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Fix/model factory #13

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merged 3 commits into from
May 27, 2024
Merged

Fix/model factory #13

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1e6e1c8
fix bad default for in_channels
CarlosGomes98 May 27, 2024
6430c3c
add test for default in_channels
CarlosGomes98 May 27, 2024
46e7b9a
edit test to use less resources for github actions
CarlosGomes98 May 27, 2024
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2 changes: 1 addition & 1 deletion src/terratorch/models/prithvi_model_factory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ def build_model(
backbone: str | nn.Module,
decoder: str | nn.Module,
bands: list[HLSBands | int],
in_channels: int = int | None, # this should be removed, can be derived from bands. But it is a breaking change
in_channels: int | None = None, # this should be removed, can be derived from bands. But it is a breaking change
num_classes: int | None = None,
pretrained: bool = True, # noqa: FBT001, FBT002
num_frames: int = 1,
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72 changes: 67 additions & 5 deletions tests/test_prithvi_model_factory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
NUM_CLASSES = 2
EXPECTED_SEGMENTATION_OUTPUT_SHAPE = (1, NUM_CLASSES, 224, 224)
EXPECTED_REGRESSION_OUTPUT_SHAPE = (1, 224, 224)
EXPECTED_CLASSIFICATION_OUTPUT_SHAPE = (1, NUM_CLASSES)


@pytest.fixture(scope="session")
Expand All @@ -25,6 +26,37 @@ def model_input() -> torch.Tensor:
return torch.ones((1, NUM_CHANNELS, 224, 224))

@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
def test_create_classification_model(backbone, model_factory: PrithviModelFactory, model_input):
model = model_factory.build_model(
"classification",
backbone=backbone,
decoder="IdentityDecoder",
in_channels=NUM_CHANNELS,
bands=PRETRAINED_BANDS,
pretrained=False,
num_classes=NUM_CLASSES,
)
model.eval()

with torch.no_grad():
assert model(model_input).output.shape == EXPECTED_CLASSIFICATION_OUTPUT_SHAPE

@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
def test_create_classification_model_no_in_channels(backbone, model_factory: PrithviModelFactory, model_input):
model = model_factory.build_model(
"classification",
backbone=backbone,
decoder="IdentityDecoder",
bands=PRETRAINED_BANDS,
pretrained=False,
num_classes=NUM_CLASSES,
)
model.eval()

with torch.no_grad():
assert model(model_input).output.shape == EXPECTED_CLASSIFICATION_OUTPUT_SHAPE

@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_segmentation_model(backbone, decoder, model_factory: PrithviModelFactory, model_input):
model = model_factory.build_model(
Expand All @@ -41,8 +73,24 @@ def test_create_segmentation_model(backbone, decoder, model_factory: PrithviMode
with torch.no_grad():
assert model(model_input).output.shape == EXPECTED_SEGMENTATION_OUTPUT_SHAPE

@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_segmentation_model_no_in_channels(backbone, decoder, model_factory: PrithviModelFactory, model_input):
model = model_factory.build_model(
"segmentation",
backbone=backbone,
decoder=decoder,
bands=PRETRAINED_BANDS,
pretrained=False,
num_classes=NUM_CLASSES,
)
model.eval()

@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
with torch.no_grad():
assert model(model_input).output.shape == EXPECTED_SEGMENTATION_OUTPUT_SHAPE


@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_segmentation_model_with_aux_heads(backbone, decoder, model_factory: PrithviModelFactory, model_input):
aux_heads_name = ["first_aux", "second_aux"]
Expand All @@ -67,7 +115,7 @@ def test_create_segmentation_model_with_aux_heads(backbone, decoder, model_facto
assert output.shape == EXPECTED_SEGMENTATION_OUTPUT_SHAPE


@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_regression_model(backbone, decoder, model_factory: PrithviModelFactory, model_input):
model = model_factory.build_model(
Expand All @@ -83,8 +131,22 @@ def test_create_regression_model(backbone, decoder, model_factory: PrithviModelF
with torch.no_grad():
assert model(model_input).output.shape == EXPECTED_REGRESSION_OUTPUT_SHAPE

@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_regression_model_no_in_channels(backbone, decoder, model_factory: PrithviModelFactory, model_input):
model = model_factory.build_model(
"regression",
backbone=backbone,
decoder=decoder,
bands=PRETRAINED_BANDS,
pretrained=False,
)
model.eval()

with torch.no_grad():
assert model(model_input).output.shape == EXPECTED_REGRESSION_OUTPUT_SHAPE

@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_regression_model_with_aux_heads(backbone, decoder, model_factory: PrithviModelFactory, model_input):
aux_heads_name = ["first_aux", "second_aux"]
Expand All @@ -108,14 +170,14 @@ def test_create_regression_model_with_aux_heads(backbone, decoder, model_factory
assert output.shape == EXPECTED_REGRESSION_OUTPUT_SHAPE


@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
def test_create_model_with_extra_bands(backbone, decoder, model_factory: PrithviModelFactory):
model = model_factory.build_model(
"segmentation",
backbone=backbone,
decoder=decoder,
in_channels=NUM_CHANNELS,
in_channels=NUM_CHANNELS + 1,
bands=[*PRETRAINED_BANDS, 7], # add an extra band
pretrained=False,
num_classes=NUM_CLASSES,
Expand Down
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