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test_segmentation.py
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test_segmentation.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
from pathlib import Path
from typing import Any, cast
import pytest
import segmentation_models_pytorch as smp
import timm
import torch
import torch.nn as nn
import torchvision
from lightning.pytorch import Trainer
from pytest import MonkeyPatch
from torch.nn.modules import Module
from torchvision.models._api import WeightsEnum
from torchgeo.datamodules import MisconfigurationException, SEN12MSDataModule
from torchgeo.datasets import LandCoverAI, RGBBandsMissingError
from torchgeo.main import main
from torchgeo.models import ResNet18_Weights
from torchgeo.trainers import SemanticSegmentationTask
class SegmentationTestModel(Module):
def __init__(self, in_channels: int = 3, classes: int = 3, **kwargs: Any) -> None:
super().__init__()
self.conv1 = nn.Conv2d(
in_channels=in_channels, out_channels=classes, kernel_size=1, padding=0
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return cast(torch.Tensor, self.conv1(x))
def create_model(**kwargs: Any) -> Module:
return SegmentationTestModel(**kwargs)
def load(url: str, *args: Any, **kwargs: Any) -> dict[str, Any]:
state_dict: dict[str, Any] = torch.load(url)
return state_dict
def plot(*args: Any, **kwargs: Any) -> None:
return None
def plot_missing_bands(*args: Any, **kwargs: Any) -> None:
raise RGBBandsMissingError()
class TestSemanticSegmentationTask:
@pytest.mark.parametrize(
"name",
[
"agrifieldnet",
"chabud",
"chesapeake_cvpr_5",
"chesapeake_cvpr_7",
"deepglobelandcover",
"etci2021",
"gid15",
"inria",
"l7irish",
"l8biome",
"landcoverai",
"loveda",
"naipchesapeake",
"potsdam2d",
"sen12ms_all",
"sen12ms_s1",
"sen12ms_s2_all",
"sen12ms_s2_reduced",
"sentinel2_cdl",
"sentinel2_nccm",
"sentinel2_south_america_soybean",
"spacenet1",
"ssl4eo_l_benchmark_cdl",
"ssl4eo_l_benchmark_nlcd",
"vaihingen2d",
],
)
def test_trainer(
self, monkeypatch: MonkeyPatch, name: str, fast_dev_run: bool
) -> None:
if name == "naipchesapeake":
pytest.importorskip("zipfile_deflate64")
if name == "landcoverai":
sha256 = "ecec8e871faf1bbd8ca525ca95ddc1c1f5213f40afb94599884bd85f990ebd6b"
monkeypatch.setattr(LandCoverAI, "sha256", sha256)
config = os.path.join("tests", "conf", name + ".yaml")
monkeypatch.setattr(smp, "Unet", create_model)
monkeypatch.setattr(smp, "DeepLabV3Plus", create_model)
args = [
"--config",
config,
"--trainer.accelerator",
"cpu",
"--trainer.fast_dev_run",
str(fast_dev_run),
"--trainer.max_epochs",
"1",
"--trainer.log_every_n_steps",
"1",
]
main(["fit"] + args)
try:
main(["test"] + args)
except MisconfigurationException:
pass
try:
main(["predict"] + args)
except MisconfigurationException:
pass
@pytest.fixture
def weights(self) -> WeightsEnum:
return ResNet18_Weights.SENTINEL2_ALL_MOCO
@pytest.fixture
def mocked_weights(
self, tmp_path: Path, monkeypatch: MonkeyPatch, weights: WeightsEnum
) -> WeightsEnum:
path = tmp_path / f"{weights}.pth"
model = timm.create_model(
weights.meta["model"], in_chans=weights.meta["in_chans"]
)
torch.save(model.state_dict(), path)
try:
monkeypatch.setattr(weights.value, "url", str(path))
except AttributeError:
monkeypatch.setattr(weights, "url", str(path))
monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
return weights
def test_weight_file(self, checkpoint: str) -> None:
SemanticSegmentationTask(backbone="resnet18", weights=checkpoint, num_classes=6)
def test_weight_enum(self, mocked_weights: WeightsEnum) -> None:
SemanticSegmentationTask(
backbone=mocked_weights.meta["model"],
weights=mocked_weights,
in_channels=mocked_weights.meta["in_chans"],
)
def test_weight_str(self, mocked_weights: WeightsEnum) -> None:
SemanticSegmentationTask(
backbone=mocked_weights.meta["model"],
weights=str(mocked_weights),
in_channels=mocked_weights.meta["in_chans"],
)
@pytest.mark.slow
def test_weight_enum_download(self, weights: WeightsEnum) -> None:
SemanticSegmentationTask(
backbone=weights.meta["model"],
weights=weights,
in_channels=weights.meta["in_chans"],
)
@pytest.mark.slow
def test_weight_str_download(self, weights: WeightsEnum) -> None:
SemanticSegmentationTask(
backbone=weights.meta["model"],
weights=str(weights),
in_channels=weights.meta["in_chans"],
)
def test_invalid_model(self) -> None:
match = "Model type 'invalid_model' is not valid."
with pytest.raises(ValueError, match=match):
SemanticSegmentationTask(model="invalid_model")
def test_invalid_loss(self) -> None:
match = "Loss type 'invalid_loss' is not valid."
with pytest.raises(ValueError, match=match):
SemanticSegmentationTask(loss="invalid_loss")
def test_no_plot_method(self, monkeypatch: MonkeyPatch, fast_dev_run: bool) -> None:
monkeypatch.setattr(SEN12MSDataModule, "plot", plot)
datamodule = SEN12MSDataModule(
root="tests/data/sen12ms", batch_size=1, num_workers=0
)
model = SemanticSegmentationTask(
backbone="resnet18", in_channels=15, num_classes=6
)
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(SEN12MSDataModule, "plot", plot_missing_bands)
datamodule = SEN12MSDataModule(
root="tests/data/sen12ms", batch_size=1, num_workers=0
)
model = SemanticSegmentationTask(
backbone="resnet18", in_channels=15, num_classes=6
)
trainer = Trainer(
accelerator="cpu",
fast_dev_run=fast_dev_run,
log_every_n_steps=1,
max_epochs=1,
)
trainer.validate(model=model, datamodule=datamodule)
@pytest.mark.parametrize("model_name", ["unet", "deeplabv3+"])
@pytest.mark.parametrize(
"backbone", ["resnet18", "mobilenet_v2", "efficientnet-b0"]
)
def test_freeze_backbone(self, model_name: str, backbone: str) -> None:
model = SemanticSegmentationTask(
model=model_name, backbone=backbone, freeze_backbone=True
)
assert all(
[param.requires_grad is False for param in model.model.encoder.parameters()]
)
assert all([param.requires_grad for param in model.model.decoder.parameters()])
assert all(
[
param.requires_grad
for param in model.model.segmentation_head.parameters()
]
)
@pytest.mark.parametrize("model_name", ["unet", "deeplabv3+"])
def test_freeze_decoder(self, model_name: str) -> None:
model = SemanticSegmentationTask(model=model_name, freeze_decoder=True)
assert all(
[param.requires_grad is False for param in model.model.decoder.parameters()]
)
assert all([param.requires_grad for param in model.model.encoder.parameters()])
assert all(
[
param.requires_grad
for param in model.model.segmentation_head.parameters()
]
)