Remove logging of figures

tests/datamodules/test_fair1m.py

@@ -3,7 +3,6 @@
 
 import os
 
-import matplotlib.pyplot as plt
 import pytest
 
 from torchgeo.datamodules import FAIR1MDataModule
@@ -29,14 +28,3 @@ def test_val_dataloader(self, datamodule: FAIR1MDataModule) -> None:
     def test_predict_dataloader(self, datamodule: FAIR1MDataModule) -> None:
         datamodule.setup('predict')
         next(iter(datamodule.predict_dataloader()))
-
-    def test_plot(self, datamodule: FAIR1MDataModule) -> None:
-        datamodule.setup('validate')
-        batch = next(iter(datamodule.val_dataloader()))
-        sample = {
-            'image': batch['image'][0],
-            'boxes': batch['boxes'][0],
-            'label': batch['label'][0],
-        }
-        datamodule.plot(sample)
-        plt.close()

tests/datamodules/test_geo.py

@@ -3,12 +3,10 @@
 
 from typing import Any
 
-import matplotlib.pyplot as plt
 import pytest
 import torch
 from _pytest.fixtures import SubRequest
 from lightning.pytorch import Trainer
-from matplotlib.figure import Figure
 from rasterio.crs import CRS
 from torch import Tensor
 
@@ -34,9 +32,6 @@ def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
         image = torch.arange(3 * 2 * 2).view(3, 2, 2)
         return {'image': image, 'crs': CRS.from_epsg(4326), 'bounds': query}
 
-    def plot(self, *args: Any, **kwargs: Any) -> Figure:
-        return plt.figure()
-
 
 class CustomGeoDataModule(GeoDataModule):
     def __init__(self) -> None:
@@ -73,9 +68,6 @@ def __getitem__(self, index: int) -> dict[str, Tensor]:
     def __len__(self) -> int:
         return self.length
 
-    def plot(self, *args: Any, **kwargs: Any) -> Figure:
-        return plt.figure()
-
 
 class CustomNonGeoDataModule(NonGeoDataModule):
     def __init__(self) -> None:
@@ -133,11 +125,6 @@ def test_predict(self, datamodule: CustomGeoDataModule) -> None:
         batch = datamodule.transfer_batch_to_device(batch, torch.device('cpu'), 1)
         batch = datamodule.on_after_batch_transfer(batch, 0)
 
-    def test_plot(self, datamodule: CustomGeoDataModule) -> None:
-        datamodule.setup('validate')
-        datamodule.plot()
-        plt.close()
-
     def test_no_datasets(self) -> None:
         dm = CustomGeoDataModule()
         msg = r'CustomGeoDataModule\.setup must define one of '
@@ -235,11 +222,6 @@ def test_predict(self, datamodule: CustomNonGeoDataModule) -> None:
         batch = next(iter(datamodule.predict_dataloader()))
         batch = datamodule.on_after_batch_transfer(batch, 0)
 
-    def test_plot(self, datamodule: CustomNonGeoDataModule) -> None:
-        datamodule.setup('validate')
-        datamodule.plot()
-        plt.close()
-
     def test_no_datasets(self) -> None:
         dm = CustomNonGeoDataModule()
         msg = r'CustomNonGeoDataModule\.setup must define one of '

tests/datamodules/test_usavars.py

@@ -3,12 +3,10 @@
 
 import os
 
-import matplotlib.pyplot as plt
 import pytest
 from _pytest.fixtures import SubRequest
 
 from torchgeo.datamodules import USAVarsDataModule
-from torchgeo.datasets import unbind_samples
 
 
 class TestUSAVarsDataModule:
@@ -41,10 +39,3 @@ def test_test_dataloader(self, datamodule: USAVarsDataModule) -> None:
         assert len(datamodule.test_dataloader()) == 1
         batch = next(iter(datamodule.test_dataloader()))
         assert batch['image'].shape[0] == datamodule.batch_size
-
-    def test_plot(self, datamodule: USAVarsDataModule) -> None:
-        datamodule.setup('validate')
-        batch = next(iter(datamodule.val_dataloader()))
-        sample = unbind_samples(batch)[0]
-        datamodule.plot(sample)
-        plt.close()

