I would like to study the effect of pre-trained model, so I want to test t5 model with and without pre-trained weights. Using pre-trained weights is straight forward, but I cannot figure out how to use the architecture of T5 from hugging face without the weights. I am using Hugging face with pytorch but open for different solution.
https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model
"Initializing with a config file does not load the weights associated with the model, only the configuration."
for without weights create a T5Model with config file
from transformers import AutoConfig
from transformers import T5Tokenizer, T5Model
model_name = "t5-small"
config = AutoConfig.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5Model.from_pretrained(model_name)
model_raw = T5Model(config)
Related
I hope you are well I have a question can I train transfer learning like pre-trained model 'vgg16' trained on ImageNet in my customer data and save weight to train another customer data?
How I can do this, please
Thanks for ur time
I do example with Pytorch framework.
When you train any model, you have to define training strategy.
There are two ways to save and load trained weight for transfer learning in Pytorch.
The first is load state dict - only save and load the weight (paramemters) (recommended).
While training, if condition you defined satisfied, let's save the trained weight by this command (refer link)
torch.save(model.state_dict(), PATH)
And then when you train VGG16 model with other custom dataset and want to transfer learning from previous trained weight, use this
model = VGG16(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()
The second way is load the whole model (model structure included)
To save model, use (refer link)
torch.save(model, PATH)
And to load it
# Model class must be defined somewhere
model = torch.load(PATH)
model.eval()
You can refer some links [1], [2]
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?
I'm quite new to transfer learning. I'm planning to do it using YOLO for a CNN supervised regression task. Given an image, predict the number of times it will be viewed. I'm inclined on using YOLO as it is an object detector. Highly viewed photos mostly contain objects(face, animals, text, etc) that are classes that are in the COCO dataset where YOLO was originally trained.
I already tried using pretrained CNN models(VGGNet, MobileNet, etc.) with frozen weights but the results are not good. The option to fine tune the pretrained models are impossible since I don't have the computational resources to train using 100K+ images for x epochs just to create a good model for my problem.
YOLOuses Darknet as a CNN backbone/feature extractor. Therefore, you may want to try a pre-trained Darknet as a feature extractor and replace the classifier with your regressor. Standard YOLO uses Darknet-53 while Tiny YOLO uses Darknet-19.
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.
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.