Evaluate for Runner

🚀 Feature Request

The evaluate_loader method for Python API. Similar to .train and .predict_loader

Motivation

Proposal

Possible use case

import os
from torch import nn, optim
from torch.utils.data import DataLoader
from catalyst import dl, utils
from catalyst.data.transforms 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
    ),
}

runner = dl.SupervisedRunner(
    input_key="features", output_key="logits", target_key="targets", loss_key="loss"
)
# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=1,
    callbacks=[
        dl.AccuracyCallback(input_key="logits", target_key="targets", topk_args=(1, 3, 5)),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=10
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
        # catalyst[ml] required ``pip install catalyst[ml]``
        # dl.ConfusionMatrixCallback(input_key="logits", target_key="targets", num_classes=10),
    ],
    logdir="./logs",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    verbose=True,
    load_best_on_end=True,
)

loader_metrics = runner.evaluate_loader(
    loader=loaders["valid"]), 
    callbacks=[
        dl.AccuracyCallback(input_key="logits", target_key="targets", topk_args=(1, 3, 5)),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=10
        ),
    ])

Alternatives

The whole method could be easily done with the .train approach, but for a more user-friendly API – why should not we add a simplified alias?

Additional context

Checklist

• feature proposal description
• motivation
• extra proposal context / proposal alternatives review

FAQ

Please review the FAQ before submitting an issue:

• I have read the documentation and FAQ
• I have reviewed the minimal examples section
• I have checked the changelog for main framework updates
• I have read the contribution guide
• I have joined Catalyst slack (#__questions channel) for issue discussion

[Scitator]

btw, it would be also great if we could update our main Readme.md example with runner.evaluate_loader 😃 Looking forward to see your contribution 😉

[Scitator]

It looks good from my point of view 👍 The only thing, that would be definitely required: different test cases. For example, I am not sure we need to use CheckpointCallback (which is usually turned on if logdir is specified) to save our model. Or we should write proper docs to "Do not mess train and evaluate logdirs 😃 ", something like that.