Source code for torchgeo.trainers.classification
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Classification tasks."""
import os
from typing import Any, Dict, cast
import pytorch_lightning as pl
import timm
import torch
import torch.nn as nn
from segmentation_models_pytorch.losses import FocalLoss, JaccardLoss
from torch import Tensor
from torch.nn.modules import Conv2d, Linear
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchmetrics import Accuracy, FBetaScore, JaccardIndex, MetricCollection
from ..datasets.utils import unbind_samples
from . import utils
# https://github.com/pytorch/pytorch/issues/60979
# https://github.com/pytorch/pytorch/pull/61045
Conv2d.__module__ = "nn.Conv2d"
Linear.__module__ = "nn.Linear"
class ClassificationTask(pl.LightningModule):
"""LightningModule for image classification."""
[docs] def config_model(self) -> None:
"""Configures the model based on kwargs parameters passed to the constructor."""
in_channels = self.hyperparams["in_channels"]
classification_model = self.hyperparams["classification_model"]
imagenet_pretrained = False
custom_pretrained = False
if self.hyperparams["weights"] and not os.path.exists(
self.hyperparams["weights"]
):
if self.hyperparams["weights"] not in ["imagenet", "random"]:
raise ValueError(
f"Weight type '{self.hyperparams['weights']}' is not valid."
)
else:
imagenet_pretrained = self.hyperparams["weights"] == "imagenet"
custom_pretrained = False
else:
custom_pretrained = True
# Create the model
valid_models = timm.list_models(pretrained=True)
if classification_model in valid_models:
self.model = timm.create_model(
classification_model,
num_classes=self.hyperparams["num_classes"],
in_chans=in_channels,
pretrained=imagenet_pretrained,
)
else:
raise ValueError(
f"Model type '{classification_model}' is not a valid timm model."
)
if custom_pretrained:
name, state_dict = utils.extract_encoder(self.hyperparams["weights"])
if self.hyperparams["classification_model"] != name:
raise ValueError(
f"Trying to load {name} weights into a "
f"{self.hyperparams['classification_model']}"
)
self.model = utils.load_state_dict(self.model, state_dict)
[docs] def config_task(self) -> None:
"""Configures the task based on kwargs parameters passed to the constructor."""
self.config_model()
if self.hyperparams["loss"] == "ce":
self.loss: nn.Module = nn.CrossEntropyLoss()
elif self.hyperparams["loss"] == "jaccard":
self.loss = JaccardLoss(mode="multiclass")
elif self.hyperparams["loss"] == "focal":
self.loss = FocalLoss(mode="multiclass", normalized=True)
else:
raise ValueError(f"Loss type '{self.hyperparams['loss']}' is not valid.")
[docs] def __init__(self, **kwargs: Any) -> None:
"""Initialize the LightningModule with a model and loss function.
Keyword Args:
classification_model: Name of the classification model use
loss: Name of the loss function
weights: Either "random", "imagenet_only", "imagenet_and_random", or
"random_rgb"
"""
super().__init__()
# Creates `self.hparams` from kwargs
self.save_hyperparameters() # type: ignore[operator]
self.hyperparams = cast(Dict[str, Any], self.hparams)
self.config_task()
self.train_metrics = MetricCollection(
{
"OverallAccuracy": Accuracy(
num_classes=self.hyperparams["num_classes"], average="micro"
),
"AverageAccuracy": Accuracy(
num_classes=self.hyperparams["num_classes"], average="macro"
),
"JaccardIndex": JaccardIndex(
num_classes=self.hyperparams["num_classes"]
),
"F1Score": FBetaScore(
num_classes=self.hyperparams["num_classes"],
beta=1.0,
average="micro",
),
},
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: input image
Returns:
prediction
"""
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"]
y_hat = self.forward(x)
y_hat_hard = y_hat.argmax(dim=1)
loss = self.loss(y_hat, y)
# by default, the train step logs every `log_every_n_steps` steps where
# `log_every_n_steps` is a parameter to the `Trainer` object
self.log("train_loss", loss, on_step=True, on_epoch=False)
self.train_metrics(y_hat_hard, y)
return cast(Tensor, loss)
[docs] def training_epoch_end(self, outputs: Any) -> None:
"""Logs epoch-level training metrics.
