SAM e2e test tolerance explained

tests/foundationals/segment_anything/test_sam.py

@@ -21,7 +21,7 @@
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.fluxion.utils import image_to_tensor, load_tensors, no_grad
 from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention, RelativePositionAttention
-from refiners.foundationals.segment_anything.model import SegmentAnythingH
+from refiners.foundationals.segment_anything.model import ImageEmbedding, SegmentAnythingH
 from refiners.foundationals.segment_anything.transformer import TwoWayTransformerLayer
 
 # See predictor_example.ipynb official notebook
@@ -409,7 +409,7 @@ def test_predictor_single_output(
     assert torch.allclose(
         low_res_masks[0, 0, ...],
         torch.as_tensor(facebook_low_res_masks[0], device=sam_h_single_output.device),
-        atol=6e-3,  # TODO: This diff on logits is high, and requires deeper investigation
+        atol=6e-3,  # see test_predictor_resized_single_output for more explanation
     )
     assert isclose(scores[0].item(), facebook_scores[0].item(), abs_tol=1e-05)
 
@@ -418,6 +418,43 @@ def test_predictor_single_output(
     assert isclose(intersection_over_union(mask_prediction, facebook_mask), 1.0, rel_tol=5e-05)
 
 
+def test_predictor_resized_single_output(
+    facebook_sam_h_predictor: FacebookSAMPredictor,
+    sam_h_single_output: SegmentAnythingH,
+    truck: Image.Image,
+    one_prompt: SAMPrompt,
+) -> None:
+    # The refiners implementation of SAM differs from official
+    # implementation by a 6e-3 absolute diff (see test_predictor_single_output)
+    # This diff is related to 2 components :
+    # * image_encoder (see test_image_encoder)
+    # * point rescaling (facebook uses numpy while refiners uses torch)
+    #
+    # Current test is designed to workaround those 2 components
+    # * facebook image_embedding is used
+    # * the image is pre-resized by (1024, 1024) so there is no rescaling
+    # Then the test pass with torch.equal
+
+    predictor = facebook_sam_h_predictor
+    size = (1024, 1024)
+    resized_truck = truck.resize(size)
+    predictor.set_image(np.array(resized_truck))
+
+    _, _, facebook_low_res_masks = predictor.predict(  # type: ignore
+        **one_prompt.facebook_predict_kwargs(),  # type: ignore
+        multimask_output=False,
+    )
+
+    facebook_image_embedding = ImageEmbedding(features=predictor.features, original_image_size=size)
+
+    _, _, low_res_masks = sam_h_single_output.predict(facebook_image_embedding, **one_prompt.__dict__)
+
+    assert torch.equal(
+        low_res_masks[0, 0, ...],
+        torch.as_tensor(facebook_low_res_masks[0], device=sam_h_single_output.device),
+    )
+
+
 def test_mask_encoder(
     facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt
 ) -> None:

tests/foundationals/segment_anything/utils.py

@@ -40,6 +40,7 @@ def device(self) -> Any: ...
 
 class FacebookSAMPredictor:
     model: FacebookSAM
+    features: Tensor
 
     def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None: ...