A model evaluator is for training and validating each generated model. They are necessary to evaluate the performance of new explored models.
Customize Evaluator with Any Function¶
The simplest way to customize a new evaluator is with
FunctionalEvaluator, which is very easy when training code is already available. Users only need to write a fit function that wraps everything, which usually includes training, validating and testing of a single model. This function takes one positional arguments (
model) and possible keyword arguments. The keyword arguments (other than
model) are fed to
FunctionalEvaluator as its initialization parameters (note that they will be serialized). In this way, users get everything under their control, but expose less information to the framework and as a result, further optimizations like CGO might be not feasible. An example is as belows:
from nni.nas.evaluator import FunctionalEvaluator from nni.nas.experiment import NasExperiment def fit(model, dataloader): train(model, dataloader) acc = test(model, dataloader) nni.report_final_result(acc) # The dataloader will be serialized, thus ``nni.trace`` is needed here. # See serialization tutorial for more details. evaluator = FunctionalEvaluator(fit, dataloader=nni.trace(DataLoader)(foo, bar)) experiment = NasExperiment(base_model, lightning, strategy)
Different from the legacy Retiarii FunctionEvaluator, the new FunctionalEvaluator now accepts model instance as the first argument, rather than
model_cls. This makes it more intuitive and easier to use.
When using customized evaluators, if you want to visualize models, you need to export your model and save it into
$NNI_OUTPUT_DIR/model.onnx in your evaluator. An example here:
def fit(model): onnx_path = Path(os.environ.get('NNI_OUTPUT_DIR', '.')) / 'model.onnx' onnx_path.parent.mkdir(exist_ok=True) dummy_input = torch.randn(10, 3, 224, 224) torch.onnx.export(model, dummy_input, onnx_path) # the rest of training code here
If the conversion is successful, the model will be able to be visualized with powerful tools Netron.
Use Evaluators to Train and Evaluate Models¶
Users can use evaluators to train or evaluate a single, concrete architecture. This is very useful when:
Debugging your evaluator against a baseline model.
Fully train, validate and test your model after the search process is complete.
The usage is shown below:
# Class definition of a model space, for example, ResNet. class MyModelSpace(ModelSpace): ... # Mock a model instance from nni.nas.space import RawFormatModelSpace model_container = RawFormatModelSpace.from_model(MyModelSpace()) # Randomly sample a model model = model_container.random() # Mock a runtime so that `nni.get_next_parameter` and `nni.report_xxx_result` will work. with evaluator.mock_runtime(model): evaluator.evaluate(model.executable_model())
The underlying implementation of
evaluate() depends on concrete evaluator that you used.
For example, if
FunctionalEvaluator is used, it will run your customized fit function.
If lightning evaluators like
nni.nas.evaluator.pytorch.Classification are used, it will invoke the
trainer.fit() of Lightning.
To evaluate an architecture that is exported from experiment (i.e., from
nni.nas.space.model_context() to instantiate the exported model:
with model_context(exported_model_dict): model = MyModelSpace() # Then use evaluator.evaluate evaluator.evaluate(model)
Another way of doing this is probably using
freeze API. It will also preserve the weights at best effort if the model space has been mutated by one-shot strategies:
Evaluators with PyTorch-Lightning¶
Use Built-in Evaluators¶
NNI provides some commonly used model evaluators for users' convenience. These evaluators are built upon the awesome library PyTorch-Lightning. Read the reference for their detailed usages.
nni.nas.evaluator.pytorch.Classification: for classification tasks.
nni.nas.evaluator.pytorch.Regression: for regression tasks.
We recommend to read the serialization tutorial before using these evaluators. A few notes to summarize the tutorial:
nni.nas.evaluator.pytorch.DataLoadershould be used in place of
The datasets used in data-loader should be decorated with
import nni.nas.evaluator.pytorch.lightning as pl from torchvision import transforms transform = nni.trace(transforms.Compose, [nni.trace(transforms.ToTensor()), nni.trace(transforms.Normalize, (0.1307,), (0.3081,))]) train_dataset = nni.trace(MNIST, root='data/mnist', train=True, download=True, transform=transform) test_dataset = nni.trace(MNIST, root='data/mnist', train=False, download=True, transform=transform) # pl.DataLoader and pl.Classification is already traced and supports serialization. evaluator = pl.Classification(train_dataloaders=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100), max_epochs=10)
Customize Evaluator with PyTorch-Lightning¶
Another approach is to write training code in PyTorch-Lightning style, that is, to write a LightningModule that defines all elements needed for training (e.g., loss function, optimizer) and to define a trainer that takes (optional) dataloaders to execute the training. Before that, please read the document of PyTorch-lightning to learn the basic concepts and components provided by PyTorch-lightning.
In practice, writing a new training module in nas should inherit
nni.nas.evaluator.pytorch.LightningModule, which has a
set_model that will be called after
__init__ to save the candidate model (generated by strategy) as
self.model. The rest of the process (like
training_step) should be the same as writing any other lightning module. Evaluators should also communicate with strategies via two API calls (
nni.report_intermediate_result() for periodical metrics and
nni.report_final_result() for final metrics), added in
An example is as follows:
from nni.nas.evaluator.pytorch.lightning import LightningModule # please import this one @nni.trace class AutoEncoder(LightningModule): def __init__(self): super().__init__() self.decoder = nn.Sequential( nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28*28) ) def forward(self, x): embedding = self.model(x) # let's search for encoder return embedding def training_step(self, batch, batch_idx): # training_step defined the train loop. # It is independent of forward x, y = batch x = x.view(x.size(0), -1) z = self.model(x) # model is the one that is searched for x_hat = self.decoder(z) loss = F.mse_loss(x_hat, x) # Logging to TensorBoard by default self.log('train_loss', loss) return loss def validation_step(self, batch, batch_idx): x, y = batch x = x.view(x.size(0), -1) z = self.model(x) x_hat = self.decoder(z) loss = F.mse_loss(x_hat, x) self.log('val_loss', loss) def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) return optimizer def on_validation_epoch_end(self): nni.report_intermediate_result(self.trainer.callback_metrics['val_loss'].item()) def teardown(self, stage): if stage == 'fit': nni.report_final_result(self.trainer.callback_metrics['val_loss'].item())
If you are trying to use your customized evaluator with one-shot strategy, bear in mind that your defined methods will be reassembled into another LightningModule, which might result in extra constraints when writing the LightningModule. For example, your validation step could appear else where (e.g., in
training_step). This prohibits you from returning arbitrary object in
Then, users need to wrap everything (including LightningModule, trainer and dataloaders) into a
nni.nas.evaluator.pytorch.Lightning object, and pass this object into a nas experiment.
import nni.nas.evaluator.pytorch.lightning as pl from nni.nas.experiment import NasExperiment lightning = pl.Lightning(AutoEncoder(), pl.Trainer(max_epochs=10), train_dataloaders=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100)) experiment = NasExperiment(base_model, lightning, strategy)