Source code for torchgeo.trainers.regression
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Regression tasks."""
import os
from typing import Any, Dict, cast
import matplotlib.pyplot as plt
import timm
import torch
import torch.nn.functional as F
from lightning.pytorch import LightningModule
from torch import Tensor
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection
from torchvision.models._api import WeightsEnum
from ..datasets import unbind_samples
from ..models import get_weight
from . import utils
[docs]class RegressionTask(LightningModule): # type: ignore[misc]
"""LightningModule for training models on regression datasets.
Supports any available `Timm model
<https://huggingface.co/docs/timm/index>`_
as an architecture choice. To see a list of available
models, you can do:
.. code-block:: python
import timm
print(timm.list_models())
"""
[docs] def config_task(self) -> None:
"""Configures the task based on kwargs parameters."""
# Create model
weights = self.hyperparams["weights"]
self.model = timm.create_model(
self.hyperparams["model"],
num_classes=self.hyperparams["num_outputs"],
in_chans=self.hyperparams["in_channels"],
pretrained=weights is True,
)
# Load weights
if weights and weights is not True:
if isinstance(weights, WeightsEnum):
state_dict = weights.get_state_dict(progress=True)
elif os.path.exists(weights):
_, state_dict = utils.extract_backbone(weights)
else:
state_dict = get_weight(weights).get_state_dict(progress=True)
self.model = utils.load_state_dict(self.model, state_dict)
[docs] def __init__(self, **kwargs: Any) -> None:
"""Initialize a new LightningModule for training simple regression models.
Keyword Args:
model: Name of the timm model to use
weights: Either a weight enum, the string representation of a weight enum,
True for ImageNet weights, False or None for random weights,
or the path to a saved model state dict.
num_outputs: Number of prediction outputs
in_channels: Number of input channels to model
learning_rate: Learning rate for optimizer
learning_rate_schedule_patience: Patience for learning rate scheduler
.. versionchanged:: 0.4
Change regression model support from torchvision.models to timm
"""
super().__init__()
# Creates `self.hparams` from kwargs
self.save_hyperparameters()
self.hyperparams = cast(Dict[str, Any], self.hparams)
self.config_task()
self.train_metrics = MetricCollection(
{"RMSE": MeanSquaredError(squared=False), "MAE": MeanAbsoluteError()},
prefix="train_",
)
self.val_metrics = self.train_metrics.clone(prefix="val_")
self.test_metrics = self.train_metrics.clone(prefix="test_")
[docs] def forward(self, *args: Any, **kwargs: Any) -> Any:
"""Forward pass of the model.
Args:
x: tensor of data to run through the model
Returns:
output from the model
"""
return self.model(*args, **kwargs)
[docs] def training_step(self, *args: Any, **kwargs: Any) -> Tensor:
"""Compute and return the training loss.
Args:
batch: the output of your DataLoader
Returns:
training loss
"""
batch = args[0]
x = batch["image"]
y = batch["label"].view(-1, 1)
y_hat = self(x)
loss = F.mse_loss(y_hat, y)
self.log("train_loss", loss) # logging to TensorBoard
self.train_metrics(y_hat, y)
return loss
[docs] def on_train_epoch_end(self) -> None:
"""Logs epoch-level training metrics."""
self.log_dict(self.train_metrics.compute())
self.train_metrics.reset()
[docs] def validation_step(self, *args: Any, **kwargs: Any) -> None:
"""Compute validation loss and log example predictions.
Args:
batch: the output of your DataLoader
batch_idx: the index of this batch
"""
batch = args[0]
batch_idx = args[1]
x = batch["image"]
y = batch["label"].view(-1, 1)
y_hat = self(x)
loss = F.mse_loss(y_hat, y)
self.log("val_loss", loss)
self.val_metrics(y_hat, y)
if (
batch_idx < 10
and hasattr(self.trainer, "datamodule")
and self.logger
and hasattr(self.logger, "experiment")
and hasattr(self.logger.experiment, "add_figure")
):
try:
datamodule = self.trainer.datamodule
batch["prediction"] = y_hat
for key in ["image", "label", "prediction"]:
batch[key] = batch[key].cpu()
sample = unbind_samples(batch)[0]
fig = datamodule.plot(sample)
summary_writer = self.logger.experiment
summary_writer.add_figure(
f"image/{batch_idx}", fig, global_step=self.global_step
)
plt.close()
except ValueError:
pass
[docs] def on_validation_epoch_end(self) -> None:
"""Logs epoch level validation metrics."""
self.log_dict(self.val_metrics.compute())
self.val_metrics.reset()
[docs] def test_step(self, *args: Any, **kwargs: Any) -> None:
"""Compute test loss.
Args:
batch: the output of your DataLoader
"""
batch = args[0]
x = batch["image"]
y = batch["label"].view(-1, 1)
y_hat = self(x)
loss = F.mse_loss(y_hat, y)
self.log("test_loss", loss)
self.test_metrics(y_hat, y)
[docs] def on_test_epoch_end(self) -> None:
"""Logs epoch level test metrics."""
self.log_dict(self.test_metrics.compute())
self.test_metrics.reset()
[docs] def predict_step(self, *args: Any, **kwargs: Any) -> Tensor:
"""Compute and return the predictions.
Args:
batch: the output of your DataLoader
Returns:
predicted values
"""
batch = args[0]
x = batch["image"]
y_hat: Tensor = self(x)
return y_hat
[docs] def configure_optimizers(self) -> Dict[str, Any]:
"""Initialize the optimizer and learning rate scheduler.
Returns:
learning rate dictionary
"""
optimizer = torch.optim.AdamW(
self.model.parameters(), lr=self.hyperparams["learning_rate"]
)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": ReduceLROnPlateau(
optimizer,
patience=self.hyperparams["learning_rate_schedule_patience"],
),
"monitor": "val_loss",
},
}