tests/datamodules/test_xview2.py

@@ -3,11 +3,9 @@
 
 import os
 
-import matplotlib.pyplot as plt
 import pytest
 
 from torchgeo.datamodules import XView2DataModule
-from torchgeo.datasets import unbind_samples
 
 
 class TestXView2DataModule:
@@ -33,10 +31,3 @@ def test_val_dataloader(self, datamodule: XView2DataModule) -> None:
     def test_test_dataloader(self, datamodule: XView2DataModule) -> None:
         datamodule.setup('test')
         next(iter(datamodule.test_dataloader()))
-
-    def test_plot(self, datamodule: XView2DataModule) -> None:
-        datamodule.setup('validate')
-        batch = next(iter(datamodule.val_dataloader()))
-        sample = unbind_samples(batch)[0]
-        datamodule.plot(sample)
-        plt.close()

tests/trainers/test_classification.py

@@ -20,7 +20,7 @@
     EuroSATDataModule,
     MisconfigurationException,
 )
-from torchgeo.datasets import BigEarthNet, EuroSAT, RGBBandsMissingError
+from torchgeo.datasets import BigEarthNet, EuroSAT
 from torchgeo.main import main
 from torchgeo.models import ResNet18_Weights
 from torchgeo.trainers import ClassificationTask, MultiLabelClassificationTask
@@ -61,14 +61,6 @@ def load(url: str, *args: Any, **kwargs: Any) -> dict[str, Any]:
     return state_dict
 
 
-def plot(*args: Any, **kwargs: Any) -> None:
-    return None
-
-
-def plot_missing_bands(*args: Any, **kwargs: Any) -> None:
-    raise RGBBandsMissingError()
-
-
 class TestClassificationTask:
     @pytest.mark.parametrize(
         'name',
@@ -186,34 +178,6 @@ def test_invalid_loss(self) -> None:
         with pytest.raises(ValueError, match=match):
             ClassificationTask(model='resnet18', loss='invalid_loss')
 
-    def test_no_plot_method(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(EuroSATDataModule, 'plot', plot)
-        datamodule = EuroSATDataModule(
-            root='tests/data/eurosat', batch_size=1, num_workers=0
-        )
-        model = ClassificationTask(model='resnet18', in_channels=13, num_classes=10)
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
-    def test_no_rgb(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(EuroSATDataModule, 'plot', plot_missing_bands)
-        datamodule = EuroSATDataModule(
-            root='tests/data/eurosat', batch_size=1, num_workers=0
-        )
-        model = ClassificationTask(model='resnet18', in_channels=13, num_classes=10)
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
     def test_predict(self, fast_dev_run: bool) -> None:
         datamodule = PredictClassificationDataModule(
             root='tests/data/eurosat', batch_size=1, num_workers=0
@@ -277,38 +241,6 @@ def test_invalid_loss(self) -> None:
         with pytest.raises(ValueError, match=match):
             MultiLabelClassificationTask(model='resnet18', loss='invalid_loss')
 
-    def test_no_plot_method(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(BigEarthNetDataModule, 'plot', plot)
-        datamodule = BigEarthNetDataModule(
-            root='tests/data/bigearthnet', batch_size=1, num_workers=0
-        )
-        model = MultiLabelClassificationTask(
-            model='resnet18', in_channels=14, num_classes=19, loss='bce'
-        )
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
-    def test_no_rgb(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(BigEarthNetDataModule, 'plot', plot_missing_bands)
-        datamodule = BigEarthNetDataModule(
-            root='tests/data/bigearthnet', batch_size=1, num_workers=0
-        )
-        model = MultiLabelClassificationTask(
-            model='resnet18', in_channels=14, num_classes=19, loss='bce'
-        )
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
     def test_predict(self, fast_dev_run: bool) -> None:
         datamodule = PredictMultiLabelClassificationDataModule(
             root='tests/data/bigearthnet', batch_size=1, num_workers=0

tests/trainers/test_detection.py

@@ -13,7 +13,7 @@
 from torch.nn.modules import Module
 
 from torchgeo.datamodules import MisconfigurationException, NASAMarineDebrisDataModule
-from torchgeo.datasets import NASAMarineDebris, RGBBandsMissingError
+from torchgeo.datasets import NASAMarineDebris
 from torchgeo.main import main
 from torchgeo.trainers import ObjectDetectionTask
 
