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loosen the tolerances for some sam tests (because of a recent pytorch upgrade)
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@ -256,8 +256,8 @@ def test_predictor(
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atol=4e-3,
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atol=4e-3,
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)
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)
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assert (
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assert (
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torch.abs(reference_high_res_mask_hq - refiners_high_res_mask_hq).flatten().sum() <= 1
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torch.abs(reference_high_res_mask_hq - refiners_high_res_mask_hq).flatten().sum() <= 2
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) # The diff on the logits above leads to an absolute diff of 1 pixel on the high res masks
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) # The diff on the logits above leads to an absolute diff of 2 pixel on the high res masks
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assert torch.allclose(
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assert torch.allclose(
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iou_predictions_np,
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iou_predictions_np,
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torch.max(iou_predictions),
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torch.max(iou_predictions),
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@ -422,7 +422,7 @@ def test_predictor_single_output(
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assert torch.allclose(
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assert torch.allclose(
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low_res_masks[0, 0, ...],
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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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torch.as_tensor(facebook_low_res_masks[0], device=sam_h_single_output.device),
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atol=6e-3, # see test_predictor_resized_single_output for more explanation
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atol=5e-2, # see test_predictor_resized_single_output for more explanation
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)
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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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assert isclose(scores[0].item(), facebook_scores[0].item(), abs_tol=1e-05)
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@ -497,7 +497,7 @@ def test_mask_encoder(
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dense_embeddings = sam_h.mask_encoder(mask_input)
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dense_embeddings = sam_h.mask_encoder(mask_input)
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assert facebook_mask_input.shape == mask_input.shape
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assert facebook_mask_input.shape == mask_input.shape
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assert torch.allclose(dense_embeddings, fb_dense_embeddings, atol=1e-4, rtol=1e-4)
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assert torch.allclose(dense_embeddings, fb_dense_embeddings, atol=1e-3)
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@no_grad()
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@no_grad()
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