Introduction to PyTorch Model
Python class represents the model where it is taken from the module with atleast two parameters defined in the program which we call as PyTorch Model. One model will have other models or attributes of other models in the same network which represents other parameters as well. Whole model should be called for each computation and predictions of the output results. An iterator is retrieved over all the parameters of the model.
What is PyTorch Model?
A model with different parameters in the same module and the same dataset where the data is from tensors or CUDA from which we can create different iterators is called PyTorch Model. We can set the model to a training model which does not train the model as such but will set the dataset to different methods of dropout and others. We can create a linear or nested model based on our requirements and manage the model with the parameters.
PyTorch Model Overviews
The initial step is to prepare the model where input and output data will be numerical. We can use Python libraries to load the data and PyTorch to customize the dataset. Also, any transforms can be done to the dataset using scaling or encoding activities. A DataLoader class is provided that helps in navigating the dataset while the training and evaluation of the model happen.
We must define the model as well where the class is defined with different layers such as linear, Conv2d, and maxpool2d layers. Activation functions can also be used to define the models. The next step is to train the model with loss function and optimization function. We have to clear the last errors followed by a forward pass of input to the model. The next step is to calculate the loss of the function and we have to check for errors in the function by backpropagation. We must update the model so that we can reduce the loss in the function.
We should evaluate the model using the test dataset. Again, DataLoader is useful here where we can collect the predictions and a performance metric can be calculated. This model can be used for the next set of predictions of new data. Hence, PyTorch tensor is used to wrap the dataset where we can do differentiation tasks along with NumPy functions.
Use PyTorch Model
We can use the PyTorch model for new predictions from the old model so that a new dataset can be created. It is good to import NumPy and matplotlib while doing predictions in the model. If NumPy is imported, we should disable grad or else numpy will not work and may give inconsistent results. The collected dataset should be checked thoroughly and the information should be specified such as index, and item loads. We can split the data into image and target. The next step is to generate the prediction where mlp instance is fed into the module and we can name it according to the neural network in the module.
The prediction gives a list of probabilities by Softmax and hence this must be converted to a class with np. argmax. The highest probable value is taken into consideration here. The next step is to reshape the model image where matplotlib can be used to check the visualization of the model. Now, visualization is completed and the prediction can be set where the actual target is considered as the title. The saved model can also be considered for practice.
Load a pre-trained Model
We have to create the instance of the network where an argument should be passed. We should note that the keys of state_dict given as input to the model should exactly be the same as keys of state_dict output of the model. KeyError will be raised if the names and keys are not present in the new model.
def my_keys_dict(self, state_dict):
model_keys = self.state_dict()
for key, parameters in state_dict.items():
if key not in model_keys:
continue
if instancegiven(parameters, values):
parameters = parameters.data
model_keys[key].copy_(parameters)
If needed, we can remove all the keys while loading the model and then load the weights once the model is loaded.
pretrained_model = ...
stat_dict = model.state_dict()
pretrained_model = {a: i for a, i in pretrained_model.items() if a in stat_dict}
stat_dict.update(pretrained_model)
model.load_state_dict(pretrained_model)
We can also restore the weights in the model as per the previous model.
self.dict = models.__stat.dict__[args.arch](pretrained = False,
numbered_classes = args.classes,
auxiliary_logits = False)
if self. args.pretrained:
print("=> Pretrained model '{}'".format(args.arch))
pretrained_dict = model_zoo.load_url(model [args.arch])
model = self._model._dict()
pretrained_state = { a:i for a,i in pretrained_state.iteritems() if a in model_state and i.size() == model_state[a].size() }
model_state.update(pretrained_state)
self._model.load_state_dict(model_state)
We can use model.named_parameters() where a key name is returned along with the corresponding parameter name. This helps in identifying the parameter along with the dictionary stats.
PyTorch Model – Load the entire model
We should save the model first before loading the same. We can use the following command to save the model.
Torch.save(modelname, path_where_model_is_saved)
We can load the model with simple command.
Modelname = torch.load(path_where_model_is_saved)
Model.eval()
This method helps to save the model with the least code and we can save the entire Python module using this code. When we save the code using this method, data is stored in a serialized manner and directory structure is used in the code. Model class is not saved as normal methods, but the path where the model is stored is saved and this makes certain changes in the model when the same model is used in other classes.
We can use the file extension .pt or .pth to save the model. It is important to call a model. eval() where dropout and normalization layers are set so that the model will be set to evaluation mode. Inconsistent results will be received from the output end if we do not do this step.
Conclusion
We should save the dictionary and an optimizer so that we can resume the training of the model in any later point. Models and optimizers should be initialized following the dictionary of the model. PyTorch model is very important for the entire network and it is necessary to know the basic steps in the model.
Recommended Articles
This is a guide to PyTorch Model. Here we discuss Introduction, overview, What is PyTorch Model is, Examples along with the codes and outputs. You may also have a look at the following articles to learn more –
