Error in Prediction of certain Model Families

[Manideep]

Hi ,

I have generated an google net onnx model for prediction and the model is compatible to predict using in built predictor but i couldn't predict using my customized predictor. From the families listed in https://github.com/microsoft/nn-Meter/tree/dev/dataset-generator/nn_meter/dataset/generator/configs , I am facing this issue with google net , dense net , squeeze net , shufflenetV2 families.

I have attached the required materials for reference : Material

When running the nn-meter predictor command:
nn-meter predict --predictor tflitemicropredictor --predictor-version 1.0 --onnx googlenet_0_deq.onnx
resulted in the following error:

2023-05-05 17:21:23.559308: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-05-05 17:21:23.559335: 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.
(nn-Meter) checking local kernel predictors at /../nn-Meter/py3.9_env/tflitemicropredictor
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/addrelu.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/dwconv-bn-relu.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/add.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/bnrelu.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/relu.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/global-avgpool.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/bn.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/maxpool.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/hswish.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/fc.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/conv-bn-relu.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/split.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/se.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/avgpool.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/concat.pkl
(nn-Meter) load predictor /../nn-Meter/py3.9_env/tflitemicropredictor/channelshuffle.pkl
(nn-Meter) Start latency prediction ...
Traceback (most recent call last):
File "/../nn-Meter/py3.9_env/bin/nn-meter", line 33, in
sys.exit(load_entry_point('nn-meter', 'console_scripts', 'nn-meter')())
File "/../nn-Meter/py3.9_env/nn-Meter/nn_meter/utils/nn_meter_cli/interface.py", line 266, in nn_meter_cli
args.func(args)
File "/../nn-Meter/py3.9_env/nn-Meter/nn_meter/utils/nn_meter_cli/predictor.py", line 56, in apply_latency_predictor_cli
latency = predictor.predict(model, model_type) # in unit of ms
File "/../nn-Meter/py3.9_env/nn-Meter/nn_meter/predictor/nn_meter_predictor.py", line 113, in predict
py = nn_predict(self.kernel_predictors, self.kd.get_kernels()) # in unit of ms
File "/../nn-Meter/py3.9_env/nn-Meter/nn_meter/predictor/prediction/predict_by_kernel.py", line 54, in nn_predict
py = predict_model(features, predictors)
File "/../nn-Meter/py3.9_env/nn-Meter/nn_meter/predictor/prediction/predict_by_kernel.py", line 39, in predict_model
pys = pred.predict(dicts[kernel]) # in unit of ms
File "/../nn-Meter/py3.9_env/lib/python3.9/site-packages/sklearn/ensemble/_forest.py", line 981, in predict
X = self._validate_X_predict(X)
File "/../nn-Meter/py3.9_env/lib/python3.9/site-packages/sklearn/ensemble/_forest.py", line 602, in _validate_X_predict
X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr", reset=False)
File "/../nn-Meter/py3.9_env/lib/python3.9/site-packages/sklearn/base.py", line 588, in _validate_data
self._check_n_features(X, reset=reset)
File "/../nn-Meter/py3.9_env/lib/python3.9/site-packages/sklearn/base.py", line 389, in _check_n_features
raise ValueError(
ValueError: X has 6 features, but RandomForestRegressor is expecting 5 features as input.

Hope you reply back soon.
Thank you.

[Manideep]

When i tried debugging this issue , the error is raised when we are using 'concat' predictor. When building , we fit the random forest regressor with 5 features ( refer here ) with feature list as "concat": ["HW", "CIN1", "CIN2", "CIN3", "CIN4"] which are extracted from the model whereas when we are trying to predict , we are extracting the features and appending no. of input tensors as another feature which is giving us 6 features from the model hence getting this error refer here can you say why we are adding that additional feature ? keeping the snippet here
features = [inputh, len(itensors)]

[Manideep]

When i tried debugging this issue , the error is raised when we are using 'concat' predictor. When building , we fit the random forest regressor with 5 features ( refer here ) with feature list as "concat": ["HW", "CIN1", "CIN2", "CIN3", "CIN4"] which are extracted from the model whereas when we are trying to predict , we are extracting the features and appending no. of input tensors as another feature which is giving us 6 features from the model hence getting this error refer here can you say why we are adding that additional feature ? keeping the snippet here features = [inputh, len(itensors)]

Did necessary changes to the way we extract features while predicting and raised a pull request here