@@ -26,10 +26,6 @@ def setup(self, stage: str) -> None:
         self.predict_dataset = NASAMarineDebris(**self.kwargs)
 
 
-def plot_missing_bands(*args: Any, **kwargs: Any) -> None:
-    raise RGBBandsMissingError()
-
-
 class ObjectDetectionTestModel(Module):
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__()
@@ -62,10 +58,6 @@ def forward(self, images: Any, targets: Any = None) -> Any:
             return output
 
 
-def plot(*args: Any, **kwargs: Any) -> None:
-    return None
-
-
 class TestObjectDetectionTask:
     @pytest.mark.parametrize('name', ['nasa_marine_debris', 'vhr10'])
     @pytest.mark.parametrize('model_name', ['faster-rcnn', 'fcos', 'retinanet'])
@@ -120,34 +112,6 @@ def test_invalid_backbone(self) -> None:
     def test_pretrained_backbone(self) -> None:
         ObjectDetectionTask(backbone='resnet18', weights=True)
 
-    def test_no_plot_method(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(NASAMarineDebrisDataModule, 'plot', plot)
-        datamodule = NASAMarineDebrisDataModule(
-            root='tests/data/nasa_marine_debris', batch_size=1, num_workers=0
-        )
-        model = ObjectDetectionTask(backbone='resnet18', num_classes=2)
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
-    def test_no_rgb(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(NASAMarineDebrisDataModule, 'plot', plot_missing_bands)
-        datamodule = NASAMarineDebrisDataModule(
-            root='tests/data/nasa_marine_debris', batch_size=1, num_workers=0
-        )
-        model = ObjectDetectionTask(backbone='resnet18', num_classes=2)
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
     def test_predict(self, fast_dev_run: bool) -> None:
         datamodule = PredictObjectDetectionDataModule(
             root='tests/data/nasa_marine_debris', batch_size=1, num_workers=0

tests/trainers/test_regression.py

@@ -17,7 +17,7 @@
 from torchvision.models._api import WeightsEnum
 
 from torchgeo.datamodules import MisconfigurationException, TropicalCycloneDataModule
-from torchgeo.datasets import RGBBandsMissingError, TropicalCyclone
+from torchgeo.datasets import TropicalCyclone
 from torchgeo.main import main
 from torchgeo.models import ResNet18_Weights
 from torchgeo.trainers import PixelwiseRegressionTask, RegressionTask
@@ -51,14 +51,6 @@ def load(url: str, *args: Any, **kwargs: Any) -> dict[str, Any]:
     return state_dict
 
 
-def plot(*args: Any, **kwargs: Any) -> None:
-    return None
-
-
-def plot_missing_bands(*args: Any, **kwargs: Any) -> None:
-    raise RGBBandsMissingError()
-
-
 class TestRegressionTask:
     @classmethod
     def create_model(*args: Any, **kwargs: Any) -> Module:
@@ -156,34 +148,6 @@ def test_weight_str_download(self, weights: WeightsEnum) -> None:
             in_channels=weights.meta['in_chans'],
         )
 
-    def test_no_plot_method(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(TropicalCycloneDataModule, 'plot', plot)
-        datamodule = TropicalCycloneDataModule(
-            root='tests/data/cyclone', batch_size=1, num_workers=0
-        )
-        model = RegressionTask(model='resnet18')
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
-    def test_no_rgb(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
-        monkeypatch.setattr(TropicalCycloneDataModule, 'plot', plot_missing_bands)
-        datamodule = TropicalCycloneDataModule(
-            root='tests/data/cyclone', batch_size=1, num_workers=0
-        )
-        model = RegressionTask(model='resnet18')
-        trainer = Trainer(
-            accelerator='cpu',
-            fast_dev_run=fast_dev_run,
-            log_every_n_steps=1,
-            max_epochs=1,
-        )
-        trainer.validate(model=model, datamodule=datamodule)
-
     def test_predict(self, fast_dev_run: bool) -> None:
         datamodule = PredictRegressionDataModule(
             root='tests/data/cyclone', batch_size=1, num_workers=0