I have two models that are in ONNX format. Both models are similar (both are pre-trained deep learning models, ex. ResNet50 models). The only difference between them is that the last layers are optimized/retrained for different data sets.
I want to merge the first k layers of these two models, as shown below. This should enhance the performance of inference.
To make my case clearer these are examples from other machine learning tools to implement this feature Ex. Pytorch, Keras.
You can use the ONNX package and the APIs it exposes (https://github.com/onnx/onnx/blob/master/docs/PythonAPIOverview.md) to mutate models/graphs.
The sclblonnx package provides a number of higher level functions to edit ONNX graphs, including the ability to merge two subgraphs.
sclblonnx package does not support dynamic size inputs.
For dynamic size inputs, one solution would be writing your own code using ONNX API as stated earlier.Another solution would be converting the two ONNX models to a framework(Tensorflow or PyTorch) using tools like onnx-tensorflow or onnx2pytorch. You could manipulate the networks in the Tensorflow or Pytorch and export the whole network to Onnx format.
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For a research project, I need to be able to give input to say the third encoder layer of a pre-trained BERT model.
I know that things like these are done using hooks in PyTorch, but that works when I'm defining the model myself.
I was hoping for some frameworks that implement that sort of thing for popular pre-trained models. Or if there was a way to do this in huggingface itself.
I am trying to do binary classification using transfer learning using Timm
In the process, I want to experiment with freezing/unfreezing different layers of different architectures but so far, I am able to freeze/unfreeze entire models only.
Can anyone help me in illustrating it with a couple of model architectures for the sake of heterogeneity of different architectures?
Below, I am ilustrating the entire freezing of couple of architectures using Timm - convnext and resnet but can anyone illustrate me with any different models but only using Timm(As it is more comprehensive than Pytorch model zoo)-
import timm
convnext = timm.create_model('convnext_tiny_in22k', pretrained=True,num_classes=2)
resnet = timm.create_model('resnet50d', pretrained=True,num_classes=2)
I'm looking to train a word2vec 2.0 model from scratch, but I am a bit new to the field. Crucially, I would like to train it using a large dataset of non-human speech (i.e. cetacean sounds) in order to capture the underlying structure.
Once the pre-training is performed, is it possible to visualize the embeddings the model creates, in a similar way to how latent features are visualized in image processing when using e.g. CNNs? Or are the representations too abstract to be mapped to a spectrogram?
What I would like to do is to see what features the network is learning as the units of speech.
Thanks in advance for the help!
Does number of parameters and FLOPS (float operations per second) change when convert a model from PyTorch to ONNX or TensorRT format?
I don't think Anvar's post answered OP's question thoroughly so I did a little bit of research. Some general info before the answers to the questions as I believe OP hasn't understood fully what TensorRT and ONNX optimizations happen during the conversion from PyTorch format.
Both conversions, Pytorch to ONNX and ONNX to TensorRT increase the performance of the model by using several different optimizations. The tools actually print you information about what they do if you choose the verbose flag for them.
The preferred way to convert a Pytorch model to TensorRT is to use Torch-TensorRT as explained here.
TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU.
ONNX runtime offers mostly graph optimizations such as graph simplifications and node fusions to improve performance.
1. Does the number of parameters change when converting a PyTorch model to ONNX or TensorRT?
No: even though the layers are fused the number of parameters does not decrease unless there are some redundant branches in the model.
I tested this by downloading the yolov5s.onnx model here. The original model has 7.2M parameters according to the repository authors. Then I used this tool to count the number of parameters in the yolov5.onnx model and got 7225917 as a result. Thus, onnx conversion did not reduce the amount of parameters.
I was not able to get as elaborate information for TensorRT model but you can get layer information using trtexec. There is a recent question about this but there are no answers yet.
2. Does the number of FLOPS change when converting a PyTorch model to ONNX or TensorRT?
According to this post, no.
I know that since some of new versions of Pytorch (I used 1.8 and it worked for me) there are some fusions of batch norm layers and convolutions while saving model. I'm not sure about ONNX, but TensorRT actively uses horizontal and vertical fusion of different layers, so final model would be computational cheaper, than model that you initialized.
I am trying to perform binary segmentation on a custom dataset (DAGM dataset in my case Link to the dataset
I was just curious to know if pretrained networks on the imagenet dataset like VGG,Resnet will be of any particular use as I am not trying to segment objects like cats,dogs etc but anomalies in the images.
Normally you would want to fine tune a model on your new dataset which was previously trained and tuned on a similar problem. Neural networks extract features from samples and use those features to classify. If you have previously trained your network on biomedical dataset, then it has learned how to extract features from those models. So try to find a model that was trained on similar domain.
Also you can check the below link for more insight about the issue.
https://en.wikipedia.org/wiki/Catastrophic_interference