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SAM e2e test tolerance explained
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@ -21,7 +21,7 @@ from refiners.fluxion import manual_seed
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from refiners.fluxion.model_converter import ModelConverter
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from refiners.fluxion.utils import image_to_tensor, load_tensors, no_grad
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from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention, RelativePositionAttention
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from refiners.foundationals.segment_anything.model import SegmentAnythingH
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from refiners.foundationals.segment_anything.model import ImageEmbedding, SegmentAnythingH
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from refiners.foundationals.segment_anything.transformer import TwoWayTransformerLayer
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# See predictor_example.ipynb official notebook
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@ -409,7 +409,7 @@ def test_predictor_single_output(
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assert torch.allclose(
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low_res_masks[0, 0, ...],
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torch.as_tensor(facebook_low_res_masks[0], device=sam_h_single_output.device),
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atol=6e-3, # TODO: This diff on logits is high, and requires deeper investigation
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atol=6e-3, # see test_predictor_resized_single_output for more explanation
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)
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assert isclose(scores[0].item(), facebook_scores[0].item(), abs_tol=1e-05)
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@ -418,6 +418,43 @@ def test_predictor_single_output(
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assert isclose(intersection_over_union(mask_prediction, facebook_mask), 1.0, rel_tol=5e-05)
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def test_predictor_resized_single_output(
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facebook_sam_h_predictor: FacebookSAMPredictor,
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sam_h_single_output: SegmentAnythingH,
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truck: Image.Image,
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one_prompt: SAMPrompt,
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) -> None:
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# The refiners implementation of SAM differs from official
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# implementation by a 6e-3 absolute diff (see test_predictor_single_output)
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# This diff is related to 2 components :
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# * image_encoder (see test_image_encoder)
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# * point rescaling (facebook uses numpy while refiners uses torch)
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#
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# Current test is designed to workaround those 2 components
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# * facebook image_embedding is used
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# * the image is pre-resized by (1024, 1024) so there is no rescaling
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# Then the test pass with torch.equal
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predictor = facebook_sam_h_predictor
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size = (1024, 1024)
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resized_truck = truck.resize(size)
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predictor.set_image(np.array(resized_truck))
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_, _, facebook_low_res_masks = predictor.predict( # type: ignore
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**one_prompt.facebook_predict_kwargs(), # type: ignore
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multimask_output=False,
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)
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facebook_image_embedding = ImageEmbedding(features=predictor.features, original_image_size=size)
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_, _, low_res_masks = sam_h_single_output.predict(facebook_image_embedding, **one_prompt.__dict__)
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assert torch.equal(
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low_res_masks[0, 0, ...],
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torch.as_tensor(facebook_low_res_masks[0], device=sam_h_single_output.device),
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)
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def test_mask_encoder(
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facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt
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) -> None:
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@ -40,6 +40,7 @@ class FacebookSAM(nn.Module):
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class FacebookSAMPredictor:
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model: FacebookSAM
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features: Tensor
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def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None: ...
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