I am trying to use Linear class of Pytorch module.In this automatically forward method is called once I create an instance of the class.Can anyone explain me from basics (If I can get code of Linear class of pytorch that would be great)
from torch.nn mport Linear
model=Linear(in_features=1,out_features=1)
x=torch.tensor([1])
y=model(x)
I am not able to understand how model(x) displays the result.As per my understanding model.forward() should display the actual result
It would be great if someone explain me from basics (Would be great if someone can share the link of code of Linear class in pytorch)
In the source, you'll see that the __call__ method of any Module in turn calls the forward method, so calling model(x) in your case is kind of like calling model.forward(x), but not exactly, due to the reasons explained here.
If you're not familiar with the __call__ method, just know roughly that it makes an object of a class callable.
This same question is answered in this Pytorch forum post.
Related
I have a loss function which includes several contributions, i.e.
L=L1+L2+... .
I am in particular interested in the individual development of L1,L2... on both the training and validation data set during learning.
If I generate my model via subclassing (and Functional API) and perform the training via model.fit(), how can I add the validation losses maybe called "val_L1", "Val_L2"... to the History-Object?
Thanks for any help
I figured it out by myself. I hope this will help someone in future who is also struggling with this issue.
If you define your customized model as the subclass of tf.keras.Model you have to use functions "train_step" and "test_step" via
def train_step (....): and def test_step (...):.
"train_step" is the function which is used to describe the training procedure according model.fit().
if both functions return:
return {'L1':L1, 'L2':L2}
the history-object will contain automatically 'val_L1' and 'val_L2'
I have a quantized model in pytorch and now I want to extract the parameter of the quantized linear layer and implement the forward manually.
I search the source code but only find this function.
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.ops.quantized.linear(
x, self._packed_params._packed_params, self.scale, self.zero_point)
But no where I can find how torch.ops.quantized.linear is defined.
Can someone give me a hind how the forward of quantized linear are defined?
In answer to the question of where torch.ops.quantized.linear is, I was looking for the same thing but was never able to find it. I believe it's probably somewhere in the aten (C++ namespace). I did, however, find some useful PyTorch-based implementations in the NVIDIA TensorRT repo below. It's quite possible these are the ones actually called by PyTorch via some DLLs. If you're trying to add quantization to a custom layer, these implementations walk you through it.
You can find the docs here and the GitHub page here.
For the linear layer specifically, see the QuantLinear layer here
Under the hood, this calls TensorQuantFunction.apply() for post-training quantization or FakeTensorQuantFunction.apply() for quantization-aware training.
I'm trying to use a regression model I have implemented in combination with the GridSearchCV class of scikit-learn to optimize the hyper-parameters of my model. My modelclass is nicely build following the suggestions of the scikit-api:
class FOO(BaseEstimator, RegressorMixin):
def __init__(self,...)
*** initialisation of all the parameters and hyperparameters (including the kernelfunction)***
def fit(self,X,y)
*** implementation of fit: just takes input and performs fit of parameters.
def predict(self,X)
*** implementation of predict: just takes input and calculates the result
The regression-class works as it should, but strangely enough, when I study the behavior of the hyperparameters, I tend to get inconsistencies. It appears one hyper-parameter is correctly applied by GridSearchCV, but the other one is clearly not.
So I am wondering, can someone explain to me how gridsearchCV is working (from the technical perspective)? How does it initialise the estimator, how does it run it over the grid?
My current assumption of the workings and required use of GridsearchCV is this:
Create a GridSearchCV instance (CVmodel=GridSearchCV(MyRegressor,param_grid=Myparamgrid,...)
Fit the hyperparameter(s) via: CVmodel.fit(X,y). Which naively would work like this:
> Loop over Parameter-values
> - create esimator instance with parameter value(and defaults for the other params)
> - estimator.fit
> - result[parameter-value]=estimator.predict
However, experience shows me this naive idea is quite wrong, as the hyper-parameter associated with the kernel-function of my regressor is not correctly initialized.
Can anyone provide some insight into what GridSearchCV is truly doing?
After quite some digging I discovered, scikit-learn does not create new instances (as would be expected in OOP) but rather updates the properties of the object via the set_params method. In my case, this worked fine for the hyperparameter which is directly defined by the same keyword in the __ init __ method, however, it breaks down when the hyperparameter is a property of the static method set during the __ init __ method. Overriding the set_params method (which many tutorials advise against) to deal with this fixes the problem.
