skps

tests/examples/test_scripts.py

@@ -79,7 +79,7 @@
             marks=[
                 pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed"),
                 pytest.mark.skipif(not _ICEDATA_AVAILABLE, reason="icedata package isn't installed"),
-                pytest.xfail()  # ToDo
+                pytest.xfail(),  # ToDo
             ],
         ),
         pytest.param(

tests/image/semantic_segm/test_data.py

@@ -12,6 +12,7 @@
     _FIFTYONE_AVAILABLE,
     _MATPLOTLIB_AVAILABLE,
     _PIL_AVAILABLE,
+    _SEGMENTATION_MODELS_AVAILABLE,
     _TOPIC_IMAGE_AVAILABLE,
 )
 from flash.image import SemanticSegmentation, SemanticSegmentationData
@@ -375,6 +376,7 @@ def test_from_fiftyone(tmpdir):
 
     @staticmethod
     @pytest.mark.skipif(not _MATPLOTLIB_AVAILABLE, reason="matplotlib isn't installed.")
+    @pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
     def test_map_labels(tmpdir):
         tmp_dir = Path(tmpdir)
 

tests/image/semantic_segm/test_heads.py

@@ -16,7 +16,7 @@
 import pytest
 import torch
 
-from flash.core.utilities.imports import _SEGMENTATION_MODELS_AVAILABLE, _TOPIC_IMAGE_AVAILABLE
+from flash.core.utilities.imports import _SEGMENTATION_MODELS_AVAILABLE
 from flash.image.segmentation import SemanticSegmentation
 from flash.image.segmentation.backbones import SEMANTIC_SEGMENTATION_BACKBONES
 from flash.image.segmentation.heads import SEMANTIC_SEGMENTATION_HEADS
@@ -37,8 +37,7 @@ def test_semantic_segmentation_heads_registry(head):
     assert res.shape[1] == 10
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
-@unittest.mock.patch("flash.image.segmentation.heads.smp")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_pretrained_weights(mock_smp):
     mock_smp.create_model = unittest.mock.MagicMock()
     available_weights = SemanticSegmentation.available_pretrained_weights("resnet18")

tests/image/semantic_segm/test_model.py

@@ -21,12 +21,13 @@
 
 from flash import Trainer
 from flash.core.data.io.input import DataKeys
-from flash.core.utilities.imports import _TOPIC_IMAGE_AVAILABLE, _TOPIC_SERVE_AVAILABLE
+from flash.core.utilities.imports import _SEGMENTATION_MODELS_AVAILABLE, _TOPIC_IMAGE_AVAILABLE
 from flash.image import SemanticSegmentation
 from flash.image.segmentation.data import SemanticSegmentationData
 from tests.helpers.task_tester import TaskTester
 
 
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 class TestSemanticSegmentation(TaskTester):
     task = SemanticSegmentation
     task_args = (2,)
@@ -59,29 +60,29 @@ def example_test_sample(self):
         return self.example_train_sample
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_non_existent_backbone():
     with pytest.raises(KeyError):
         SemanticSegmentation(2, "i am never going to implement this lol")
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_freeze():
     model = SemanticSegmentation(2)
     model.freeze()
     for p in model.backbone.parameters():
         assert p.requires_grad is False
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_unfreeze():
     model = SemanticSegmentation(2)
     model.unfreeze()
     for p in model.backbone.parameters():
         assert p.requires_grad is True
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_predict_tensor():
     img = torch.rand(1, 3, 64, 64)
     model = SemanticSegmentation(2, backbone="mobilenetv3_large_100")
@@ -93,7 +94,7 @@ def test_predict_tensor():
     assert len(out[0][0][0]) == 64
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_predict_numpy():
     img = np.ones((1, 3, 64, 64))
     model = SemanticSegmentation(2, backbone="mobilenetv3_large_100")
@@ -105,14 +106,14 @@ def test_predict_numpy():
     assert len(out[0][0][0]) == 64
 
 
-@pytest.mark.skipif(not _TOPIC_SERVE_AVAILABLE, reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 @mock.patch("flash._IS_TESTING", True)
 def test_serve():
     model = SemanticSegmentation(2)
     model.eval()
     model.serve()
 
 
-@pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 def test_available_pretrained_weights():
     assert SemanticSegmentation.available_pretrained_weights("resnet18") == ["imagenet", "ssl", "swsl"]

tests/image/semantic_segm/test_output.py

@@ -15,11 +15,12 @@
 import torch
 
 from flash.core.data.io.input import DataKeys
-from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _TOPIC_IMAGE_AVAILABLE
+from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _SEGMENTATION_MODELS_AVAILABLE, _TOPIC_IMAGE_AVAILABLE
 from flash.image.segmentation.output import FiftyOneSegmentationLabelsOutput, SegmentationLabelsOutput
 
 
 @pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, "image libraries aren't installed.")
+@pytest.mark.skipif(not _SEGMENTATION_MODELS_AVAILABLE, reason="No SMP")
 class TestSemanticSegmentationLabelsOutput:
     @staticmethod
     def test_smoke():