How to Serving caffe models?
I'm doing a project on image classification using pre trained caffe model, I want to serve my model like TFServing,How to Serving caffe models?
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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 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.
I have met a problem that I train my model in pytorch 0.4.1, but I can't find a tool to convert it into caffe model.
How to use a pytorch 0.4.1 model to init pytorch 0.2.0?
Or how to convert a pytorch 0.4.1 model to a caffe model?
Assuming you are using Caffe2, you can use ONNX to convert models trained in one AI framework to another (with some limitations). In your case you need to export your PyTorch trained model to ONNX model and then import the ONNX model to Caffe2 model.
Follow these tutorials:
PyTorch to ONNX export: link
ONNX to Caffe2 import: link
The tutorials are pretty straightforward, you don't need to write much code. But there are some limitations while exporting your PyTorch model to ONNX.
Check this link for in-depth tutorial on Pytorch to ONNX.
Comment below if you have any doubts/ questions.
EDIT: Try this repo, I have tested it on a toy model it is working.
I am using pre-trained and fine-tuned models for cell classification. The images were not seen before by any of the transfer learning models. In the process, I found that a fine-tuned VGG19 model outperforms the fine-tuned Inception-V3 mode though both these models are trained on the ImageNet data. What contributes to this difference? is it because of the model architecture?
Newbie to Caffe.
I am trying to use the trained Convolutional neural network on MNIST dataset using Caffe deep learning framework.
Following the official tutorial.
Steps taken successfully:
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
Model was trained and stopped with the following message:
I1203 solver.cpp:133] Snapshotting solver state to lenet_iter_10000.solverstate
I1203 solver.cpp:78] Optimization Done.
Now, I am not sure as how to get a testing image and use the existing trained model which I believe has been snapshot by the name lenet_iter_10000.solverstate to see the predicted scores for each class.
Use the test function of caffe:
<path to caffe root>/caffe test -model <val filename>.prototxt -weights lenet_iter_10000.caffemodel
As you want to test only one image, give that image as input to your test data layer. Use the mean_image as input as well in your <val filename>.protoxt. Test batch size is 1 in this case.
Also note that lenet_iter_10000.solverstate is not your trained model. Your trained model is actually lenet_iter_10000.caffemodel. To know about the diffrence between solverstate and caffemodel files see here.