HQ-SAM logit equal test, following #331

pyproject.toml

@@ -144,7 +144,7 @@ exclude_also = [
 
 [tool.typos.default]
 extend-words = { adaptee = "adaptee" }
-extend-ignore-identifiers-re = ["NDArray*"]
+extend-ignore-identifiers-re = ["NDArray*", "interm"]
 
 [tool.pytest.ini_options]
 filterwarnings = [

tests/foundationals/segment_anything/test_hq_sam.py

@@ -23,7 +23,7 @@
     MaskDecoderTokensExtender,
     PredictionsPostProc,
 )
-from refiners.foundationals.segment_anything.model import SegmentAnythingH
+from refiners.foundationals.segment_anything.model import ImageEmbedding, SegmentAnythingH
 
 
 @pytest.fixture(scope="module")
@@ -248,8 +248,8 @@ def test_predictor(
     reference_low_res_mask_hq = torch.from_numpy(low_res_masks_np[0, ...]).to(dtype=torch.float32)  # type: ignore
     iou_predictions_np = torch.from_numpy(iou_predictions_np).to(dtype=torch.float32)  # type: ignore
 
-    # NOTE: Diff on logits is relatively high, but on the same scale / even lower than base SAM logits diff (6e-3)
-    # See https://github.com/finegrain-ai/refiners/blob/c6b5eb24a179d48e4542d94684a70c5ef3142ab1/tests/foundationals/segment_anything/test_sam.py#L426
+    # NOTE: Diff on logits is relatively high,
+    # see test_predictor_equal for a stricter version
     assert torch.allclose(
         reference_low_res_mask_hq,
         refiners_low_res_mask_hq,
@@ -265,6 +265,58 @@ def test_predictor(
     )
 
 
+@pytest.mark.parametrize("hq_mask_only", [True, False])
+def test_predictor_equal(
+    sam_h: SegmentAnythingH,
+    hq_adapter_weights: Path,
+    hq_mask_only: bool,
+    reference_sam_h_predictor: FacebookSAMPredictorHQ,
+    tennis: Image.Image,
+    one_prompt: SAMPrompt,
+) -> None:
+    adapter = HQSAMAdapter(sam_h, weights=load_from_safetensors(hq_adapter_weights)).inject()
+
+    adapter.hq_mask_only = hq_mask_only
+    assert sam_h.ensure_find(PredictionsPostProc).hq_mask_only == hq_mask_only
+
+    # See in test_sam.py test_predictor_resized_single_output
+    # to do torch.equal we need to resize the image before
+    # and to use image_embedding as input
+
+    size = (1024, 1024)
+    resized_tennis = tennis.resize(size)
+
+    # Reference
+    reference_sam_h_predictor.set_image(np.array(resized_tennis))
+
+    predictor_prompt = one_prompt.__dict__["box_points"]
+    masks_np, _, low_res_masks_np = reference_sam_h_predictor.predict(
+        box=np.array(predictor_prompt).flatten(),
+        multimask_output=False,
+        hq_token_only=hq_mask_only,
+    )
+
+    reference_high_res_mask_hq = torch.from_numpy(masks_np[0, ...]).to(dtype=torch.float32)  # type: ignore
+    reference_low_res_mask_hq = torch.from_numpy(low_res_masks_np[0, ...]).to(dtype=torch.float32)  # type: ignore
+
+    # Refiners
+
+    # We bypass the refiners ViT by using directly the image features and interm_features
+    # from the reference implementation: this gives the ability to do torch.equal
+    reference_image_embedding = ImageEmbedding(features=reference_sam_h_predictor.features, original_image_size=size)
+    adapter.set_context("hq_sam", {"early_vit_embedding": reference_sam_h_predictor.interm_features[0]})
+
+    high_res_masks, _, low_res_masks = sam_h.predict(reference_image_embedding, **one_prompt.__dict__)
+    refiners_high_res_mask_hq = high_res_masks[0, 0, ...].to(dtype=torch.float32).detach().cpu()
+    refiners_low_res_mask_hq = low_res_masks[0, 0, ...].to(dtype=torch.float32).detach().cpu()
+
+    assert torch.equal(
+        reference_low_res_mask_hq,
+        refiners_low_res_mask_hq,
+    )
+    assert torch.abs(reference_high_res_mask_hq - refiners_high_res_mask_hq).flatten().sum() == 0
+
+
 @no_grad()
 def test_batch_mask_decoder(sam_h: SegmentAnythingH, hq_adapter_weights: Path) -> None:
     HQSAMAdapter(sam_h, weights=load_from_safetensors(hq_adapter_weights)).inject()

tests/foundationals/segment_anything/utils.py

@@ -57,6 +57,8 @@ def predict(
 
 class FacebookSAMPredictorHQ:
     model: FacebookSAM
+    features: Tensor
+    interm_features: Tensor
 
     def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None: ...