For those interested in more details, I wrote this all up in a tutorial myself.
I know I can save a model by torch.save(model.state_dict(), FILE) or torch.save(model, FILE). But both of them don't save the architecture of model.
So how can we save the architecture of a model in PyTorch like creating a .pb file in Tensorflow ? I want to apply different tweaks to my model. Do I have any better way than copying the whole class definition every time and creating a new class if I can't save the architecture of a model?
You can refer to this article to understand how to save the classifier. To make a tweaks to a model, what you can do is create a new model which is a child of the existing model.
class newModel( oldModelClass):
def __init__(self):
super(newModel, self).__init__()
With this setup, newModel has all the layers as well as the forward function of oldModelClass. If you need to make tweaks, you can define new layers in the __init__ function and then write a new forward function to define it.
Saving all the parameters (state_dict) and all the Modules is not enough, since there are operations that manipulates the tensors, but are only reflected in the actual code of the specific implementation (e.g., reshapeing in ResNet).
Furthermore, the network might not have a fixed and pre-determined compute graph: You can think of a network that has branching or a loop (recurrence).
Therefore, you must save the actual code.
Alternatively, if there are no branches/loops in the net, you may save the computation graph, see, e.g., this post.
You should also consider exporting your model using onnx and have a representation that captures both the trained weights as well as the computation graph.
Regarding the actual question:
So how can we save the architecture of a model in PyTorch like creating a .pb file in Tensorflow ?
The answer is: You cannot
Is there any way to load a trained model without declaring the class definition before ?
I want the model architecture as well as parameters to be loaded.
no, you have to load the class definition before, this is a python pickling limitation.
https://discuss.pytorch.org/t/how-to-save-load-torch-models/718/11
Though, there are other options (probably you have already seen most of those) that are listed at this PyTorch post:
https://pytorch.org/tutorials/beginner/saving_loading_models.html
PyTorch's way of serializing a model for inference is to use torch.jit to compile the model to TorchScript.
PyTorch's TorchScript supports more advanced control flows than TensorFlow, and thus the serialization can happen either through tracing (torch.jit.trace) or compiling the Python model code (torch.jit.script).
Great references:
Video which explains this: https://www.youtube.com/watch?app=desktop&v=2awmrMRf0dA
Documentation: https://pytorch.org/docs/stable/jit.html
I would like to use PyTorch as a scientific computation package. It has much to recommend it in that respect - its Tensors are basically GPU-accelerated numpy arrays, and its autograd mechanism is potentially useful for a lot of things besides neural networks.
However, the available tutorials and documentation seem strongly geared towards quickly getting people up and running using it for machine learning. Although there is lots of good information available on the Tensor and Variable classes (and I understand that material reasonably well), the nn and optim packages always seem to be introduced by example rather than by explaining the API, which makes it hard to figure out exactly what's going on.
My main question at this point is whether I can use the optim package without also using the nn package, and if so how to do so. Of course I can always implement my simulations as subclasses of nn.Module even though they are not neural networks, but I would like to understand what happens under the hood when I do this, and what benefits/drawbacks it would give for my particular application.
More broadly, I would appreciate pointers to any resource that gives more of a logical overview of the API (for nn and optim specifically), rather than just presenting examples.
This is a partial self-answer to the specific question about using optim without using nn. The answer is, yes, you can do that. In fact, from looking at the source code, the optim package doesn't know anything about nn and only cares about Variables and tensors.
The documentation gives the following incomplete example:
optimizer = optim.Adam([var1, var2], lr = 0.0001)
and then later:
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
The function model isn't defined anywhere and looks like it might be something to do with nn, but in fact it can just be a Python function that computes output from input using var1 and var2 as parameters, as long as all the intermediate steps are done using Variables so that it can be differentiated. The call to optimizer.step() will update the values of var1 and var2 automatically.
In terms of the structure of PyTorch overall, it seems that optim and nn are independent of one another, with nn being basically just a convenient way to chain differentiable functions together, along with a library of such functions that are useful in machine learning. I would still appreciate pointers to a good technical overview of the whole package, though.