I’m new to pytorch. I see lots of tutorials that focus on how to use the API to train, but my question is, once I have a trained model, i have this 3 files :
pytorch_model.bin
config.json
preprocessor_config.json
my question is how i load a model and test some Picture on him
train_results = trainer.train()
trainer.save_model()
Related
I am trying to port two pre-trained keras models into the IPU machine. I managed to load and run them using IPUstrategy.scope but I dont know if i am doing it the right way. I have my pre-trained models in .h5 file format.
I load them this way:
def first_model():
model = tf.keras.models.load_model("./model1.h5")
return model
After searching your ipu.keras.models.py file I couldn't find any load methods to load my pre-trained models, and this is why i used tf.keras.models.load_model().
Then i use this code to run:
cfg=ipu.utils.create_ipu_config()
cfg=ipu.utils.auto_select_ipus(cfg, 1)
ipu.utils.configure_ipu_system(cfg)
ipu.utils.move_variable_initialization_to_cpu()
strategy = ipu.ipu_strategy.IPUStrategy()
with strategy.scope():
model = first_model()
print('compile attempt\n')
model.compile("sgd", "categorical_crossentropy", metrics=["accuracy"])
print('compilation completed\n')
print('running attempt\n')
res = model.predict(input_img)[0]
print('run completed\n')
you can see the output here:link
So i have some difficulties to understand how and if the system is working properly.
Basically the model.compile wont compile my model but when i use model.predict then the system first compiles and then is running. Why is that happening? Is there another way to run pre-trained keras models on an IPU chip?
Another question I have is if its possible to load a pre-trained keras model inside an ipu.keras.model and then use model.fit/evaluate to further train and evaluate it and then save it for future use?
One last question I have is about the compilation part of the graph. Is there a way to avoid recompilation of the graph every time i use the model.predict() in a different strategy.scope()?
I use tensorflow2.1.2 wheel
Thank you for your time
To add some context, the Graphcore TensorFlow wheel includes a port of Keras for the IPU, available as tensorflow.python.ipu.keras. You can access the API documentation for IPU Keras at this link. This module contains IPU-specific optimised replacement for TensorFlow Keras classes Model and Sequential, plus more high-performance, multi-IPU classes e.g. PipelineModel and PipelineSequential.
As per your specific issue, you are right when you mention that there are no IPU-specific ways to load pre-trained Keras models at present. I would encourage you, as you appear to have access to IPUs, to reach out to Graphcore Support. When doing so, please attach your pre-trained Keras model model1.h5 and a self-contained reproducer of your code.
Switching topic to the recompilation question: using an executable cache prevents recompilation, you can set that up with environmental variable TF_POPLAR_FLAGS='--executable_cache_path=./cache'. I'd also recommend to take a look into the following resources:
this tutorial gathers several considerations around recompilation and how to avoid it when using TensorFlow2 on the IPU.
Graphcore TensorFlow documentation here explains how to use the pre-compile mode on the IPU.
I am new in Pytorch. My question is: How do I apply transfer learning to a custom dataset? I am doing image segmentation on brain tumors. I can find examples which use U-net structure but I could not find examples using weights of the pre-trained models for a U-net image segmentation?
You could obtain pre-trained models in two ways:
Model weights or complete models shared in formats such .pt or .pth:
In this case, Saving and Loading Models is a good starting point. Copying from the tutorial there, you could load a model as
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
The other way is to load the model from torchvision. A list is available models is available at Torchvision Models. U-Net is not available yet. However, it is possible to load a pre-trained model as the encoder and write a separate decoder to form a U-Net with a pre-trained encoder.
In this case, the model object returned from the function calls shown in the API are already loaded with pretrained weights when pretrained=True.
For writing a custom dataloader, PyTorch data loaders may be a useful guide.
I am learning Pytorch and trying to understand how the library works for semantic segmentation.
What I've understood so far is that we can use a pre-trained model in pytorch. I've found an article which was using this model in the .eval() mode but I have not been able to find any tutorial on using such a model for training on our own dataset. I have a very small dataset and I need transfer learning to get results. My goal is to only train the FC layers with my own data. How is that achievable in Pytorch without complicating the code with OOP or so many .py files. I have been having a hard time figuring out such repos in github as I am not the most proficient person when it comes to OOP. I have been using Keras for Deep Learning until recently and there everything is easy and straightforward. Do I have the same options in Pycharm?
I appreciate any guidance on this. I need to run a piece of code that does the semantic segmentation and I am really confused about many of the steps I need to take.
Assume you start with a pretrained model called model. All of this occurs before you pass the model any data.
You want to find the layers you want to train by looking at all of them and then indexing them using model.children(). Running this command will show you all of the blocks and layers.
list(model.children())
Suppose you have now found the layers that you want to finetune (your FC layers as you describe). If the layers you want to train are the last 5 you can grab all of the layers except for the last 5 in order to set their requires_grad params to False so they don't train when you run the training algorithm.
list(model.children())[-5:]
Remove those layers:
layer_list = list(model.children())[-5:]
Rebuild model using sequential:
model_small = nn.Sequential(*list(model.children())[:-5])
Set requires_grad params to False:
for param in model_small.parameters():
param.requires_grad = False
Now you have a model called model_small that has all of the layers except the layers you want to train. Now you can reattach the layers that your removed and they will intrinsically have the requires_grad param set to True. Now when you train the model it will only update the weights on those layers.
model_small.avgpool_1 = nn.AdaptiveAvgPool2d()
model_small.lin1 = nn.Linear()
model_small.logits = nn.Linear()
model_small.softmax = nn.Softmax()
model = model_small.to(device)
I'm using faster_rcnn_resnet50 to train a model which will detect corrosions in images and I want to train a model from scratch instead of using transfer learning.
I don't know if this right but the reason I want to do this is that the already existing weights (which are trained on COCO) will affect my model trained on corrosion images.
One way I would like to do this is randomize or unfreeze the weights of the feature extractor on the resnet50 and then train the model on my images.
but there's no function or an option in the resnet50 config file to randomize or unfreeze weights.
I've made a new labelmap with a single label and tried it with transfer learning. It's working but I would like to have a model is trained just on my images and the previous weights shouldn't affect my predictions.
This is the first time I'm working with object detection and transfer learning. Will the weights of the pre-trained model on COCO affect my model which is trained on custom images of corrosion? How do you use tensorflow object-detection API without transfer learning?
I just completed the implementation of A Guide to TF Layers: Building a Convolutional Neural Network for the MNIST data set. The training model successfully ran and gave accuracy of 97.3%.
However, the tutorial does not mention how to use this new trained model to supply own images and see the predictions. Does anyone know how to use the output of the training model to make predictions? I see in the tmp/mnist_convnet_model$ folder, there are some output files like .pbtxt , meta files and index files. But I can't find instructions to use them for making predictions on my own images.
y_pred = tf.nn.softmax(your_final_layer)
y_pred_cls = tf.argmax(y_pred, dimension=1)
and for prediction
feed_dict = {x: [your_image]}
classification = tf.run(y_pred_cls, feed_dict)
print classification
This applies to just about any model you create