Model debugging

Pipeline debugging

To check pipeline correctness, that everything is working correctly and does not throws any error, we suggest to use CheckRunCallback. You could find more information about it here <../early_stopping.rst>.

To check model convergence withing pipeline, we suggest to use BatchOverfitCallback. You could find more information about it here <../data.rst>.

Python debugging

For python debugging we suggest to use ipdb. You could install it with:

pip install ipdb

After that you could stop the pipeline in the place you prefer, for example:

import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(
        MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
    ),
    "valid": DataLoader(
        MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
    ),
}

class CustomRunner(dl.Runner):

    def predict_batch(self, batch):
        # model inference step
        return self.model(batch[0].to(self.device).view(batch[0].size(0), -1))

    def handle_batch(self, batch):
        # model train/valid step
        x, y = batch
        y_hat = self.model(x.view(x.size(0), -1))

        # let's stop before metric computation, but after model forward pass
        import ipdb; ipdb.set_trace()
        # <--- we will stop here --->
        loss = F.cross_entropy(y_hat, y)
        accuracy01, accuracy03 = metrics.accuracy(y_hat, y, topk=(1, 3))
        self.batch_metrics.update(
            {"loss": loss, "accuracy01": accuracy01, "accuracy03": accuracy03}
        )


        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

runner = CustomRunner()
# model training
runner.train(
    model=model,
    optimizer=optimizer,
    loaders=loaders,
    logdir="./logs",
    num_epochs=5,
    verbose=True,
    load_best_on_end=True,
)

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