Args:
outputs: list of items returned by training_step
"""
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"]
y_hat = self.forward(x)
y_hat_hard = y_hat.argmax(dim=1)
loss = self.loss(y_hat, y)
self.log("val_loss", loss, on_step=False, on_epoch=True)
self.val_metrics(y_hat_hard, y)
if batch_idx < 10:
try:
datamodule = self.trainer.datamodule # type: ignore[attr-defined]
batch["prediction"] = y_hat_hard
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
)
except AttributeError:
pass
[docs] def validation_epoch_end(self, outputs: Any) -> None:
"""Logs epoch level validation metrics.
Args:
outputs: list of items returned by validation_step
"""
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"]
y_hat = self.forward(x)
y_hat_hard = y_hat.argmax(dim=1)
loss = self.loss(y_hat, y)
# by default, the test and validation steps only log per *epoch*
self.log("test_loss", loss, on_step=False, on_epoch=True)
self.test_metrics(y_hat_hard, y)
[docs] def test_epoch_end(self, outputs: Any) -> None:
"""Logs epoch level test metrics.
Args:
outputs: list of items returned by test_step
"""
self.log_dict(self.test_metrics.compute())
self.test_metrics.reset()
[docs] def configure_optimizers(self) -> Dict[str, Any]:
"""Initialize the optimizer and learning rate scheduler.
Returns:
a "lr dict" according to the pytorch lightning documentation --
https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#configure-optimizers
"""
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",
},
}
class MultiLabelClassificationTask(ClassificationTask):
"""LightningModule for multi-label image classification."""
[docs] def config_task(self) -> None:
"""Configures the task based on kwargs parameters passed to the constructor."""
self.config_model()
if self.hyperparams["loss"] == "bce":
self.loss = nn.BCEWithLogitsLoss()
else:
raise ValueError(f"Loss type '{self.hyperparams['loss']}' is not valid.")
[docs] def __init__(self, **kwargs: Any) -> None:
"""Initialize the LightningModule with a model and loss function.
Keyword Args:
classification_model: Name of the classification model use
loss: Name of the loss function
weights: Either "random", "imagenet_only", "imagenet_and_random", or
"random_rgb"
"""
super().__init__(**kwargs)
# Creates `self.hparams` from kwargs
self.save_hyperparameters() # type: ignore[operator]
self.hyperparams = cast(Dict[str, Any], self.hparams)
self.config_task()
self.train_metrics = MetricCollection(
{
"OverallAccuracy": Accuracy(
num_classes=self.hyperparams["num_classes"],
average="micro",
multiclass=False,
),
"AverageAccuracy": Accuracy(
num_classes=self.hyperparams["num_classes"],
average="macro",
multiclass=False,
),
"F1Score": FBetaScore(
num_classes=self.hyperparams["num_classes"],
beta=1.0,
average="micro",
multiclass=False,
),
},
prefix="train_",
)
self.val_metrics = self.train_metrics.clone(prefix="val_")
self.test_metrics = self.train_metrics.clone(prefix="test_")
[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"]
y_hat = self.forward(x)
y_hat_hard = torch.softmax(y_hat, dim=-1)
loss = self.loss(y_hat, y.to(torch.float))
# by default, the train step logs every `log_every_n_steps` steps where
# `log_every_n_steps` is a parameter to the `Trainer` object
self.log("train_loss", loss, on_step=True, on_epoch=False)
self.train_metrics(y_hat_hard, y)
return cast(Tensor, loss)
[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"]
y_hat = self.forward(x)
y_hat_hard = torch.softmax(y_hat, dim=-1)
loss = self.loss(y_hat, y.to(torch.float))
self.log("val_loss", loss, on_step=False, on_epoch=True)
self.val_metrics(y_hat_hard, y)
if batch_idx < 10:
try:
datamodule = self.trainer.datamodule # type: ignore[attr-defined]
batch["prediction"] = y_hat_hard
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
)
except AttributeError:
pass
[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"]
y_hat = self.forward(x)
y_hat_hard = torch.softmax(y_hat, dim=-1)
loss = self.loss(y_hat, y.to(torch.float))
# by default, the test and validation steps only log per *epoch*
self.log("test_loss", loss, on_step=False, on_epoch=True)
self.test_metrics(y_hat_hard, y)
