Inference

To use your model in the inference mode, you could redefine the Runner.predict_batch.

Suppose you have the following classification pipeline:

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

model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
criterion = nn.CrossEntropyLoss()
optimizer = 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):

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

    def on_loader_start(self, runner):
        super().on_loader_start(runner)
        self.meters = {
            key: metrics.AdditiveValueMetric(compute_on_call=False)
            for key in ["loss", "accuracy01", "accuracy03"]
        }

    def handle_batch(self, batch):
        # model train/valid step
        # unpack the batch
        x, y = batch
        # run model forward pass
        logits = self.model(x)
        # compute the loss
        loss = self.criterion(logits, y)
        # compute other metrics of interest
        accuracy01, accuracy03 = metrics.accuracy(logits, y, topk=(1, 3))
        # log metrics
        self.batch_metrics.update(
            {"loss": loss, "accuracy01": accuracy01, "accuracy03": accuracy03}
        )
        for key in ["loss", "accuracy01", "accuracy03"]:
            self.meters[key].update(self.batch_metrics[key].item(), self.batch_size)
        # run model backward pass
        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

    def on_loader_end(self, runner):
        for key in ["loss", "accuracy01", "accuracy03"]:
            self.loader_metrics[key] = self.meters[key].compute()[0]
        super().on_loader_end(runner)

runner = CustomRunner()
# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    logdir="./logs",
    num_epochs=5,
    verbose=True,
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
)

Now you could easily predict your data with the Runner-specified logic.

Predict batch

If you want to predict one batch:

batch_prediciton = runner.predict_batch(next(iter(loaders["valid"])))
# which would be the same with
batch_model_prediciton = model(next(iter(loaders["valid"]))[0])
batch_prediciton == batch_model_prediciton
>>> True

You could also check out the example above in this Google Colab notebook.

Predict loader

If you want to predict entire loader:

for prediction in runner.predict_loader(loader=loaders["valid"]):
    assert prediction.detach().cpu().numpy().shape[-1] == 10

The runner.predict_loader method just iteratively goes through the loader batches, makes model predictions and yields the results.

You could also check out the example above in this Google Colab notebook.

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