb --IRWeightPath model-descr512.npy --dstModelPath tf_resnet50.py

Here comes the tensorflow snippet.

Target network code snippet is saved as [tf_resnet50.py].

However, Two things have to be modified in this snippet.

  • The “‘_minusscalar0_second’ is not a valid node name” issue The line 25-29 of the snippet is
    _mulscalar0_second = tf.constant(__weights_dict['_mulscalar0_second']['value'], name='_mulscalar0_second')
    _minusscalar0_second = tf.constant(__weights_dict['_minusscalar0_second']['value'], name='_minusscalar0_second')
    data            = tf.placeholder(tf.float32, shape = (None, 112, 112, 3), name = 'data')
    minusscalar0    = data - _minusscalar0_second
    mulscalar0      = minusscalar0 * _mulscalar0_second

_mulscalar0_second and _minusscalar0_second should be changed, say mulscalar0_second and minusscalar0_second respectively.

Then the code should be like

    mulscalar0_second = tf.constant(__weights_dict['_mulscalar0_second']['value'], name='mulscalar0_second')
    minusscalar0_second = tf.constant(__weights_dict['_minusscalar0_second']['value'], name='minusscalar0_second')
    data            = tf.placeholder(tf.float32, shape = (None, 112, 112, 3), name = 'data')
    minusscalar0    = data - minusscalar0_second
    mulscalar0      = minusscalar0 * mulscalar0_second

In my view, this may because the node name can not begin with ‘_’ in tensorflow.

  • The “bn1 = tf.reshape(bn1, [-1, 7x7x512])” issue.

The code (line 255-260) should be converted from

    bn1             = batch_normalization(plus23, variance_epsilon=1.99999994948e-05, name='bn1')
    pre_fc1         =...

to

    bn1             = batch_normalization(plus23, variance_epsilon=1.99999994948e-05, name='bn1')
    bn1             = tf.reshape(bn1, [-1, 7*7*512])
    pre_fc1         = ...

In my opinion, this is because in this mxnet json, there is no flatten operator before the fc layer. That is why there is no flatten layer when converted to tensorflow. Here, we add this flatten layer to the tensorflow code.

As to the Sanity checks, I compare the inference result of the two. You might take it as a reference. For mxnet,

# inference with mxnet

import mxnet as mx
from tensorflow.contrib.keras.api.keras.preprocessing import image
import numpy as np

from collections import namedtuple
Batch = namedtuple('Batch', ['data'])

ctx = mx.cpu()

# load model
sym, arg_params, aux_params = mx.model.load_checkpoint('/Users/kit/Downloads/model-r50-am-lfw-n/model', 0)
mod = mx.mod.Module(symbol = sym, context= ctx, label_names= None)
mod.bind(for_training=False, data_shapes=[('data', (1, 3, 112, 112))], label_shapes= mod._label_shapes)
mod.set_params(arg_params, aux_params, allow_missing= True)


path = '/Users/kit/github/MMdnn/mmdnn/conversion/examples/data/seagull.jpg'

# load image with BGRTranspose=True
img = image.load_img(path, target_size = (112, 112))
img = image.img_to_array(img)

img = img[..., ::-1]


# channel first in mxnet
img = np.expand_dims(img, 0).transpose((0,3,1,2))

# compute the predict probabilities
mod.forward(Batch([mx.nd.array(img)]))
prob = mod.get_outputs()[0].asnumpy()
prob = np.squeeze(prob)
print(prob)

For tensorflow,

Exploit the script tensorflow_inference.py

# inference with tensorflow

from __future__ import absolute_import
import argparse
import numpy as np
from six import text_type as _text_type
from tensorflow.contrib.keras.api.keras.preprocessing import image
import tensorflow as tf


parser = argparse.ArgumentParser()

parser.add_argument('-n', type=_text_type, default='kit_imagenet',
                    help='Network structure file name.')


parser.add_argument('-w', type=_text_type, required=True,
                    help='Network weights file name')

parser.add_argument('--image', '-i',
                    type=_text_type, help='Test image path.',
                    default="mmdnn/conversion/examples/data/seagull.jpg"
)


args = parser.parse_args()
if args.n.endswith('.py'):
    args.n = args.n[:-3]

# import converted model
model_converted = __import__(args.n).KitModel(args.w)
input_tf, model_tf = model_converted

# load img with BGRTranspose=True
img = image.load_img(args.image, target_size = (112, 112))
img = image.img_to_array(img)
img = img[..., ::-1]

input_data = np.expand_dims(img, 0)

# inference with tensorflow
with tf.Session() as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    predict = sess.run(model_tf, feed_dict = {input_tf : input_data})
print(predict)

one can get the inference by running

python tensorflow_inference.py -n tf_resnet50.py -w model-descr512.npy -i /Users/kit/github/MMdnn/mmdnn/conversion/examples/data/seagull.jpg

To sum up, the two results are exactly the same,

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+2
JiahaoYao on May 3, 2018
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