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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Tutorial - Migrating from Catalyst
Incrementally adding fastai goodness to your Catalyst training
Catalyst code
We're going to use the MNIST training code from Catalyst's README (as at August 2020), converted to a module.
Note
Step1:  To use it in fastai, we first convert the Catalyst dict into a DataLoaders object
Step2:  Using callbacks
In the Catalyst code, a training loop is defined manually, which is where the input tensor is flattened. In fastai, there's no need to define your own training loop - you can insert your own code into any part of the training process by using a callback, which can even modify data, gradients, the loss function, or anything else in the training loop
Step3:  The Catalyst example also modifies the training loop to add metrics, but you can pass these directly to your Learner in fastai
Step4:  You can now fit your model. fastai supports many schedulers. We recommend using 1cycle
Step5:  As you can see, migrating from Catalyst allowed us to replace 17 lines of code (in CustomRunner) with just 3 lines, and doesn't require you to change any of your existing data pipelines, optimizers, loss functions, models, etc. Once you've made this change, you can then benefit from fastai's rich set of callbacks, transforms, visualizations, and so forth.
Note that fastai is very different from Catalyst, in that it is much more than just a training loop (although we're only using the training loop in this example) - it is a complete framework including GPU-accelerated transformations, end-to-end inference, integrated applications for vision, text, tabular, and collaborative filtering, and so forth. You can use any part of the framework on its own, or combine them together, as described in the fastai paper.
Changing the model
Instead of using callbacks, in this case you can also simply change the model. Here we pull the view() out of the training loop, and into the model, using fastai's Flatten layer
Step6:  We can now create a Learner and train without using any callbacks
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Python Code:
from migrating_catalyst import *
Explanation: Tutorial - Migrating from Catalyst
Incrementally adding fastai goodness to your Catalyst training
Catalyst code
We're going to use the MNIST training code from Catalyst's README (as at August 2020), converted to a module.
Note: The source script for migrating_catalyst is in the examples subdirectory of this folder if you checked out the fastai repo from git, or can be downloaded from here if you're using an online viewer such as Colab.
End of explanation
data = DataLoaders(loaders['train'], loaders['valid']).cuda()
Explanation: To use it in fastai, we first convert the Catalyst dict into a DataLoaders object:
End of explanation
@before_batch_cb
def cb(self, xb, yb): return (xb[0].view(xb[0].size(0), -1),),yb
Explanation: Using callbacks
In the Catalyst code, a training loop is defined manually, which is where the input tensor is flattened. In fastai, there's no need to define your own training loop - you can insert your own code into any part of the training process by using a callback, which can even modify data, gradients, the loss function, or anything else in the training loop:
End of explanation
metrics=[accuracy,top_k_accuracy]
learn = Learner(data, model, loss_func=F.cross_entropy, opt_func=Adam,
                metrics=metrics, cbs=cb)
Explanation: The Catalyst example also modifies the training loop to add metrics, but you can pass these directly to your Learner in fastai:
End of explanation
learn.fit_one_cycle(1, 0.02)
Explanation: You can now fit your model. fastai supports many schedulers. We recommend using 1cycle:
End of explanation
model = nn.Sequential(
    Flatten(),
    torch.nn.Linear(28 * 28, 10))
Explanation: As you can see, migrating from Catalyst allowed us to replace 17 lines of code (in CustomRunner) with just 3 lines, and doesn't require you to change any of your existing data pipelines, optimizers, loss functions, models, etc. Once you've made this change, you can then benefit from fastai's rich set of callbacks, transforms, visualizations, and so forth.
Note that fastai is very different from Catalyst, in that it is much more than just a training loop (although we're only using the training loop in this example) - it is a complete framework including GPU-accelerated transformations, end-to-end inference, integrated applications for vision, text, tabular, and collaborative filtering, and so forth. You can use any part of the framework on its own, or combine them together, as described in the fastai paper.
Changing the model
Instead of using callbacks, in this case you can also simply change the model. Here we pull the view() out of the training loop, and into the model, using fastai's Flatten layer:
End of explanation
learn = Learner(data, model, loss_func=F.cross_entropy, opt_func=Adam, metrics=metrics)
learn.fit_one_cycle(1, 0.02)
Explanation: We can now create a Learner and train without using any callbacks:
End of explanation
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